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Victor Chernozhukov

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Daron Acemoglu & Victor Chernozhukov & Iván Werning & Michael D. Whinston, 2021. "Optimal Targeted Lockdowns in a Multigroup SIR Model," American Economic Review: Insights, American Economic Association, vol. 3(4), pages 487-502, December.

    Mentioned in:

    1. Some Covid Links
      by Don Boudreaux in Cafe Hayek on 2021-12-04 12:32:56
  2. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2019. "Distributional conformal prediction," Papers 1909.07889, arXiv.org, revised Aug 2021.

    Mentioned in:

    1. Interval Prediction
      by Francis Diebold in No Hesitations on 2019-10-12 19:16:00

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.

    Mentioned in:

    1. > Econometrics > Big Data
  2. Alexandre Belloni & Victor Chernozhukov, 2011. "High Dimensional Sparse Econometric Models: An Introduction," Papers 1106.5242, arXiv.org, revised Sep 2011.

    Mentioned in:

    1. > Econometrics > Big Data

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2016. "Inference in High-Dimensional Panel Models With an Application to Gun Control," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 590-605, October.

    Mentioned in:

    1. Inference in High Dimensional Panel Models with an Application to Gun Control (J Business & Econ Statistics 2016) in ReplicationWiki ()
  2. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.

    Mentioned in:

    1. Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain (ECTA 2012) in ReplicationWiki ()

Working papers

  1. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen & Liyang Sun, 2025. "Policy Learning with Confidence," Papers 2502.10653, arXiv.org, revised Jan 2026.

    Cited by:

    1. Isaiah Andrews & Jiafeng Chen, 2025. "Certified Decisions," Papers 2502.17830, arXiv.org.
    2. Nan Liu & Yanbo Liu & Yuya Sasaki & Yuanyuan Wan, 2025. "Nonparametric Uniform Inference in Binary Classification and Policy Values," Working Papers tecipa-811, University of Toronto, Department of Economics.
    3. Sarah Moon, 2025. "Optimal Policy Choices Under Uncertainty," Papers 2503.03910, arXiv.org, revised Feb 2026.
    4. Hamsa Bastani & Osbert Bastani & Bryce McLaughlin, 2025. "Beating the Winner's Curse via Inference-Aware Policy Optimization," Papers 2510.18161, arXiv.org, revised Feb 2026.

  2. Achim Ahrens & Victor Chernozhukov & Christian Hansen & Damian Kozbur & Mark Schaffer & Thomas Wiemann, 2025. "An Introduction to Double/Debiased Machine Learning," Papers 2504.08324, arXiv.org, revised Feb 2026.

    Cited by:

    1. Francis J. DiTraglia & Laura Liu, 2025. "Bayesian Double Machine Learning for Causal Inference," Papers 2508.12688, arXiv.org.

  3. Victor Chernozhukov & Iv'an Fern'andez-Val & Jonas Meier & Aico van Vuuren & Francis Vella, 2024. "Conditional Rank-Rank Regression," Papers 2407.06387, arXiv.org, revised Oct 2025.

    Cited by:

    1. Chernozhukov, Victor & Fernández-Val, Iván & Meier, Jonas & van Vuuren, Aico & Vella, Francis, 2025. "Bivariate Distribution Regression; Theory, Estimation and an Application to Intergenerational Mobility," IZA Discussion Papers 18091, IZA Network @ LISER.

  4. Victor Chernozhukov & Iv'an Fern'andez-Val & Sukjin Han & Kaspar Wuthrich, 2024. "Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments," Papers 2403.05850, arXiv.org, revised Dec 2024.

    Cited by:

    1. Chen, Songnian & Liu, Nianqing & Zhang, Hanghui, 2025. "Distribution regression with censored selection," Journal of Econometrics, Elsevier, vol. 251(C).
    2. Onil Boussim, 2025. "Identifying treatment effects on categorical outcomes in IV models," Papers 2510.10946, arXiv.org, revised Nov 2025.
    3. Onil Boussim, 2025. "Correcting sample selection bias with categorical outcomes," Papers 2510.05551, arXiv.org, revised Nov 2025.
    4. Undral Byambadalai & Tomu Hirata & Tatsushi Oka & Shota Yasui, 2025. "Beyond the Average: Distributional Causal Inference under Imperfect Compliance," Papers 2509.15594, arXiv.org, revised Oct 2025.
    5. Shuyuan Chen & Peng Zhang & Yifan Cui, 2025. "Identification and Debiased Learning of Causal Effects with General Instrumental Variables," Papers 2510.20404, arXiv.org, revised Feb 2026.

  5. Philipp Bach & Oliver Schacht & Victor Chernozhukov & Sven Klaassen & Martin Spindler, 2024. "Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study," Papers 2402.04674, arXiv.org.

    Cited by:

    1. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    2. Ballinari, Daniele, 2024. "Calibrating doubly-robust estimators with unbalanced treatment assignment," Economics Letters, Elsevier, vol. 241(C).
    3. Shilong Xi & Xiaohui Wang & Kejun Lin, 2025. "The Impact of Carbon Emissions Trading Pilot Policies on High-Quality Agricultural Development: An Empirical Assessment Using Double Machine Learning," Sustainability, MDPI, vol. 17(5), pages 1-28, February.

  6. Sven Klaassen & Jan Teichert-Kluge & Philipp Bach & Victor Chernozhukov & Martin Spindler & Suhas Vijaykumar, 2024. "DoubleMLDeep: Estimation of Causal Effects with Multimodal Data," Papers 2402.01785, arXiv.org.

    Cited by:

    1. Philipp Bach & Victor Chernozhukov & Sven Klaassen & Martin Spindler & Jan Teichert-Kluge & Suhas Vijaykumar, 2024. "Adventures in Demand Analysis Using AI," Papers 2501.00382, arXiv.org, revised Feb 2026.

  7. Victor Chernozhukov & Christian Hansen & Nathan Kallus & Martin Spindler & Vasilis Syrgkanis, 2024. "Applied Causal Inference Powered by ML and AI," Papers 2403.02467, arXiv.org.

    Cited by:

    1. Brunstein, Daniel & Casamatta, Georges & Giannoni, Sauveur, 2025. "Using machine learning to estimate the heterogeneous impact of Airbnb on house prices: Evidence from Corsica," Journal of Housing Economics, Elsevier, vol. 67(C).
    2. Marco Piccininni & Eric J. Tchetgen Tchetgen & Mats J. Stensrud, 2025. "Refining the Notion of No Anticipation in Difference-in-Differences Studies," Papers 2507.12891, arXiv.org, revised Sep 2025.
    3. Philipp Alexander Schwarz & Oliver Schacht & Sven Klaassen & Johannes Oberpriller & Martin Spindler, 2025. "Effect Identification and Unit Categorization in the Multi-Score Regression Discontinuity Design with Application to LED Manufacturing," Papers 2508.15692, arXiv.org, revised Oct 2025.
    4. Ravi B. Sojitra & Vasilis Syrgkanis, 2024. "Dynamic Local Average Treatment Effects," Papers 2405.01463, arXiv.org, revised May 2025.
    5. Martin Huber, 2024. "An introduction to causal discovery," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-16, December.
    6. Chris Hays & Manish Raghavan, 2025. "Double Machine Learning for Causal Inference under Shared-State Interference," Papers 2504.08836, arXiv.org.
    7. Hiroyuki Egami & Md. Shafiur Rahman & Tsuyoshi Yamamoto & Chihiro Egami & Takahisa Wakabayashi, 2024. "Causal effect of video gaming on mental well-being in Japan 2020–2022," Nature Human Behaviour, Nature, vol. 8(10), pages 1943-1956, October.
    8. Aur'elien Bibaut & Nathan Kallus, 2024. "Demistifying Inference after Adaptive Experiments," Papers 2405.01281, arXiv.org.
    9. Yang Cai & Alkis Kalavasis & Katerina Mamali & Anay Mehrotra & Manolis Zampetakis, 2025. "What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness," Papers 2506.04194, arXiv.org, revised Jun 2025.
    10. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
    11. Purificato Erasmo & Bili Danai & Jungnickel Robert & Ruiz Serra Victoria & Fabiani Josefina & Abendroth Dias Kulani & Fernandez Llorca David & Gomez Emilia, 2025. "The Role of Artificial Intelligence in Scientific Research," JRC Research Reports JRC143482, Joint Research Centre.
    12. Philipp Bach & Victor Chernozhukov & Carlos Cinelli & Lin Jia & Sven Klaassen & Nils Skotara & Martin Spindler, 2025. "Sensitivity Analysis for Causal ML: A Use Case at Booking.com," Papers 2510.09109, arXiv.org.

  8. Victor Chernozhukov & Han Hong, 2023. "An MCMC Approach to Classical Estimation," Papers 2301.07782, arXiv.org.

    Cited by:

    1. Chatterjee Pratiti, 2019. "Asymmetric impact of uncertainty in recessions: are emerging countries more vulnerable?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 23(2), pages 1-27, April.
    2. Roberta Serafini & J. Bruha & B. Pierluigi, 2011. "Euro area labour markets: different reaction to shocks?," EcoMod2011 2970, EcoMod.
    3. Lucia Del Carpio & Samuel Kapon & Sylvain Chassang, 2022. "Using Divide-and-Conquer to Improve Tax Collection: Evidence from the Field," Working Papers 301, Princeton University, Department of Economics, Center for Economic Policy Studies..
    4. Patrick Bajari & Jeremy T. Fox & Stephen Ryan, 2006. "Evaluating Wireless Carrier Consolidation Using Semiparametric Demand Estimation," NBER Working Papers 12425, National Bureau of Economic Research, Inc.
    5. Yuriy Gorodnichenko & Anna Mikusheva & Serena Ng, 2011. "Estimators for Persistent and Possibly Non-Stationary Data with Classical Properties," NBER Working Papers 17424, National Bureau of Economic Research, Inc.
    6. Mike G. Tsionas, 2023. "Linex and double-linex regression for parameter estimation and forecasting," Annals of Operations Research, Springer, vol. 323(1), pages 229-245, April.
    7. Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2011. "-Consistent robust integration-based estimation," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 828-846, April.
    8. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers CWP43/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Haroon Mumtaz & Paolo Surico, 2015. "The Transmission Mechanism In Good And Bad Times," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(4), pages 1237-1260, November.
    10. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
    11. Erik Figueiredo & Luiz Renato Lima & Gianluca Orefice, 2016. "Migration and Regional Trade Agreements: A (New) Gravity Estimation," Review of International Economics, Wiley Blackwell, vol. 24(1), pages 99-125, February.
    12. Greg Kaplan, 2007. "Inequality and the Lifecycle," 2007 Meeting Papers 262, Society for Economic Dynamics.
    13. Xiaohong Chen & Timothy M. Christensen & Keith O'Hara & Elie Tamer, 2016. "MCMC confidence sets for identified sets," CeMMAP working papers CWP28/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Ando, Tomohiro & Bai, Jushan & Li, Kunpeng, 2022. "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 230(1), pages 20-38.
    15. Gyuhyeong Goh & Jisang Yu, 2022. "Causal inference with some invalid instrumental variables: A quasi‐Bayesian approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1432-1451, December.
    16. Bournakis, Ioannis & Tsionas, Mike G., 2023. "A Non-Parametric Estimation of Productivity with Idiosyncratic and Aggregate Shocks: The Role of Research and Development (R&D) and Corporate Tax," MPRA Paper 118100, University Library of Munich, Germany.
    17. David T. Frazier & Gael M. Martin & Ruben Loaiza-Maya, 2022. "Variational Bayes in State Space Models: Inferential and Predictive Accuracy," Monash Econometrics and Business Statistics Working Papers 1/22, Monash University, Department of Econometrics and Business Statistics.
    18. Bonciani, Dario, 2014. "Uncertainty shocks: it's a matter of habit," MPRA Paper 59370, University Library of Munich, Germany.
    19. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    20. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
    21. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    23. Alessandro Casini & Pierre Perron, 2018. "Generalized Laplace Inference in Multiple Change-Points Models," Papers 1803.10871, arXiv.org, revised Jan 2021.
    24. Canova, Fabio & Matthes, Christian, 2018. "A composite likelihood approach for dynamic structural models," CEPR Discussion Papers 13245, C.E.P.R. Discussion Papers.
    25. Grigory Franguridi & Bulat Gafarov & Kaspar Wuthrich, 2020. "Bias correction for quantile regression estimators," Papers 2011.03073, arXiv.org, revised Feb 2025.
    26. Takuo Matsubara & Jeremias Knoblauch & François‐Xavier Briol & Chris J. Oates, 2022. "Robust generalised Bayesian inference for intractable likelihoods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 997-1022, July.
    27. Victor Chernozhukov & Christian B. Hansen & Lingwei Kong & Weining Wang, 2025. "Plausible GMM: A Quasi-Bayesian Approach," Bristol Economics Discussion Papers 25/817, School of Economics, University of Bristol, UK.
    28. Stephanie Schmitt‐Grohé & Martín Uribe, 2012. "What's News in Business Cycles," Econometrica, Econometric Society, vol. 80(6), pages 2733-2764, November.
    29. Komunjer, Ivana, 2005. "Quasi-maximum likelihood estimation for conditional quantiles," Journal of Econometrics, Elsevier, vol. 128(1), pages 137-164, September.
    30. Igari Ryosuke & Takahiro Hoshino, 2017. "Semiparametric Quasi-Bayesian Inference with Dirichlet Process Priors: Application to Nonignorable Missing Responses," Keio-IES Discussion Paper Series 2017-020, Institute for Economics Studies, Keio University.
    31. Jeremy Lise & Costas Meghir & Jean-Marc Robin, 2013. "Mismatch, sorting and wage dynamics," IFS Working Papers W13/16, Institute for Fiscal Studies.
    32. Popp, Aaron & Zhang, Fang, 2016. "The macroeconomic effects of uncertainty shocks: The role of the financial channel," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 319-349.
    33. Fusejima, Koki, 2024. "Identification of multi-valued treatment effects with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
    34. Ana M. Fernandes & Peter J. Klenow & Sergii Meleshchuk & Denisse Pierola & Andrés Rodríguez-Clare, 2018. "The Intensive Margin in Trade," NBER Working Papers 25195, National Bureau of Economic Research, Inc.
    35. Noriko Amano, 2018. "Nutrition Inequality: The Role of Prices, Income, and Preferences," 2018 Meeting Papers 453, Society for Economic Dynamics.
    36. Ryosuke Igari & Takahiro Hoshino, 2018. "A Bayesian Gamma Frailty Model Using the Sum of Independent Random Variables: Application of the Estimation of an Interpurchase Timing Model," Keio-IES Discussion Paper Series 2018-021, Institute for Economics Studies, Keio University.
    37. Ruge-Murcia, Francisco, 2012. "Estimating nonlinear DSGE models by the simulated method of moments: With an application to business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 36(6), pages 914-938.
    38. Manuel Arellano & Stéphane Bonhomme, 2007. "Robust priors in nonlinear panel data models," CeMMAP working papers CWP07/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    39. Chung, Ray S.W. & So, Mike K.P. & Chu, Amanda M.Y. & Chan, Thomas W.C., 2020. "Regularization of Bayesian quasi-likelihoods constructed from complex estimating functions," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    40. Sebastian Heise & Justin R. Pierce & Georg Schaur & Peter K. Schott, 2024. "Tariff Rate Uncertainty and the Structure of Supply Chains," International Finance Discussion Papers 1389, Board of Governors of the Federal Reserve System (U.S.).
    41. Giovanni Caggiano & Efrem Castelnuovo & Valentina Colombo & Gabriela Nodari, 2014. "Estimating Fiscal Multipliers: News from a Nonlinear World," Melbourne Institute Working Paper Series wp2014n26, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    42. Fotiou, Alexandra & Shen, Wenyi & Yang, Shu-Chun S., 2020. "The fiscal state-dependent effects of capital income tax cuts," Journal of Economic Dynamics and Control, Elsevier, vol. 117(C).
    43. Jeremy Lise & Costas Meghir & Jean-Marc Robin, 2016. "Matching, Sorting and Wages," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 19, pages 63-87, January.
    44. Jean-Jacques Forneron & Liang Zhong, 2023. "Convexity Not Required: Estimation of Smooth Moment Condition Models," Papers 2304.14386, arXiv.org, revised Jul 2025.
    45. David Powell, 2013. "A New Framework for Estimation of Quantile Treatment Effects Nonseparable Disturbance in the Presence of Covariates," Working Papers WR-824-1, RAND Corporation.
    46. Andrew Chia, 2021. "Automatically Differentiable Random Coefficient Logistic Demand Estimation," Papers 2106.04636, arXiv.org.
    47. Alhamzawi, Rahim & Yu, Keming, 2013. "Conjugate priors and variable selection for Bayesian quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 209-219.
    48. Ana M. Fernandes & Peter J. Klenow & Sergii Meleshchuk & Martha Denisse Pierola & Andrés Rodríguez- Clare, 2023. "The Intensive Margin in Trade: How Big and How Important?," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(3), pages 320-354, July.
    49. Jarkko Jääskelä & Kristoffer Nimark, 2008. "A Medium-scale Open Economy Model of Australia," RBA Research Discussion Papers rdp2008-07, Reserve Bank of Australia.
    50. Huigang Chen & Mr. Alin T Mirestean & Mr. Charalambos G Tsangarides, 2011. "Limited Information Bayesian Model Averaging for Dynamic Panels with An Application to a Trade Gravity Model," IMF Working Papers 2011/230, International Monetary Fund.
    51. Jiti Gao & Han Hong, 2014. "Nonparametric Regression Approach to Bayesian Estimation," Monash Econometrics and Business Statistics Working Papers 25/14, Monash University, Department of Econometrics and Business Statistics.
    52. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
    53. Otero, Karina V., 2016. "Intensity of default in sovereign bonds: Estimation of an unobservable process," MPRA Paper 86782, University Library of Munich, Germany.
    54. Ron Gallant & Raffaella Giacomini & Giuseppe Ragusa, 2013. "Generalized method of moments with latent variables," CeMMAP working papers CWP50/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    55. Simon Loertscher & Andras Niedermayer, 2012. "Assessing the Performance of Simple Contracts Empirically:The Case of Percentage Fees," Department of Economics - Working Papers Series 1163, The University of Melbourne.
    56. Arcos, Ariel & Lara, Bernardo & Paredes, Dusan, 2025. "Enhancing private transportation: The positive spillovers from Santiago’s subway expansion," Transport Policy, Elsevier, vol. 172(C).
    57. Sid Kankanala, 2025. "Generalized Bayes in Conditional Moment Restriction Models," Papers 2510.01036, arXiv.org.
    58. Maria Laura Battagliola & Helle Sørensen & Anders Tolver & Ana-Maria Staicu, 2025. "Quantile Regression for Longitudinal Functional Data with Application to Feed Intake of Lactating Sows," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 30(1), pages 211-230, March.
    59. Abby Alpert & David Powell, 2014. "Estimating Intensive and Extensive Tax Responsiveness Do Older Workers Respond to Income Taxes?," Working Papers WR-987-1, RAND Corporation.
    60. Nikolay Gospodinov & Esfandiar Massoumi, 2025. "On Model Aggregation and Forecast Combination," FRB Atlanta Working Paper 2025-12, Federal Reserve Bank of Atlanta.
    61. Tibor Szendrei, 2025. "Crossing penalised CAViaR," Papers 2501.10564, arXiv.org.
    62. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.
    63. Wenli Li & Costas Meghir & Florian Oswald, 2022. "Consumer Bankruptcy, Mortgage Default and Labor Supply," Working Papers 22-26, Federal Reserve Bank of Philadelphia.
    64. Jean-Pierre Florens & Anna Simoni, 2021. "Revisiting Identification Concepts in Bayesian Analysis," Post-Print hal-03504692, HAL.
    65. Manuel Arellano & Stéphane Bonhomme, 2016. "Quantile Selection Models with an Application to Understanding Changes in Wage Inequality," Working Papers wp2016_1610, CEMFI.
    66. Hlouskova, Jaroslava & Sögner, Leopold, 2015. "GMM Estimation of Affine Term Structure Models," Economics Series 315, Institute for Advanced Studies.
    67. Mi Luo & Simon Mongey, 2019. "Assets and Job Choice: Student Debt, Wages, and Job Satisfaction," 2019 Meeting Papers 1220, Society for Economic Dynamics.
    68. Leon Zolotoy & Don O’Sullivan & Geoffrey P. Martin & Robert M. Wiseman, 2021. "Stakeholder Agency Relationships: CEO Stock Options and Corporate Tax Avoidance," Journal of Management Studies, Wiley Blackwell, vol. 58(3), pages 782-814, May.
    69. Jean-Jacques Forneron, 2019. "Detecting Identification Failure in Moment Condition Models," Papers 1907.13093, arXiv.org, revised Oct 2023.
    70. David Kohns & Tibor Szendrei, 2025. "Joint Quantile Shrinkage: A State-Space Approach toward Non-Crossing Bayesian Quantile Models," Papers 2506.13257, arXiv.org, revised Aug 2025.
    71. Benjamin Friedrich & Lisa Laun & Costas Meghir & Luigi Pistaferri, 2021. "Earnings dynamics and firm-level shocks," IFS Working Papers W21/33, Institute for Fiscal Studies.
    72. In Kim & In-Seok Baek & Jaesun Noh & Sol Kim, 2007. "The role of stochastic volatility and return jumps: reproducing volatility and higher moments in the KOSPI 200 returns dynamics," Review of Quantitative Finance and Accounting, Springer, vol. 29(1), pages 69-110, July.
    73. Fabio Canova & Christian Matthes, 2021. "Dealing with misspecification in structural macroeconometric models," Quantitative Economics, Econometric Society, vol. 12(2), pages 313-350, May.
    74. Benoit, Dries F. & Van den Poel, Dirk, 2017. "bayesQR: A Bayesian Approach to Quantile Regression," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i07).
    75. Anna Kormilitsina, 2009. "Oil Price Shocks and the Optimality of Monetary Policy," Departmental Working Papers 0901, Southern Methodist University, Department of Economics.
    76. Florian Oswald, 2015. "Regional Shocks, Migration and Homeownership," 2015 Meeting Papers 759, Society for Economic Dynamics.
    77. Ertian Chen, 2025. "Model-Adaptive Approach to Dynamic Discrete Choice Models with Large State Spaces," Papers 2501.18746, arXiv.org, revised Jun 2025.
    78. Rubio-Ramírez, Juan Francisco & Schorfheide, Frank & Fernández-Villaverde, Jesús, 2015. "Solution and Estimation Methods for DSGE Models," CEPR Discussion Papers 11032, C.E.P.R. Discussion Papers.
    79. Haroon Mumtaz & Roman Sustek, 2023. "Global house prices since 1950," Discussion Papers 2307, Centre for Macroeconomics (CFM).
    80. Paul A. Borochin, 2014. "When Does a Merger Create Value? Using Option Prices to Elicit Market Beliefs," Financial Management, Financial Management Association International, vol. 43(2), pages 445-466, June.
    81. Victor Chernozhukov & Christian Hansen, 2013. "Quantile models with endogeneity," CeMMAP working papers CWP25/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    82. Giuseppe Ragusa, 2007. "Bayesian Likelihoods for Moment Condition Models," Working Papers 060714, University of California-Irvine, Department of Economics.
    83. Porzio, Tommaso & Heise, Sebastian, 2019. "Spatial Wage Gaps in Frictional Labor Markets," CEPR Discussion Papers 14197, C.E.P.R. Discussion Papers.
    84. Alexandre Belloni & Victor Chernozhukov, 2009. "Posterior Inference in Curved Exponential Families under Increasing Dimensions," Papers 0904.3132, arXiv.org, revised Apr 2014.
    85. George Monokroussos, 2006. "Dynamic Limited Dependent Variable Modeling and U.S. Monetary Policy," Discussion Papers 06-02, University at Albany, SUNY, Department of Economics.
    86. Chen Jau-er, 2015. "Factor instrumental variable quantile regression," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 71-92, February.
    87. David Kang & Seojeong Lee, 2025. "Misspecification-Robust Asymptotic and Bootstrap Inference for Nonsmooth GMM," Working Papers 423284005, Lancaster University Management School, Economics Department.
    88. Bassi, Vittorio & Muoio, Raffaela & Porzio, Tommaso & Sen, Ritwika & Tugume, Esau, 2020. "Achieving Scale Collectively," CEPR Discussion Papers 15134, C.E.P.R. Discussion Papers.
    89. Gregor Jarosch, 2014. "Falling off the Ladder - Earnings Losses from Job Loss," 2014 Meeting Papers 1248, Society for Economic Dynamics.
    90. Fernandes, Marcelo & Guerre, Emmanuel & Horta, Eduardo, 2017. "Smoothing quantile regressions," Textos para discussão 457, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    91. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    92. Olli Saarela & Elja Arjas, 2015. "Non-parametric Bayesian Hazard Regression for Chronic Disease Risk Assessment," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 609-626, June.
    93. Jesús Fernández-Villaverde, 2010. "The econometrics of DSGE models," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 1(1), pages 3-49, March.
    94. Goodhead, Robert, 2024. "The economic impact of yield curve compression: Evidence from euro area forward guidance and unconventional monetary policy," European Economic Review, Elsevier, vol. 164(C).
    95. Bruins, Marianne & Duffy, James A. & Keane, Michael P. & Smith, Anthony A., 2018. "Generalized indirect inference for discrete choice models," Journal of Econometrics, Elsevier, vol. 205(1), pages 177-203.
    96. Anna Kormilitsina & Denis Nekipelov, 2015. "Consistent Variance of the Laplace Type Estimators: Application to DSGE Models," Departmental Working Papers 1510, Southern Methodist University, Department of Economics.
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  9. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2023. "Distribution regression with sample selection and UK wage decomposition," CeMMAP working papers 09/23, Institute for Fiscal Studies.

    Cited by:

    1. Chen, Songnian & Liu, Nianqing & Zhang, Hanghui, 2025. "Distribution regression with censored selection," Journal of Econometrics, Elsevier, vol. 251(C).
    2. Onil Boussim, 2023. "Correcting Sample Selection Bias in PISA Rankings," Papers 2309.10642, arXiv.org, revised Oct 2025.

  10. Patrick Bajari & Zhihao Cen & Victor Chernozhukov & Manoj Manukonda & Suhas Vijaykumar & Jin Wang & Ramon Huerta & Junbo Li & Ling Leng & George Monokroussos & Shan Wang, 2023. "Hedonic Prices and Quality Adjusted Price Indices Powered by AI," Papers 2305.00044, arXiv.org, revised Jan 2026.

    Cited by:

    1. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2025. "Model Averaging and Double Machine Learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(3), pages 249-269, April.
    2. Radoslaw Trojanek & Luke Hartigan & Norbert Pfeifer & Miriam Steurer, 2025. "Nowcasting Transaction-Based House Price Indices Using Web-Scraped Listings and MIDAS Regression," Graz Economics Papers 2025-13, University of Graz, Department of Economics.
    3. Bajari, P. & Cen, Z. & Chernozhukov, V. & Manukonda, M. & Vijaykumar, S. & Wang, J. & Huerta, R. & Li, J. & Leng, L. & Monokroussos, G. & Wang, S., 2025. "Hedonic prices and quality adjusted price indices powered by AI," Journal of Econometrics, Elsevier, vol. 251(C).
    4. Philipp Bach & Victor Chernozhukov & Sven Klaassen & Martin Spindler & Jan Teichert-Kluge & Suhas Vijaykumar, 2024. "Adventures in Demand Analysis Using AI," Papers 2501.00382, arXiv.org, revised Feb 2026.

  11. Guillaume Carlier & Victor Chernozhukov & Gwendoline de Bie & Alfred Galichon, 2022. "Correction to: Vector Quantile Regression and Optimal Transport, from Theory to Numerics," Post-Print hal-03896159, HAL.

    Cited by:

    1. Bernd Fitzenberger & Roger Koenker & José Machado & Blaise Melly, 2022. "Economic applications of quantile regression 2.0," Empirical Economics, Springer, vol. 62(1), pages 1-6, January.
    2. Linjie Wang & Jean‐Paul Chavas & Jian Li, 2024. "Dynamic linkages in agricultural and energy markets: A quantile impulse response approach," Agricultural Economics, International Association of Agricultural Economists, vol. 55(4), pages 639-676, July.

  12. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022. "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers 2203.13887, arXiv.org, revised Jun 2023.

    Cited by:

    1. Victor Quintas-Martinez & Mohammad Taha Bahadori & Eduardo Santiago & Jeff Mu & Dominik Janzing & David Heckerman, 2024. "Multiply-Robust Causal Change Attribution," Papers 2404.08839, arXiv.org, revised Sep 2024.

  13. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.

    Cited by:

    1. Riccardi, L. & Compare, M. & Mascherona, R. & Zio, E., 2025. "Structural causal modeling and STPA for the risk analysis of a rail system powered by H2 fuel," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    2. Keyon Vafa & Susan Athey & David M. Blei, 2024. "Estimating Wage Disparities Using Foundation Models," Papers 2409.09894, arXiv.org, revised Apr 2025.
    3. Melody Huang & Cory McCartan, 2025. "Relative Bias Under Imperfect Identification in Observational Causal Inference," Papers 2507.23743, arXiv.org.
    4. Hünermund Paul & Louw Beyers & Caspi Itamar, 2023. "Double machine learning and automated confounder selection: A cautionary tale," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-12, January.
    5. Geonwoo Kim & Suyong Song, 2024. "Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables," Papers 2408.14671, arXiv.org.

  14. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2021. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees," Papers 2105.15197, arXiv.org, revised Oct 2022.

    Cited by:

    1. Dmitry Arkhangelsky & Kazuharu Yanagimoto & Tom Zohar, 2024. "On Causal Inference with Model-Based Outcomes," Papers 2403.19563, arXiv.org, revised Jan 2026.
    2. Dmitry Arkhangelsky & Kazuharu Yanagimoto & Tom Zohar, 2025. "Using Event Studies as an Outcome in Causal Analysis," Working Papers wp2025_2503, CEMFI.
    3. Fabian Muny, 2025. "Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies," Papers 2506.11960, arXiv.org.
    4. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Jun 2025.
    5. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Source Condition Double Robust Inference on Functionals of Inverse Problems," Papers 2307.13793, arXiv.org.
    6. Jeonghwan Lee & Cong Ma, 2025. "Learning bounds for doubly-robust covariate shift adaptation," Papers 2511.11003, arXiv.org.
    7. Rahul Singh & Liyuan Xu & Arthur Gretton, 2021. "Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves," Papers 2111.03950, arXiv.org, revised Mar 2025.
    8. Rahul Singh, 2021. "Generalized Kernel Ridge Regression for Causal Inference with Missing-at-Random Sample Selection," Papers 2111.05277, arXiv.org.
    9. David Bruns-Smith & Oliver Dukes & Avi Feller & Elizabeth L. Ogburn, 2023. "Augmented balancing weights as linear regression," Papers 2304.14545, arXiv.org, revised Jun 2024.
    10. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression," Papers 2112.14249, arXiv.org, revised May 2025.

  15. Guillaume Carlier & Victor Chernozhukov & Gwendoline De Bie & Alfred Galichon, 2021. "Vector quantile regression and optimal transport, from theory to numerics," Papers 2102.12809, arXiv.org.

    Cited by:

    1. Bernd Fitzenberger & Roger Koenker & José Machado & Blaise Melly, 2022. "Economic applications of quantile regression 2.0," Empirical Economics, Springer, vol. 62(1), pages 1-6, January.
    2. Linjie Wang & Jean‐Paul Chavas & Jian Li, 2024. "Dynamic linkages in agricultural and energy markets: A quantile impulse response approach," Agricultural Economics, International Association of Agricultural Economists, vol. 55(4), pages 639-676, July.

  16. Victor Chernozhukov & Hiroyuki Kasahara & Paul Schrimpf, 2021. "A Response to Philippe Lemoine's Critique on our Paper "Causal Impact of Masks, Policies, Behavior on Early Covid-19 Pandemic in the U.S."," Papers 2110.06136, arXiv.org.

    Cited by:

    1. Crudu, Federico & Di Stefano, Roberta & Mellace, Giovanni & Tiezzi, Silvia, 2024. "The gray zone: How not imposing a strict lockdown at the beginning of a pandemic can cost many lives," Labour Economics, Elsevier, vol. 89(C).

  17. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.

    Cited by:

    1. Jing Kong, 2025. "On the Asymptotics of the Minimax Linear Estimator," Papers 2510.16661, arXiv.org.
    2. Sven Klaassen & Jan Rabenseifner & Jannis Kueck & Philipp Bach, 2025. "Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators," Papers 2503.17290, arXiv.org, revised May 2025.
    3. Jing Kong, 2025. "Causal Inference in High-Dimensional Generalized Linear Models with Binary Outcomes," Papers 2510.16669, arXiv.org.
    4. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022. "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers 2203.13887, arXiv.org, revised Jun 2023.
    5. Masahiro Kato, 2025. "Direct Debiased Machine Learning via Bregman Divergence Minimization," Papers 2510.23534, arXiv.org, revised Oct 2025.
    6. Victor Chernozhukov & Whitney K. Newey & Vasilis Syrgkanis, 2024. "Conditional Influence Functions," Papers 2412.18080, arXiv.org.
    7. Amandeep Singh & Ye Liu & Hema Yoganarasimhan, 2023. "Choice Models and Permutation Invariance: Demand Estimation in Differentiated Products Markets," Papers 2307.07090, arXiv.org, revised Feb 2024.
    8. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    9. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
    10. Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2022. "Contamination Bias in Linear Regressions," Working Papers 2022-15, Princeton University. Economics Department..
    11. Victor Quintas-Martinez, 2022. "Finite-Sample Guarantees for High-Dimensional DML," Papers 2206.07386, arXiv.org.
    12. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests," Papers 2110.03031, arXiv.org, revised Jun 2022.

  18. Victor Chernozhukov & Christian Hansen & Yuan Liao & Yinchu Zhu, 2021. "Inference for Low-Rank Models," Papers 2107.02602, arXiv.org, revised Jan 2023.

    Cited by:

    1. Jungjun Choi & Ming Yuan, 2025. "Inferential Theory for Pricing Errors with Latent Factors and Firm Characteristics," Papers 2511.03076, arXiv.org.
    2. Choi, Jungjun & Kwon, Hyukjun & Liao, Yuan, 2024. "Inference for low-rank completion without sample splitting with application to treatment effect estimation," Journal of Econometrics, Elsevier, vol. 240(1).
    3. Su, Liangjun & Jin, Sainan & Wang, Xia, 2025. "Sieve estimation of state-varying factor models," Journal of Econometrics, Elsevier, vol. 251(C).
    4. Hugo Freeman & Martin Weidner, 2021. "Linear panel regressions with two-way unobserved heterogeneity," CeMMAP working papers CWP39/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Su, Liangjun & Wang, Fa, 2025. "Inference for large dimensional factor models under general missing data patterns," Journal of Econometrics, Elsevier, vol. 250(C).

  19. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python," Papers 2104.03220, arXiv.org, revised Dec 2021.

    Cited by:

    1. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    2. Heinisch, Katja & Scaramella, Fabio & Schult, Christoph, 2025. "Assumption errors and forecast accuracy: A partial linear instrumental variable and double machine learning approach," IWH Discussion Papers 6/2025, Halle Institute for Economic Research (IWH).
    3. Anna Baiardi & Andrea A. Naghi, 2024. "The effect of plough agriculture on gender roles: A machine learning approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1396-1402, November.
    4. Philipp Schwarz & Oliver Schacht & Sven Klaassen & Daniel Grunbaum & Sebastian Imhof & Martin Spindler, 2024. "Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?," Papers 2406.11308, arXiv.org.
    5. Chakravorty, Bhaskar & Arulampalam, Wiji & Bhatiya, Apurav Yash & Imbert, Clément & Rathelot, Roland, 2024. "Can information about jobs improve the effectiveness of vocational training? Experimental evidence from India," Journal of Development Economics, Elsevier, vol. 169(C).
    6. Gomez-Gonzalez, Jose E. & Uribe, Jorge M. & Valencia, Oscar M., 2025. "Sovereign debt cost and economic complexity," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 99(C).
    7. Tommaso Manfè & Luca Nunziata, 2023. "Difference-In-Difference Design With Repeated Cross-Sections Under Compositional Changes: a Monte-Carlo Evaluation of Alternative Approaches," "Marco Fanno" Working Papers 0305, Dipartimento di Scienze Economiche "Marco Fanno".
    8. Fang, Yan & Liu, Yinglin & Yang, Yi & Lucey, Brian & Abedin, Mohammad Zoynul, 2025. "How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning," Research in International Business and Finance, Elsevier, vol. 74(C).

  20. Victor Chernozhukov & Hiroyuki Kasahara & Paul Schrimpf, 2021. "The Association of Opening K-12 Schools with the Spread of COVID-19 in the United States: County-Level Panel Data Analysis," Papers 2102.10453, arXiv.org, revised Jun 2021.

    Cited by:

    1. Ingo E. Isphording & Marc Diederichs & Reyn van Ewijk & Nico Pestel, 2021. "Schools under mandatory testing can mitigate the spread of SARS-CoV-2," Working Papers 2116, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    2. Emanuele Amodio & Michele Battisti & Antonio Francesco Gravina & Andrea Mario Lavezzi & Giuseppe Maggio, 2023. "School‐age vaccination, school openings and Covid‐19 diffusion," Health Economics, John Wiley & Sons, Ltd., vol. 32(5), pages 1084-1100, May.
    3. Kisho Hoshi & Hiroyuki Kasahara & Ryo Makioka & Michio Suzuki & Satoshi Tanaka, 2021. "Trade-off between job losses and the spread of COVID-19 in Japan," The Japanese Economic Review, Springer, vol. 72(4), pages 683-716, October.
    4. Koppa, Vijetha & West, Jeremy, 2022. "School reopenings, COVID-19, and employment," Santa Cruz Department of Economics, Working Paper Series qt5r67b4rc, Department of Economics, UC Santa Cruz.
    5. Jara, Karen Trujillo & Hernandez, Jose Ignacio & Mouter, Niek & Brouwer, Werner & van Exel, Job, 2025. "Preferences of citizens in Peru for school opening during a public-health crisis: A participatory value evaluation study," Social Science & Medicine, Elsevier, vol. 365(C).
    6. André Kurmann & Étienne Lalé, 2021. "School Closures and Effective In-Person Learning during COVID-19: When, Where, and for Whom," CIRANO Working Papers 2021s-42, CIRANO.
    7. Dan Goldhaber & Scott A. Imberman & Katharine O. Strunk & Bryant G. Hopkins & Nate Brown & Erica Harbatkin & Tara Kilbride, 2022. "To What Extent Does In‐Person Schooling Contribute To The Spread Of Covid‐19? Evidence From Michigan And Washington," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 41(1), pages 318-349, January.
    8. Boucekkine, Raouf & Chakraborty, Shankha & Goenka, Aditya & Liu, Lin, 2024. "Economic epidemiological modelling: A progress report," Journal of Mathematical Economics, Elsevier, vol. 113(C).
    9. Salvatore Lattanzio, 2023. "Schools and the transmission of Sars-Cov-2: evidence from Italy," Temi di discussione (Economic working papers) 1401, Bank of Italy, Economic Research and International Relations Area.
    10. Amodio, Emanuele & Battisti, Michele & Kourtellos, Andros & Maggio, Giuseppe & Maida, Carmelo Massimo, 2022. "Schools opening and Covid-19 diffusion: Evidence from geolocalized microdata," European Economic Review, Elsevier, vol. 143(C).

  21. Victor Chernozhukov & Hiroyuki Kasahara & Paul Schrimpf, 2021. "The Association of Opening K-12 Schools and Colleges with the Spread of Covid-19 in the United States: County-Level Panel Data Analysis," CESifo Working Paper Series 8929, CESifo.

    Cited by:

    1. Ingo E. Isphording & Marc Diederichs & Reyn van Ewijk & Nico Pestel, 2021. "Schools under mandatory testing can mitigate the spread of SARS-CoV-2," Working Papers 2116, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    2. Koppa, Vijetha & West, Jeremy, 2021. "School Reopenings, COVID-19, and Employment," Santa Cruz Department of Economics, Working Paper Series qt07w4z6vb, Department of Economics, UC Santa Cruz.
    3. André Kurmann & Étienne Lalé, 2021. "School Closures and Effective In-Person Learning during COVID-19: When, Where, and for Whom," CIRANO Working Papers 2021s-42, CIRANO.
    4. Dan Goldhaber & Scott A. Imberman & Katharine O. Strunk & Bryant G. Hopkins & Nate Brown & Erica Harbatkin & Tara Kilbride, 2022. "To What Extent Does In‐Person Schooling Contribute To The Spread Of Covid‐19? Evidence From Michigan And Washington," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 41(1), pages 318-349, January.
    5. Emanuele Amodio & Michele Battisti & Antonio Francesco Gravina & Andrea Mario Lavezzi & Giuseppe Maggio, 2022. "School-age Vaccination, School Openings and Covid-19 diffusion," Papers 2203.12331, arXiv.org.
    6. Boucekkine, Raouf & Chakraborty, Shankha & Goenka, Aditya & Liu, Lin, 2024. "Economic epidemiological modelling: A progress report," Journal of Mathematical Economics, Elsevier, vol. 113(C).
    7. Lattanzio, Salvatore, 2024. "Schools and the transmission of Sars-Cov-2: Evidence from Italy," Economics & Human Biology, Elsevier, vol. 52(C).
    8. Amodio, Emanuele & Battisti, Michele & Kourtellos, Andros & Maggio, Giuseppe & Maida, Carmelo Massimo, 2022. "Schools opening and Covid-19 diffusion: Evidence from geolocalized microdata," European Economic Review, Elsevier, vol. 143(C).

  22. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.

    Cited by:

    1. Wang, Lulu & Lyu, Jie & Wang, Shanshan & Zhang, Junyan, 2025. "Unveiling the influence of agricultural mechanization on greenhouse gas emission intensity: Insights from China using causal machine learning model," Agricultural Systems, Elsevier, vol. 226(C).
    2. Bingnan Guo & Yuren Qian & Xinyan Guo & Hao Zhang, 2025. "Impact of Zero-Waste City Pilot Policies on Urban Energy Consumption Intensity: Causal Inference Based on Double Machine Learning," Sustainability, MDPI, vol. 17(11), pages 1-25, May.
    3. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    4. Chen, Daniel L. & Loecher, Markus, 2025. "Mood and the malleability of moral reasoning: the impact of irrelevant factors on judicial decisions," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 116(C).
    5. Lulu Wang & Jie Lyu & Junyan Zhang, 2024. "Explicating the Role of Agricultural Socialized Services on Chemical Fertilizer Use Reduction: Evidence from China Using a Double Machine Learning Model," Agriculture, MDPI, vol. 14(12), pages 1-16, November.
    6. Sven Klaassen & Jan Rabenseifner & Jannis Kueck & Philipp Bach, 2025. "Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators," Papers 2503.17290, arXiv.org, revised May 2025.
    7. Jose E. Gomez-Gonzalez & Jorge M. Uribe & Oscar M. Valencia, 2023. "Sovereign Risk and Economic Complexity: Machine Learning Insights on Causality and Prediction," IREA Working Papers 202315, University of Barcelona, Research Institute of Applied Economics, revised Nov 2023.
    8. Julius Schaper, 2025. "Residualised Treatment Intensity and the Estimation of Average Partial Effects," Papers 2502.10301, arXiv.org.
    9. Michael C. Knaus, 2024. "Treatment Effect Estimators as Weighted Outcomes," Papers 2411.11559, arXiv.org, revised Dec 2024.
    10. Uribe, Jorge M., 2025. "Investment in intangible assets and economic complexity," Research Policy, Elsevier, vol. 54(1).
    11. Philipp Bach & Victor Chernozhukov & Sven Klaassen & Martin Spindler & Jan Teichert-Kluge & Suhas Vijaykumar, 2024. "Adventures in Demand Analysis Using AI," Papers 2501.00382, arXiv.org, revised Feb 2026.

  23. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.

    Cited by:

    1. Aluko, Olufemi Adewale & Opoku, Eric Evans Osei, 2022. "The financial development impact of financial globalization revisited: A focus on OECD countries," International Economics, Elsevier, vol. 169(C), pages 13-29.
    2. Jun Ma & Vadim Marmer & Zhengfei Yu, 2021. "Inference on Individual Treatment Effects in Nonseparable Triangular Models," Papers 2107.05559, arXiv.org, revised Feb 2023.
    3. Wenjie Wang & Yichong Zhang, 2021. "Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters," Papers 2108.13707, arXiv.org, revised Jan 2024.
    4. Elisa Toledo & Wilman Santiago Ochoa-Moreno & Rafael Alvarado & Lizeth Cuesta & Muntasir Murshed & Abdul Rehman, 2022. "Forest Area: Old and New Factors That Affect Its Dynamics," Sustainability, MDPI, vol. 14(7), pages 1-17, March.
    5. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
    6. Shuyuan Chen & Peng Zhang & Yifan Cui, 2025. "Identification and Debiased Learning of Causal Effects with General Instrumental Variables," Papers 2510.20404, arXiv.org, revised Feb 2026.
    7. Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2021. "A first-stage representation for instrumental variables quantile regression," Papers 2102.01212, arXiv.org, revised Feb 2022.

  24. Daron Acemoglu & Victor Chernozhukov & Iván Werning & Michael D. Whinston, 2020. "Optimal Targeted Lockdowns in a Multi-Group SIR Model," NBER Working Papers 27102, National Bureau of Economic Research, Inc.

    Cited by:

    1. Holger Strulik & Volker Grossmann, 2022. "Life Cycle Economics with Infectious and Chronic Diseases," CESifo Working Paper Series 10141, CESifo.
    2. Aditya Goenka & Lin Liu & Manh-Hung Nguyen, 2021. "SIR Economic Epidemiological Models with Disease Induced Mortality," Working Papers 202103, University of Liverpool, Department of Economics.
    3. Victor Aguirregabiria & Jiaying Gu & Yao Luo & Pedro Mira, 2020. "A Dynamic Structural Model of Virus Diffusion and Network Production: A First Report," Working Papers tecipa-665, University of Toronto, Department of Economics.
    4. Sabina Marchetti & Alessandro Borin & Francesco Paolo Conteduca & Giuseppe Ilardi & Giorgio Guzzetta & Piero Poletti & Patrizio Pezzotti & Antonino Bella & Paola Stefanelli & Flavia Riccardo & Stefano, 2022. "An Epidemic Model for SARS-CoV-2 with Self-Adaptive Containment Measures," Questioni di Economia e Finanza (Occasional Papers) 681, Bank of Italy, Economic Research and International Relations Area.
    5. Cem Cakmakli & Yasin Simsek, 2023. "Bridging the Covid-19 Data and the Epidemiological Model using Time-Varying Parameter SIRD Model," Papers 2301.13692, arXiv.org.
    6. Mellace, Giovanni & Crudu, Frederico & Di Stefano, Roberta & Tiezzi, Silvia, 2022. "The Gray Zone," Discussion Papers on Economics 5/2022, University of Southern Denmark, Department of Economics.
    7. Kumar, Anand & Priya, Bhawna & Srivastava, Samir K., 2021. "Response to the COVID-19: Understanding implications of government lockdown policies," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 76-94.
    8. Guimarães, Luís, 2021. "Antibody tests: They are more important than we thought," Journal of Mathematical Economics, Elsevier, vol. 93(C).
    9. Thuilliez, Josselin & Touré, Nouhoum, 2024. "Opinions and vaccination during an epidemic," Journal of Mathematical Economics, Elsevier, vol. 111(C).
    10. Géraldine Bouveret & Antoine Mandel, 2021. "Social interactions and the prophylaxis of SI epidemics on networks," Post-Print halshs-03165772, HAL.
    11. Gerson Javier Pérez-Valbuena & Diana Ricciulli-Marín & Jaime Bonet-Morón & Paula Barrios, 2021. "Reglas fiscales subnacionales en Colombia: desde su concepción hasta los resultados frente al COVID-19," Documentos de trabajo sobre Economía Regional y Urbana 297, Banco de la Republica de Colombia.
    12. Martin Gonzalez-Eiras & Dirk Niepelt, 2020. "Optimally Controlling an Epidemic," Working Papers 20.06, Swiss National Bank, Study Center Gerzensee.
    13. Pol Antras & Stephen J Redding & Esteban Rossi Hansberg, 2020. "Globalization and Pandemics," Working Papers 267, Princeton University, Department of Economics, Center for Economic Policy Studies..
    14. Van Rens, Thijs & Oswald, Andrew J., 2020. "Age-Based Policy in the Context of the Covid-19 Pandemic," The Warwick Economics Research Paper Series (TWERPS) 1315, University of Warwick, Department of Economics.
    15. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2022. "Social Distancing, Vaccination and Evolution of Covid-19 Transmission Rates in Europe," CESifo Working Paper Series 9754, CESifo.
    16. Bisin, Alberto & Moro, Andrea, 2022. "Spatial‐SIR with network structure and behavior: Lockdown rules and the Lucas critique," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 370-388.
    17. Lopolito, Antonio & Caferra, Rocco & Nigri, Andrea & Morone, Piergiuseppe, 2024. "An evaluation of the impact of mitigation policies on health and the economy by managing social distancing during outbreaks," Evaluation and Program Planning, Elsevier, vol. 103(C).
    18. Graham, James & Ozbilgin, Murat, 2021. "Age, industry, and unemployment risk during a pandemic lockdown," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).
    19. Yothin Jinjarak & Rashad Ahmed & Sameer Nair-Desai & Weining Xin & Joshua Aizenman, 2020. "Accounting for Global COVID-19 Diffusion Patterns, January-April 2020," NBER Working Papers 27185, National Bureau of Economic Research, Inc.
    20. Attar, M. Aykut & Tekin-Koru, Ayça, 2022. "Latent social distancing: Identification, causes and consequences," Economic Systems, Elsevier, vol. 46(1).
    21. Glover, Andrew & Heathcote, Jonathan & Krueger, Dirk, 2022. "Optimal age-Based vaccination and economic mitigation policies for the second phase of the covid-19 pandemic," Journal of Economic Dynamics and Control, Elsevier, vol. 140(C).
    22. Dongwoo Kim & Young Jun Lee, 2021. "Vaccination strategies and transmission of COVID-19: evidence across advanced countries," Papers 2109.06453, arXiv.org, revised Jan 2022.
    23. Béland, Louis-Philippe & Brodeur, Abel & Wright, Taylor, 2020. "COVID-19, Stay-At-Home Orders and Employment: Evidence from CPS Data," IZA Discussion Papers 13282, IZA Network @ LISER.
    24. M. Hashem Pesaran & Cynthia Fan Yang, 2020. "Matching Theory and Evidence on Covid-19 Using a Stochastic Network SIR Model," CESifo Working Paper Series 8695, CESifo.
    25. Goodkin-Gold, Matthew & Kremer, Michael & Snyder, Christopher M. & Williams, Heidi, 2022. "Optimal vaccine subsidies for endemic diseases," International Journal of Industrial Organization, Elsevier, vol. 84(C).
    26. Heinrich, Torsten, 2021. "Epidemics in modern economies," MPRA Paper 107578, University Library of Munich, Germany.
    27. Cheng, Chunli, 2022. "Beyond death: The impact of a population-wide health shock on life insurance," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    28. Nicola Fuchs-Schündeln & Dirk Krueger & André Kurmann & Étienne Lalé & Alexander Ludwig & Irina Popova, 2021. "The fiscal and welfare effects of policy responses to the Covid-19 school closures," CIRANO Working Papers 2021s-40, CIRANO.
    29. Krista Ruffini & Aaron Sojourner & Abigail Wozniak, 2020. "Who’s In and Who’s Out under Workplace COVID Symptom Screening?," Opportunity and Inclusive Growth Institute Working Papers 36, Federal Reserve Bank of Minneapolis.
    30. Ian M. Trotter & Lu'is A. C. Schmidt & Bruno C. M. Pinto & Andrezza L. Batista & J'essica Pellenz & Maritza Isidro & Aline Rodrigues & Attawan G. S. Suela & Loredany Rodrigues, 2020. "COVID-19 and Global Economic Growth: Policy Simulations with a Pandemic-Enabled Neoclassical Growth Model," Papers 2005.13722, arXiv.org, revised Jun 2020.
    31. Hakan Yilmazkuday, 2022. "Nonlinear Effects of Mobility on COVID-19 in the U.S.: Targeted Lockdowns Based on Income and Poverty," Working Papers 2205, Florida International University, Department of Economics.
    32. Jose Carlos Saavedra & Pablo Lavado & Sebastián Lindley & Liz Villegas, 2021. "Impacto de las medidas para la mitigación de la Covid - 19 en la Salud y en la Economía para Latinoamérica y Perú," Working Papers 181, Peruvian Economic Association.
    33. Lukasz Rachel, 2025. "The second wave," Review of Economic Design, Springer;Society for Economic Design, vol. 29(1), pages 87-113, February.
    34. Chen, Xiao & Huang, Hanwei & Ju, Jiandong & Sun, Ruoyan & Zhang, Jialiang, 2022. "Endogenous cross-region human mobility and pandemics," LSE Research Online Documents on Economics 117907, London School of Economics and Political Science, LSE Library.
    35. Constantin Bürgi & Nisan Gorgulu, 2020. "Social Distancing and the Economic Impact of Covid-19 in the United States," CESifo Working Paper Series 8577, CESifo.
    36. Robert E. Hall & Charles I. Jones & Peter J. Klenow, 2020. "Trading Off Consumption and COVID-19 Deaths," NBER Working Papers 27340, National Bureau of Economic Research, Inc.
    37. Ricardo Martínez & Juan D Moreno Ternero, 2022. "An axiomatic approach towards pandemic performance indicators," ThE Papers 22/12, Department of Economic Theory and Economic History of the University of Granada..
    38. Shami, Labib & Lazebnik, Teddy, 2022. "Economic aspects of the detection of new strains in a multi-strain epidemiological–mathematical model," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    39. Reyna-Lara, Adriana & Soriano-Paños, David & Arenas, Alex & Gómez-Gardeñes, Jesús, 2022. "The interconnection between independent reactive control policies drives the stringency of local containment," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    40. Jasper Verschuur & Elco E Koks & Jim W Hall, 2021. "Global economic impacts of COVID-19 lockdown measures stand out in high-frequency shipping data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-16, April.
    41. Christian Bredemeier & Falko Juessen & Roland Winkler, 2023. "Bringing Back the Jobs Lost to Covid‐19: The Role of Fiscal Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(7), pages 1703-1747, October.
    42. Gori, Luca & Manfredi, Piero & Marsiglio, Simone & Sodini, Mauro, 2023. "A parsimonious model of optimal social distancing and vaccination during an outbreak," GLO Discussion Paper Series 1263, Global Labor Organization (GLO).
    43. Donfouet,Hermann Pythagore Pierre & Sanogo,Ndoh Ashken & Gogoua,Amantchi Jean - Noel & Tshivuila Matala,Opope Oyaka & Traore,Adama-000558561 & Sorgho,Gaston, 2024. "Mortality Costs of and Policy Responses to the COVID-19 Pandemic in Côte d'Ivoire," Policy Research Working Paper Series 10731, The World Bank.
    44. Zéphirin Nganmeni & Roland Pongou & Bertrand Tchantcho & Jean-Baptiste Tondji, 2022. "Vaccine and Inclusion," Working Papers 2202E Classification-C62,, University of Ottawa, Department of Economics.
    45. Mira Baude & Alf Kimms, 2024. "Modelling pandemic behavior with a network-SIRD approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(2), pages 521-541, June.
    46. Amr Hosny & Kevin Pallara, 2023. "Economic Activity, Fiscal Space and Types of COVID-19 Containment Measures," Economics of Disasters and Climate Change, Springer, vol. 7(3), pages 319-355, November.
    47. Davide Bosco & Luca Portoghese, 2024. "Complementarity, Congestion and Information Design in Epidemics with Strategic Social Behaviour," DEM Working Papers Series 218, University of Pavia, Department of Economics and Management.
    48. Adolfo Rubinstein & Eduardo Levy Yeyati & Alejandro López Osornio & Federico Filippini & Adrian Santoro & Cintia Cejas & Ariel Bardach & Alfredo Palacios & Fernando Argento & Jamile Balivian & Federic, 2022. "An Integrated Epidemiological and Economic Model of Covid-19 NPIs in Argentina," CID Working Papers 421, Center for International Development at Harvard University.
    49. Vahagn Jerbashian & Luiz Brotherhood, 2023. "Firm behavior during an epidemic," UB School of Economics Working Papers 2023/439, University of Barcelona School of Economics.
    50. Jose M. Martin-Moreno & Antoni Alegre-Martinez & Victor Martin-Gorgojo & Jose Luis Alfonso-Sanchez & Ferran Torres & Vicente Pallares-Carratala, 2022. "Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(9), pages 1-16, May.
    51. Camera, Gabriele & Gioffré, Alessandro, 2021. "The economic impact of lockdowns: A theoretical assessment," Journal of Mathematical Economics, Elsevier, vol. 97(C).
    52. Beppu, Shotaro & Fujii, Daisuke & Kubota, Hiroyuki & Machi, Kohei & Maeda, Yuta & Nakata, Taisuke & Shibuya, Haruki, 2023. "Cross-regional heterogeneity in health and economic outcomes during the COVID-19 pandemic: An analysis of Japan," Journal of the Japanese and International Economies, Elsevier, vol. 70(C).
    53. Andrea Aspri & Elena Beretta & Alberto Gandolfi & Etienne Wasmer, 2020. "Mortality containment vs. economics opening: optimal policies in a SEIARD model," Papers 2006.00085, arXiv.org.
    54. Adel Ben Youssef & Burim Prenaj, 2025. "Job loss during COVID-19: estimating the poverty and food security effects in Egypt, Tunisia and Morocco," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 15(2), pages 563-592, June.
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  25. Daron Acemoglu & Victor Chernozhukov & Ivàn Werning & Michael D. Whinston, 2020. "A Multi-Risk SIR Model with Optimally Targeted Lockdown," CeMMAP working papers CWP14/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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    1. Eric Alexander Sugandi, 2022. "Indonesia’s Financial Markets and Monetary Policy Dynamics Amid the COVID-19 Pandemic," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(3), pages 411-447, September.
    2. Christian Gollier, 2020. "Cost–benefit analysis of age‐specific deconfinement strategies," Post-Print hal-03156641, HAL.
    3. Djamel KIRAT & Yassine KIRAT, 2020. "An international Comparison of the Economic Impacts of the COVID-19 Pandemic," LEO Working Papers / DR LEO 2818, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    4. Kent A. Smetters, 2020. "Stay-at-home orders and second waves: a graphical exposition," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 45(2), pages 94-103, September.
    5. Jonas E. Arias & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Minchul Shin, 2022. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," Working Papers 22-18, Federal Reserve Bank of Philadelphia.
    6. Victor Aguirregabiria & Jiaying Gu & Yao Luo & Pedro Mira, 2020. "A Dynamic Structural Model of Virus Diffusion and Network Production: A First Report," Working Papers tecipa-665, University of Toronto, Department of Economics.
    7. Shi, Wei & Qiu, Yun & Yu, Pei & Chen, Xi, 2022. "Optimal Travel Restrictions in Epidemics," IZA Discussion Papers 15290, IZA Network @ LISER.
    8. Ichino, Andrea & Favero, Carlo A. & Rustichini, Aldo, 2020. "Restarting the economy while saving lives under Covid-19," CEPR Discussion Papers 14664, C.E.P.R. Discussion Papers.
    9. Fernández-Villaverde, Jesús & Jones, Chad, 2020. "Estimating and Simulating a SIRD Model of COVID-19 for Many Countries, States, and Cities," CEPR Discussion Papers 14711, C.E.P.R. Discussion Papers.
    10. Krause, Willi & Costa, Luís & Costa Filho, João Ricardo, 2023. "The Covid-19 Recession in Germany: A Macropidemiological Analysis," Working Papers REM 2023/0290, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    11. Brotherhood, Luiz & Jerbashian, Vahagn, 2020. "Firm behavior during an epidemic," GLO Discussion Paper Series 629, Global Labor Organization (GLO).
    12. Decerf, Benoit & Ferreira, Francisco H. G. & Mahler, Daniel Gerszon & Sterck, Olivier, 2020. "Lives and Livelihoods: Estimates of the Global Mortality and Poverty Effects of the COVID-19 Pandemic," IZA Discussion Papers 13549, IZA Network @ LISER.
    13. Augeraud-Véron, E. & Fabbri, G. & Schubert, K., 2020. "Prevention and mitigation of epidemics:Biodiversity conservation and confinement policies," Working Papers 2020-11, Grenoble Applied Economics Laboratory (GAEL).
    14. Christian Gollier, 2020. "Pandemic economics: optimal dynamic confinement under uncertainty and learning," Post-Print hal-03180664, HAL.
    15. Giorgio Fabbri & Salvatore Federico & Davide Fiaschi & Fausto Gozzi, 2024. "Mobility decisions, economic dynamics and epidemic," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 77(1), pages 495-531, February.
    16. Khan, Haider, 2020. "Economic Impact of COVID-19 On Bangladesh: Agenda for Immediate Action and Planning for the Future," MPRA Paper 100380, University Library of Munich, Germany.
    17. Fernández-Villaverde, Jesús & Arias, Jonas & Rubio-Ramírez, Juan Francisco & Shin, Minchul, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CEPR Discussion Papers 15951, C.E.P.R. Discussion Papers.
    18. Hong, Jimin & Kim, Kyungsun & Seog, S. Hun, 2024. "Private efforts, public test policy and insurance against pandemic health risks," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    19. Farhi, Emmanuel & Baqaee, David Rezza, 2020. "Nonlinear Production Networks with an Application to the Covid-19 Crisis," CEPR Discussion Papers 14742, C.E.P.R. Discussion Papers.
    20. Nemati Fard, Lorenzo Amir & Bisin, Alberto & Starnini, Michele & Tizzoni, Michele, 2025. "Modeling adaptive forward-looking behavior in epidemics on networks," Journal of Economic Behavior & Organization, Elsevier, vol. 232(C).
    21. Li, Shaoran & Linton, Oliver, 2021. "When will the Covid-19 pandemic peak?," Journal of Econometrics, Elsevier, vol. 220(1), pages 130-157.
    22. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    23. Daniel Levy & Tamir Mayer & Alon Raviv, 2022. "Economists in the 2008 Financial Crisis: Slow to See, Fast to Act," Working Papers 2022-01, Bar-Ilan University, Department of Economics.
    24. Yothin Jinjarak & Rashad Ahmed & Sameer Nair-Desai & Weining Xin & Joshua Aizenman, 2020. "Accounting for Global COVID-19 Diffusion Patterns, January-April 2020," NBER Working Papers 27185, National Bureau of Economic Research, Inc.
    25. Caulkins, J.P. & Grass, D. & Feichtinger, G. & Hartl, R.F. & Kort, P.M. & Kuhn, M. & Prskawetz, A. & Sanchez-Romero, M. & Seidl, A. & Wrzaczek, S., 2023. "The hammer and the jab: Are COVID-19 lockdowns and vaccinations complements or substitutes?," European Journal of Operational Research, Elsevier, vol. 311(1), pages 233-250.
    26. Saito, Yuta & Sakamoto, Jun, 2021. "Asset pricing during pandemic lockdown," Research in International Business and Finance, Elsevier, vol. 58(C).
    27. Jonathan Caulkins & Dieter Grass & Gustav Feichtinger & Richard Hartl & Peter M Kort & Alexia Prskawetz & Andrea Seidl & Stefan Wrzaczek, 2020. "How long should the COVID-19 lockdown continue?," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-19, December.
    28. Garibaldi, Pietro & Moen, Espen R. & Pissarides, Christopher A., 2020. "Static and Dynamic Inefficiencies in an Optimizing Model of Epidemics," IZA Discussion Papers 13844, IZA Network @ LISER.
    29. Pestieau, Pierre & Ponthiere, Gregory, 2020. "Optimal Lockdown and Social Welfare," LIDAM Discussion Papers CORE 2020032, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    4. Sabina Marchetti & Alessandro Borin & Francesco Paolo Conteduca & Giuseppe Ilardi & Giorgio Guzzetta & Piero Poletti & Patrizio Pezzotti & Antonino Bella & Paola Stefanelli & Flavia Riccardo & Stefano, 2022. "An Epidemic Model for SARS-CoV-2 with Self-Adaptive Containment Measures," Questioni di Economia e Finanza (Occasional Papers) 681, Bank of Italy, Economic Research and International Relations Area.
    5. Mellace, Giovanni & Crudu, Frederico & Di Stefano, Roberta & Tiezzi, Silvia, 2022. "The Gray Zone," Discussion Papers on Economics 5/2022, University of Southern Denmark, Department of Economics.
    6. Fernández-Villaverde, Jesús & Arias, Jonas & Rubio-Ramírez, Juan Francisco & Shin, Minchul, 2021. "Bayesian Estimation of Epidemiological Models: Methods, Causality, and Policy Trade-Offs," CEPR Discussion Papers 15951, C.E.P.R. Discussion Papers.
    7. Seres, Gyula & Balleyer, Anna & Cerutti, Nicola & Friedrichsen, Jana & Süer, Müge, 2021. "Face Mask Use and Physical Distancing Before and After Mandatory Masking: No Evidence on Risk Compensation in Public Waiting Lines," Rationality and Competition Discussion Paper Series 300, CRC TRR 190 Rationality and Competition.
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    20. Dorn, Florian & Lange, Berit & Braml, Martin & Gstrein, David & Nyirenda, John L.Z. & Vanella, Patrizio & Winter, Joachim & Fuest, Clemens & Krause, Gérard, 2023. "The challenge of estimating the direct and indirect effects of COVID-19 interventions – Toward an integrated economic and epidemiological approach," Economics & Human Biology, Elsevier, vol. 49(C).
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    63. Berta, Paolo & Lovaglio, Pietro Giorgio & Paruolo, Paolo & Verzillo, Stefano, 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand," JRC Working Papers in Economics and Finance 2020-08, Joint Research Centre, European Commission.
    64. Regina Pleninger & Sina Streicher & Jan-Egbert Sturm, 2022. "Do COVID-19 containment measures work? Evidence from Switzerland," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-24, December.
    65. Hartl, Tobias, 2021. "Monitoring the pandemic: A fractional filter for the COVID-19 contact rate," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242380, Verein für Socialpolitik / German Economic Association.
    66. Gyula Seres & Anna Helen Balleyer & Nicola Cerutti & Anastasia Danilov & Jana Friedrichsen & Yiming Liu & Müge Süer, 2021. "Face masks increase compliance with physical distancing recommendations during the COVID-19 pandemic," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 7(2), pages 139-158, December.
    67. Matheus Pereira Libório & Petr Iakovlevitch Ekel & Carlos Augusto Paiva Martins, 2023. "Economic analysis through alternative data and big data techniques: what do they tell about Brazil?," SN Business & Economics, Springer, vol. 3(1), pages 1-16, January.
    68. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 10/22, Monash University, Department of Econometrics and Business Statistics.
    69. Andrei Zubarev & Maria Kirillova, 2022. "Modeling COVID-19 spread in the Russian Federation using global VAR approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 117-138.
    70. Julliard, Christian & Shi, Ran & Yuan, Kathy, 2023. "The spread of COVID-19 in London: network effects and optimal lockdowns," LSE Research Online Documents on Economics 118825, London School of Economics and Political Science, LSE Library.
    71. Jonas E. Arias & Jesús Fernández-Villaverde & Juan Rubio Ramírez & Minchul Shin, 2021. "The Causal Effects of Lockdown Policies on Health and Macroeconomic Outcomes," NBER Working Papers 28617, National Bureau of Economic Research, Inc.
    72. Daniel Goetz, 2022. "Does providing free internet access to low‐income households affect COVID‐19 spread?," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2648-2663, December.
    73. Jonas Herby & Lars Jonung & Steve H. Hanke, 2025. "Were COVID-19 lockdowns worth it? A meta-analysis," Public Choice, Springer, vol. 203(3), pages 337-367, June.
    74. Martin Huber & Henrika Langen, 2020. "Timing matters: the impact of response measures on COVID-19-related hospitalization and death rates in Germany and Switzerland," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-19, December.
    75. Léa BOU SLEIMAN & Germain GAUTHIER, 2020. "COVID-19: Reduced forms have gone viral, but what do they tell us?," Working Papers 2020-32, Center for Research in Economics and Statistics, revised 18 Jan 2021.
    76. Albert Okunade & Favour Olarewaju & Babasoji Oyemakinde & Gregory Lubiani, 2025. "Determinants of COVID-19 mortality among the US law enforcement officers," Journal of Population Research, Springer, vol. 42(3), pages 1-24, September.
    77. Dion Bongaerts & Francesco Mazzola & Wolf Wagner, 2021. "Closed for business: The mortality impact of business closures during the Covid-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-17, May.
    78. Ewen Gallic & Michel Lubrano & Pierre Michel, 2022. "Optimal lockdowns for COVID‐19 pandemics: Analyzing the efficiency of sanitary policies in Europe," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 24(5), pages 944-967, October.
    79. Florian Dorn & Sahamoddin Khailaie & Marc Stoeckli & Sebastian C. Binder & Tanmay Mitra & Berit Lange & Stefan Lautenbacher & Andreas Peichl & Patrizio Vanella & Timo Wollmershäuser & Clemens Fuest & , 2023. "The common interests of health protection and the economy: evidence from scenario calculations of COVID-19 containment policies," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(1), pages 67-74, February.
    80. Peter A.G. van Bergeijk, 2021. "Pandemic Economics," Books, Edward Elgar Publishing, number 20401.
    81. Zhaohui Su, 2021. "Rigorous Policy-Making Amid COVID-19 and Beyond: Literature Review and Critical Insights," IJERPH, MDPI, vol. 18(23), pages 1-17, November.
    82. Carnehl, Christoph & Fukuda, Satoshi & Kos, Nenad, 2023. "Epidemics with behavior," Journal of Economic Theory, Elsevier, vol. 207(C).
    83. Difang Huang & Ying Liang & Boyao Wu & Yanyi Ye, 2024. "Estimating the Impact of Social Distance Policy in Mitigating COVID-19 Spread with Factor-Based Imputation Approach," Papers 2405.12180, arXiv.org.
    84. Millimet, Daniel L. & Parmeter, Christopher F., 2021. "COVID-19 Severity: A New Approach to Quantifying Global Cases and Deaths," IZA Discussion Papers 14116, IZA Network @ LISER.
    85. Blas A. Marin-Lopez & David Jimenez-Gomez & José-María Abellán-Perpiñán, 2022. "Behavioral Economics in the Epidemiology of the COVID-19 Pandemic: Theory and Simulations," IJERPH, MDPI, vol. 19(15), pages 1-23, August.
    86. Yang Han & Jacqueline C. K. Lam & Victor O. K. Li & Jon Crowcroft, 2024. "Interpretable AI-driven causal inference to uncover the time-varying effects of PM2.5 and public health interventions on COVID-19 infection rates," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
    87. Germain Gauthier, 2021. "On the Use of Two-Way Fixed Effects Models for Policy Evaluation During Pandemics," Papers 2106.10949, arXiv.org.
    88. Cooper, Daniel & Garga, Vaishali & Luengo-Prado, María José & Tang, Jenny, 2023. "The mitigating effect of masks on the spread of Covid-19," Economics & Human Biology, Elsevier, vol. 48(C).
    89. Checo, Ariadne & Grigoli, Francesco & Mota, Jose M., 2021. "Assessing Targeted Containment Policies to Fight COVID-19," GLO Discussion Paper Series 752, Global Labor Organization (GLO).
    90. Girardi, Alessandro & Ventura, Marco, 2023. "The cost of waiting and the death toll in Italy during the first wave of the covid-19 pandemic," Health Policy, Elsevier, vol. 134(C).
    91. Dhaval Dave & Drew McNichols & Joseph J. Sabia, 2022. "Political violence, risk aversion, and population health: Evidence from the US Capitol riot," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1345-1384, October.
    92. Nada Wasi, 2022. "Comment on “Responses to COVID‐19 in Southeast Asia: Diverse Paths and Ongoing Challenges”," Asian Economic Policy Review, Japan Center for Economic Research, vol. 17(1), pages 111-113, January.
    93. Ghosh Debashis & Wang Lei, 2026. "Generalized coarsened confounding for causal effects: a large-sample framework," Journal of Causal Inference, De Gruyter, vol. 14(1), pages 1-21.
    94. Bisin, Alberto & Moro, Andrea, 2022. "JUE insight: Learning epidemiology by doing: The empirical implications of a Spatial-SIR model with behavioral responses," Journal of Urban Economics, Elsevier, vol. 127(C).
    95. Hensel, Lukas & Witte, Marc & Caria, A. Stefano & Fetzer, Thiemo & Fiorin, Stefano & Götz, Friedrich M. & Gomez, Margarita & Haushofer, Johannes & Ivchenko, Andriy & Kraft-Todd, Gordon & Reutskaja, El, 2022. "Global Behaviors, Perceptions, and the Emergence of Social Norms at the Onset of the COVID-19 Pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 193(C), pages 473-496.
    96. Wei Tian & Seojeong Lee & Valentyn Panchenko, 2023. "Synthetic Controls with Multiple Outcomes," Papers 2304.02272, arXiv.org, revised Jul 2024.
    97. Hansen, Niels-Jakob H. & Mano, Rui C., 2023. "Mask mandates save lives," Journal of Health Economics, Elsevier, vol. 88(C).
    98. Jessica Carrick-Hagenbarth & Eric Edlund & Avanti Mukherjee, 2023. "Analysis of Hybrid Epidemiological-Economic Models of COVID-19 Mitigation Policies," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 49(4), pages 585-612, October.
    99. Emanuel Nussli & Simon Hediger & Meta-Lina Spohn & Marloes H. Maathuis, 2024. "The effect of a strict facial-mask policy on the spread of COVID-19 in Switzerland during the early phase of the pandemic," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-20, December.
    100. Fezzi, Carlo & Fanghella, Valeria, 2021. "Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe," European Economic Review, Elsevier, vol. 139(C).
    101. Greenhalgh, Trisha & Engebretsen, Eivind, 2022. "The science-policy relationship in times of crisis: An urgent call for a pragmatist turn," Social Science & Medicine, Elsevier, vol. 306(C).
    102. Gaggero, Alessio & Mesa-Pedrazas, Ángela & Fernández-Pérez, Ángel, 2024. "Shutting down to save lives: A regression discontinuity analysis of non-essential business closure," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    103. Berta, P. & Bratti, M. & Fiorio, C.V. & Pisoni, E. & Verzillo, S., 2021. "Administrative border effects in Covid-19 related mortality," Health, Econometrics and Data Group (HEDG) Working Papers 21/21, HEDG, c/o Department of Economics, University of York.
    104. Christopher Avery & William Bossert & Adam Clark & Glenn Ellison & Sara Fisher Ellison, 2020. "An Economist's Guide to Epidemiology Models of Infectious Disease," Journal of Economic Perspectives, American Economic Association, vol. 34(4), pages 79-104, Fall.
    105. Johannes S. Kunz & Carol Propper, 2022. "Is Hospital Quality Predictive of Pandemic Deaths? Evidence from US Counties," Papers 2022-01, Centre for Health Economics, Monash University.
    106. Jiayin Pei & Zhi Lu & Xiaoming Yang, 2022. "What drives people to repost social media messages during the COVID‐19 pandemic? Evidence from the Weibo news microblog," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1609-1626, December.
    107. Sumedha Gupta & Kosali I. Simon & Coady Wing, 2020. "Mandated and Voluntary Social Distancing During The COVID-19 Epidemic: A Review," NBER Working Papers 28139, National Bureau of Economic Research, Inc.
    108. Martin Bodenstein & Giancarlo Corsetti & Luca Guerrieri, 2022. "Social distancing and supply disruptions in a pandemic," Quantitative Economics, Econometric Society, vol. 13(2), pages 681-721, May.
    109. Marlon Tracey & Alicia Plemmons & Ariel Belasen, 2022. "Throwing caution to the wind: How hurricanes affect COVID‐19 spread," Health Economics, John Wiley & Sons, Ltd., vol. 31(6), pages 1258-1265, June.
    110. Fuchs, Andreas & Kaplan, Lennart & Kis-Katos, Krisztina & Leue, Sebastian & Turbanisch, Felix & Wang, Feicheng, 2025. "Mask wars: Sourcing a critical medical product from China in times of COVID-19," Open Access Publications from Kiel Institute for the World Economy 318268, Kiel Institute for the World Economy.
    111. Jeroen Luyten & Sandy Tubeuf & Roselinde Kessels, 2022. "Rationing of a scarce life‐saving resource: Public preferences for prioritizing COVID‐19 vaccination," Health Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 342-362, February.
    112. Emiliozzi, Simone & Rondinelli, Concetta & Villa, Stefania, 2025. "Unveiling consumption patterns during COVID-19: Insights from credit cards," Economic Modelling, Elsevier, vol. 147(C).
    113. Giovanni Scabbia & Antonio Sanfilippo & Annamaria Mazzoni & Dunia Bachour & Daniel Perez-Astudillo & Veronica Bermudez & Etienne Wey & Mathilde Marchand-Lasserre & Laurent Saboret, 2022. "Does climate help modeling COVID-19 risk and to what extent?," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-27, September.
    114. Byrne,Kieran Thomas & Kondylis,Florence & Loeser,John Ashton & Mukama,Denis, 2022. "A Few Good Masks : Evidence from Mask Manufacturing in Rwanda during the COVID-19 Pandemic," Policy Research Working Paper Series 9993, The World Bank.
    115. Michael Berlemann & Erik Haustein, 2020. "Right and Yet Wrong: A Spatio-Temporal Evaluation of Germany's Covid-19 Containment Policy," CESifo Working Paper Series 8446, CESifo.
    116. Olivia S. Kim & Jonathan A. Parker & Antoinette Schoar, 2020. "Revenue Collapses and the Consumption of Small Business Owners in the Early Stages of the COVID-19 Pandemic," NBER Working Papers 28151, National Bureau of Economic Research, Inc.
    117. Michael D. Noel, 2022. "Competitive survival in a devastated industry: Evidence from hotels during COVID‐19," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 31(1), pages 3-24, February.
    118. Crudu, Federico & Di Stefano, Roberta & Mellace, Giovanni & Tiezzi, Silvia, 2024. "The gray zone: How not imposing a strict lockdown at the beginning of a pandemic can cost many lives," Labour Economics, Elsevier, vol. 89(C).
    119. Fırat Bilgel & Burhan Can Karahasan, 2024. "Understanding Covid-19 Mobility Through Human Capital: A Unified Causal Framework," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 793-833, February.
    120. Anand Chopra & Michael B. Devereux & Amartya Lahiri, 2022. "Pandemics through the lens of occupations," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 540-580, February.
    121. Ron S. Kenett & Giancarlo Manzi & Carmit Rapaport & Silvia Salini, 2022. "Integrated Analysis of Behavioural and Health COVID-19 Data Combining Bayesian Networks and Structural Equation Models," IJERPH, MDPI, vol. 19(8), pages 1-26, April.
    122. Carlos B. Carneiro & I'uri H. Ferreira & Marcelo C. Medeiros & Henrique F. Pires & Eduardo Zilberman, 2020. "Lockdown effects in US states: an artificial counterfactual approach," Papers 2009.13484, arXiv.org, revised Feb 2021.
    123. Syed Abdul Rehman Khan & Danish Iqbal Godil & Munaza Bibi & Zhang Yu & Syed Muhammad Ahsan Rizvi, 2021. "The Economic and Social Impact of Teleworking in Romania: Present Practices and Post Pandemic Developments," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(58), pages 787-787, August.
    124. Lash, Michael T. & Sajeesh, S. & Araz, Ozgur M., 2023. "Predicting mobility using limited data during early stages of a pandemic," Journal of Business Research, Elsevier, vol. 157(C).
    125. Martin Kahanec & Lukáš Lafférs & Bernhard Schmidpeter, 2021. "The impact of repeated mass antigen testing for COVID-19 on the prevalence of the disease," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1105-1140, October.
    126. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2022. "From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1517-1550, October.
    127. Martina Luskova, 2024. "The Effect of Face Masks on Covid Transmission: A Meta-Analysis," Working Papers IES 2024/2, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jan 2024.
    128. Philipp Breidenbach & Timo Mitze, 2022. "Large-scale sport events and COVID-19 infection effects: evidence from the German professional football ‘experiment’," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 15-45.
    129. Amodio, Emanuele & Battisti, Michele & Kourtellos, Andros & Maggio, Giuseppe & Maida, Carmelo Massimo, 2022. "Schools opening and Covid-19 diffusion: Evidence from geolocalized microdata," European Economic Review, Elsevier, vol. 143(C).
    130. Lipić, Tomislav & Štajduhar, Andrija & Medvidović, Luka & Wild, Dorian & Korošak, Dean & Podobnik, Boris, 2022. "Stringency without efficiency is not adequate to combat pandemics," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    131. Esaka, Taro & Fujii, Takao, 2022. "Quantifying the impact of the Tokyo Olympics on COVID-19 cases using synthetic control methods," Journal of the Japanese and International Economies, Elsevier, vol. 66(C).

  27. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.

    Cited by:

    1. AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021. "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised Mar 2022.
    2. Ayush Sawarni & Sahasrajit Sarmasarkar & Vasilis Syrgkanis, 2025. "Preference Learning with Response Time: Robust Losses and Guarantees," Papers 2505.22820, arXiv.org, revised Oct 2025.
    3. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Source Condition Double Robust Inference on Functionals of Inverse Problems," Papers 2307.13793, arXiv.org.
    4. Ravi B. Sojitra & Vasilis Syrgkanis, 2024. "Dynamic Local Average Treatment Effects," Papers 2405.01463, arXiv.org, revised May 2025.
    5. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022. "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers 2203.13887, arXiv.org, revised Jun 2023.
    6. Masahiro Kato & Masaaki Imaizumi & Kenichiro McAlinn & Haruo Kakehi & Shota Yasui, 2021. "Learning Causal Models from Conditional Moment Restrictions by Importance Weighting," Papers 2108.01312, arXiv.org, revised Sep 2022.
    7. Rahul Singh, 2021. "Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension," Papers 2102.11076, arXiv.org, revised Jul 2024.
    8. Qizhao Chen & Morgane Austern & Vasilis Syrgkanis, 2023. "Inference on Optimal Dynamic Policies via Softmax Approximation," Papers 2303.04416, arXiv.org, revised Dec 2023.
    9. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org, revised Nov 2024.
    10. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    11. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
    12. Anish Agarwal & Rahul Singh, 2021. "Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy," Papers 2107.02780, arXiv.org, revised Feb 2024.
    13. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2021. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees," Papers 2105.15197, arXiv.org, revised Oct 2022.
    14. Xiaohong Chen & Yuan Liao & Weichen Wang, 2022. "Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves," Papers 2301.00092, arXiv.org, revised Jan 2023.
    15. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
    16. Dmitry Arkhangelsky & Guido W. Imbens & Lihua Lei & Xiaoman Luo, 2021. "Design-Robust Two-Way-Fixed-Effects Regression For Panel Data," Papers 2107.13737, arXiv.org, revised Mar 2024.
    17. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests," Papers 2110.03031, arXiv.org, revised Jun 2022.
    18. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression," Papers 2112.14249, arXiv.org, revised May 2025.

  28. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2020. "Insights from Optimal Pandemic Shielding in a Multi-Group SEIR Framework," Papers 2011.01092, arXiv.org.

    Cited by:

    1. Milstein, Ricarda & Schreyögg, Jonas, 2022. "Activity-based funding based on diagnosis-related groups: The end of an era? A review of payment reforms in the inpatient sector in ten high-income countries," hche Research Papers 28, University of Hamburg, Hamburg Center for Health Economics (hche).

  29. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2019. "Improved Central Limit Theorem and bootstrap approximations in high dimensions," Papers 1912.10529, arXiv.org, revised May 2022.

    Cited by:

    1. Magne Mogstad & Joseph P. Romano & Daniel Wilhelm & Azeem M. Shaikh, 2020. "Inference for ranks with applications to mobility across neighborhoods and academic achievement across countries," CeMMAP working papers CWP10/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Jungjun Choi & Ming Yuan, 2025. "Inferential Theory for Pricing Errors with Latent Factors and Firm Characteristics," Papers 2511.03076, arXiv.org.
    3. Li, Jia & Liao, Zhipeng & Zhou, Wenyu, 2025. "A general test for functional inequalities," Journal of Econometrics, Elsevier, vol. 251(C).
    4. Bakalli, Gaetan & Guerrier, Stéphane & Scaillet, Olivier, 2023. "A penalized two-pass regression to predict stock returns with time-varying risk premia," Journal of Econometrics, Elsevier, vol. 237(2).
    5. Kock, Anders Bredahl & Preinerstorfer, David, 2024. "A remark on moment-dependent phase transitions in high-dimensional Gaussian approximations," Statistics & Probability Letters, Elsevier, vol. 211(C).
    6. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
    7. Matias D. Cattaneo & Ricardo P. Masini & William G. Underwood, 2022. "Yurinskii's Coupling for Martingales," Papers 2210.00362, arXiv.org, revised Aug 2025.
    8. Victor Chernozhukov & Sokbae Lee & Adam Rosen & Liyang Sun, 2025. "Policy learning with confidence," CeMMAP working papers 15/25, Institute for Fiscal Studies.
    9. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2022. "High-dimensional Data Bootstrap," Papers 2205.09691, arXiv.org.
    10. Cheng, Guanghui & Liu, Zhi & Peng, Liuhua, 2022. "Gaussian approximations for high-dimensional non-degenerate U-statistics via exchangeable pairs," Statistics & Probability Letters, Elsevier, vol. 182(C).
    11. Matias D. Cattaneo & Rajita Chandak & Michael Jansson & Xinwei Ma, 2022. "Boundary Adaptive Local Polynomial Conditional Density Estimators," Papers 2204.10359, arXiv.org, revised Dec 2023.
    12. Peccati, Giovanni & Turchi, Nicola, 2023. "The discrepancy between min–max statistics of Gaussian and Gaussian-subordinated matrices," Stochastic Processes and their Applications, Elsevier, vol. 158(C), pages 315-341.
    13. Kojevnikov, Denis & Song, Kyungchul, 2022. "A Berry–Esseen bound for vector-valued martingales," Statistics & Probability Letters, Elsevier, vol. 186(C).
    14. Matias D. Cattaneo & Jason M. Klusowski & Ruiqi Rae Yu, 2025. "The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation," Papers 2509.11381, arXiv.org.
    15. Matias D. Cattaneo & Rocio Titiunik & Ruiqi Rae Yu, 2025. "Estimation and Inference in Boundary Discontinuity Designs: Distance-Based Methods," Papers 2510.26051, arXiv.org.
    16. Chang, Jinyuan & Jiang, Qing & Shao, Xiaofeng, 2023. "Testing the martingale difference hypothesis in high dimension," Journal of Econometrics, Elsevier, vol. 235(2), pages 972-1000.
    17. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.

  30. Victor Chernozhukov & Jerry Hausman & Whitney K. Newey, 2019. "Demand analysis with many prices," CeMMAP working papers CWP59/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    2. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Kaicheng Chen, 2025. "Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity," Papers 2504.18772, arXiv.org, revised Dec 2025.
    4. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    5. O'Connell, Martin & Smith, Kate, 2020. "Corrective Tax Design and Market Power," CEPR Discussion Papers 14582, C.E.P.R. Discussion Papers.
    6. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    7. Martin O'Connell & Kate Smith, 2021. "Optimal sin taxation and market power," IFS Working Papers W21/30, Institute for Fiscal Studies.
    8. Ming Li, 2021. "Identification and Estimation in a Time-Varying Endogenous Random Coefficient Panel Data Model," Papers 2110.00982, arXiv.org, revised Feb 2026.
    9. Pierre Dubois & Rachel Griffith & Martin O'Connell, 2020. "How well targeted are soda taxes?," Post-Print hal-03047174, HAL.
    10. Lin, Xirong, 2023. "Food demand and cash transfers: A collective household approach with Homescan data," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 233-259.

  31. Victor Chernozhukov & Christian Hansen & Yuan Liao & Yinchu Zhu, 2019. "Inference for heterogeneous effects using low-rank estimations," CeMMAP working papers CWP31/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    2. Choi, Jungjun & Kwon, Hyukjun & Liao, Yuan, 2024. "Inference for low-rank completion without sample splitting with application to treatment effect estimation," Journal of Econometrics, Elsevier, vol. 240(1).
    3. Belloni, Alexandre & Chen, Mingli & Madrid Padilla, Oscar Hernan & Wang, Zixuan (Kevin), 2019. "High Dimensional Latent Panel Quantile Regression with an Application to Asset Pricing," The Warwick Economics Research Paper Series (TWERPS) 1230, University of Warwick, Department of Economics.
    4. Pu, Dan & Fang, Kuangnan & Lan, Wei & Yu, Jihai & Zhang, Qingzhao, 2024. "Multivariate spatiotemporal models with low rank coefficient matrix," Journal of Econometrics, Elsevier, vol. 246(1).
    5. Junlong Feng, 2019. "Regularized Quantile Regression with Interactive Fixed Effects," Papers 1911.00166, arXiv.org, revised Mar 2021.
    6. Yiren Wang & Liangjun Su & Yichong Zhang, 2022. "Low-rank Panel Quantile Regression: Estimation and Inference," Papers 2210.11062, arXiv.org.
    7. Jungjun Choi & Hyukjun Kwon & Yuan Liao, 2023. "Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation," Papers 2307.16370, arXiv.org.
    8. Jianqing Fan & Kunpeng Li & Yuan Liao, 2020. "Recent Developments on Factor Models and its Applications in Econometric Learning," Papers 2009.10103, arXiv.org.
    9. Paul Haimerl & Stephan Smeekes & Ines Wilms, 2025. "Estimation of Latent Group Structures in Time-Varying Panel Data Models," Papers 2503.23165, arXiv.org, revised Nov 2025.

  32. Mert Demirer & Vasilis Syrgkanis & Greg Lewis & Victor Chernozhukov, 2019. "Semi-Parametric Efficient Policy Learning with Continuous Actions," Papers 1905.10116, arXiv.org, revised Jul 2019.

    Cited by:

    1. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    2. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Yi Zhang & Kosuke Imai, 2023. "Individualized Policy Evaluation and Learning under Clustered Network Interference," Papers 2311.02467, arXiv.org, revised Mar 2025.
    4. AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021. "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised Mar 2022.
    5. Masahiro Kato & Masatoshi Uehara & Shota Yasui, 2020. "Off-Policy Evaluation and Learning for External Validity under a Covariate Shift," Papers 2002.11642, arXiv.org, revised Oct 2020.
    6. Masahiro Kato, 2020. "Confidence Interval for Off-Policy Evaluation from Dependent Samples via Bandit Algorithm: Approach from Standardized Martingales," Papers 2006.06982, arXiv.org.
    7. Hirano, Keisuke & Porter, Jack R., 2020. "Asymptotic analysis of statistical decision rules in econometrics," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 283-354, Elsevier.
    8. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Chunrong Ai & Yue Fang & Haitian Xie, 2024. "Data-Driven Policy Learning for Continuous Treatments," Papers 2402.02535, arXiv.org, revised Dec 2025.
    10. Andrew Bennett & Nathan Kallus, 2020. "Efficient Policy Learning from Surrogate-Loss Classification Reductions," Papers 2002.05153, arXiv.org.
    11. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    12. Ruohan Zhan & Zhimei Ren & Susan Athey & Zhengyuan Zhou, 2021. "Policy Learning with Adaptively Collected Data," Papers 2105.02344, arXiv.org, revised Nov 2022.
    13. Ruohan Zhan & Shichao Han & Yuchen Hu & Zhenling Jiang, 2024. "Estimating Treatment Effects under Recommender Interference: A Structured Neural Networks Approach," Papers 2406.14380, arXiv.org, revised Oct 2025.
    14. Cai, Hengrui & Shi, Chengchun & Song, Rui & Lu, Wenbin, 2023. "Jump interval-learning for individualized decision making with continuous treatments," LSE Research Online Documents on Economics 118231, London School of Economics and Political Science, LSE Library.
    15. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2020. "Deep Learning for Individual Heterogeneity," Papers 2010.14694, arXiv.org, revised Apr 2025.

  33. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2019. "Distributional conformal prediction," Papers 1909.07889, arXiv.org, revised Aug 2021.

    Cited by:

    1. Ghosal, Rahul & Matabuena, Marcos & Ghosh, Sujit K., 2025. "Functional time transformation model with applications to digital health," Computational Statistics & Data Analysis, Elsevier, vol. 207(C).
    2. Áureo de Paula & Elie Tamer & Weiguang Liu, 2025. "Prediction sets and conformal inference with censored outcomes," CeMMAP working papers 04/25, Institute for Fiscal Studies.
    3. Meisenbacher, Stefan & Phipps, Kaleb & Taubert, Oskar & Weiel, Marie & Götz, Markus & Mikut, Ralf & Hagenmeyer, Veit, 2025. "AutoPQ: Automating quantile estimation from point forecasts in the context of sustainability," Applied Energy, Elsevier, vol. 392(C).
    4. Weiguang Liu & 'Aureo de Paula & Elie Tamer, 2025. "Prediction Sets and Conformal Inference with Interval Outcomes," Papers 2501.10117, arXiv.org, revised Feb 2026.
    5. Chen, Qiang & Xiao, Zhijie & Yao, Qingsong, 2025. "Quantile control via random forest," Journal of Econometrics, Elsevier, vol. 249(PA).
    6. Jingsen Kong & Yiming Liu & Guangren Yang & Wang Zhou, 2025. "Conformal prediction for robust deep nonparametric regression," Statistical Papers, Springer, vol. 66(1), pages 1-36, January.
    7. Leying Guan, 2023. "Localized conformal prediction: a generalized inference framework for conformal prediction," Biometrika, Biometrika Trust, vol. 110(1), pages 33-50.

  34. Shuowen Chen & Victor Chernozhukov & Iv'an Fern'andez-Val, 2019. "Mastering Panel 'Metrics: Causal Impact of Democracy on Growth," Papers 1901.03821, arXiv.org.

    Cited by:

    1. Arindrajit Dube & Daniele Girardi & Òscar Jordà & Alan M. Taylor, 2023. "A Local Projections Approach to Difference-in-Differences Event Studies," Working Paper Series 2023-12, Federal Reserve Bank of San Francisco.
    2. Daniel Czarnowske & Amrei Stammann, 2020. "Inference in Unbalanced Panel Data Models with Interactive Fixed Effects," Papers 2004.03414, arXiv.org, revised Jun 2025.
    3. Hryshko, Dmytro & Manovskii, Iourii, 2022. "How much consumption insurance in the U.S.?," Journal of Monetary Economics, Elsevier, vol. 130(C), pages 17-33.
    4. Mountford, Andrew, 2022. "Economic Growth Analysis When Balanced Growth Paths May Be Time Varying," MPRA Paper 114249, University Library of Munich, Germany.
    5. Andrew Chia, 2021. "Automatically Differentiable Random Coefficient Logistic Demand Estimation," Papers 2106.04636, arXiv.org.
    6. Christoph Doerffel & Peter Draper & Andreas Freytag & Sebastian Schuhmann, 2021. "Drivers of Inclusive Development: An Empirical Investigation," Jena Economics Research Papers 2021-015, Friedrich-Schiller-University Jena.
    7. Ihsaan Bassier & Arindrajit Dube & Suresh Naidu, 2020. "Monopsony in Movers: The Elasticity of Labor Supply to Firm Wage Policies," NBER Working Papers 27755, National Bureau of Economic Research, Inc.
    8. Fontanari, Claudia, 2024. "The role of wages in triggering innovation and productivity: A dynamic exploration for European economies," Economic Modelling, Elsevier, vol. 130(C).
    9. Emanuele Amodio & Michele Battisti & Antonio Francesco Gravina & Andrea Mario Lavezzi & Giuseppe Maggio, 2022. "School-age Vaccination, School Openings and Covid-19 diffusion," Papers 2203.12331, arXiv.org.
    10. Chernozhukov, Victor & Kasahara, Hiroyuki & Schrimpf, Paul, 2021. "Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S," Journal of Econometrics, Elsevier, vol. 220(1), pages 23-62.

  35. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2019. "Inference on average treatment effects in aggregate panel data settings," CeMMAP working papers CWP32/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Masahiro Kato & Akari Ohda, 2023. "Asymptotically Unbiased Synthetic Control Methods by Moment Matching," Papers 2307.11127, arXiv.org, revised Nov 2025.
    2. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2019. "Synthetic Difference In Differences," NBER Working Papers 25532, National Bureau of Economic Research, Inc.

  36. Victor Chernozhukov & Iv'an Fern'andez-Val & Blaise Melly, 2019. "Fast Algorithms for the Quantile Regression Process," Papers 1909.05782, arXiv.org, revised Apr 2020.

    Cited by:

    1. Cuesta, Lizeth & Ruiz, Yomara, 2021. "Efecto de la globalización sobre la desigualdad. Un estudio global para 104 países usando regresiones cuantílicas [Effect of globalization on inequality. A global study for 104 countries using quantile regressions]," MPRA Paper 111022, University Library of Munich, Germany.
    2. Wang, Xiuhua & Wang, Yipeng & Zhao, Yaxiong, 2022. "Financial permeation and rural poverty reduction Nexus: Further insights from counties in China," China Economic Review, Elsevier, vol. 76(C).
    3. Ana María Iregui-Bohórquez & María Teresa Ramírez-Giraldo & Ligia Alba Melo-Becerra & Jorge Leonardo Rodríguez-Arenas, 2025. "De la cobertura al desempeño: Explorando las brechas de género en el sistema educativo colombiano," Borradores de Economia 1301, Banco de la Republica de Colombia.
    4. Ignacio Garr'on & C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "International vulnerability of inflation," Papers 2410.20628, arXiv.org, revised Oct 2024.
    5. Fernando Rios-Avila & Gustavo Canavire-Bacarreza & Flavia Sacco-Capurro, 2024. "Recovering income distribution in the presence of interval-censored data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 22(4), pages 1039-1060, December.
    6. EunYi Chung & Mauricio Olivares, 2025. "Quantile‐Based Test for Heterogeneous Treatment Effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(1), pages 3-17, January.
    7. Garrón Vedia, Ignacio & Rodríguez Caballero, Carlos Vladimir & Ruiz Ortega, Esther, 2024. "International vulnerability of inflation," DES - Working Papers. Statistics and Econometrics. WS 44814, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Borgen, Nicolai T. & Haupt, Andreas & Wiborg, Øyvind N., 2021. "Flexible and fast estimation of quantile treatment effects: The rqr and rqrplot commands," SocArXiv 4vquh, Center for Open Science.
    9. Santiago Pereda-Fern'andez, 2024. "Fast Algorithms for Quantile Regression with Selection," Papers 2402.16693, arXiv.org.
    10. Gloria González‐Rivera & C. Vladimir Rodríguez‐Caballero & Esther Ruiz, 2024. "Expecting the unexpected: Stressed scenarios for economic growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 926-942, August.
    11. Lee, Sokbae & Liao, Yuan & Seo, Myung Hwan & Shin, Youngki, 2025. "Fast inference for quantile regression with tens of millions of observations," Journal of Econometrics, Elsevier, vol. 249(PA).
    12. Rukundo, Emmanuel Nshakira & Schroeder, Sarah & Hisarciklilar, Mehtap & McKay, Andrew D., 2024. "Health insurance premium changes and labor supply: Evidence from a low-income country," Ruhr Economic Papers 1103, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    13. Qiang Chen & Zhijie Xiao, 2025. "Spread Regression, Skewness Regression, and Kurtosis Regression With an Application to the US Wage Structure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(3), pages 325-340, April.

  37. Alexandre Belloni & Federico A. Bugni & Victor Chernozhukov, 2019. "Subvector inference in PI models with many moment inequalities," CeMMAP working papers CWP28/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Victor Aguirregabiria & Allan Collard-Wexler & Stephen P. Ryan, 2021. "Dynamic Games in Empirical Industrial Organization," Papers 2109.01725, arXiv.org, revised Sep 2021.
    2. Marcoux, Mathieu & Russell, Thomas M. & Wan, Yuanyuan, 2024. "A simple specification test for models with many conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 242(1).
    3. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    4. Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.

  38. Arun Chandrasekhar & Victor Chernozhukov & Francesca Molinari & Paul Schrimpf, 2019. "Best linear approximations to set identified functions: with an application to the gender wage gap," CeMMAP working papers 09/19, Institute for Fiscal Studies.

    Cited by:

    1. Wunsch, Conny & Strittmatter, Anthony, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers 15840, C.E.P.R. Discussion Papers.
    2. , 2023. "Price Competition and Endogenous Product Choice in Networks: Evidence from the US airline Industry," Working Papers 950, Queen Mary University of London, School of Economics and Finance.

  39. Victor Chernozhukov & Whitney Newey & Vira Semenova, 2019. "Welfare Analysis in Dynamic Models," Papers 1908.09173, arXiv.org, revised May 2025.

    Cited by:

    1. Minkyung Kim & K. Sudhir & Kosuke Uetake, 2022. "A Structural Model of a Multitasking Salesforce: Incentives, Private Information, and Job Design," Management Science, INFORMS, vol. 68(6), pages 4602-4630, June.

  40. Shuowen Chen & Victor Chernozhukov & Iv'an Fern'andez-Val & Ye Luo, 2019. "SortedEffects: Sorted Causal Effects in R," Papers 1909.00836, arXiv.org, revised Nov 2019.

    Cited by:

    1. Diego Marino Fages, 2023. "Migration and trust: Evidence on assimilation from internal migrants," Discussion Papers 2023-08, Nottingham Interdisciplinary Centre for Economic and Political Research (NICEP).

  41. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2018. "Valid Simultaneous Inference in High-Dimensional Settings (with the hdm package for R)," Papers 1809.04951, arXiv.org.

    Cited by:

    1. Helmut Wasserbacher & Martin Spindler, 2024. "Credit Ratings: Heterogeneous Effect on Capital Structure," Papers 2406.18936, arXiv.org.
    2. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2022. "High-dimensional Data Bootstrap," Papers 2205.09691, arXiv.org.
    3. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    4. Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.

  42. Alexandre Belloni & Federico Bugni & Victor Chernozhukov, 2018. "Subvector Inference in Partially Identified Models with Many Moment Inequalities," Papers 1806.11466, arXiv.org.

    Cited by:

    1. Gafarov, Bulat, 2025. "Simple subvector inference on sharp identified set in affine models," Journal of Econometrics, Elsevier, vol. 249(PB).
    2. Chesher, Andrew & Kim, Dongwoo & Rosen, Adam M., 2023. "IV methods for Tobit models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1700-1724.
    3. Arie Beresteanu, 2020. "Quantile Regression with Interval Data," Working Paper 6899, Department of Economics, University of Pittsburgh.
    4. Moyu Liao, 2020. "Estimating Economic Models with Testable Assumptions: Theory and Applications," Papers 2002.10415, arXiv.org, revised Mar 2022.
    5. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    6. Isaiah Andrews & Jonathan Roth & Ariel Pakes, 2019. "Inference for Linear Conditional Moment Inequalities," NBER Working Papers 26374, National Bureau of Economic Research, Inc.
    7. Bei, Xinyue, 2024. "Local linearization based subvector inference in moment inequality models," Journal of Econometrics, Elsevier, vol. 238(1).

  43. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández-Val, 2018. "Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," NBER Working Papers 24678, National Bureau of Economic Research, Inc.

    Cited by:

    1. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    2. Abhijit Banerjee & Esther Duflo & Amy Finkelstein & Lawrence F. Katz & Benjamin A. Olken & Anja Sautmann, 2020. "In Praise of Moderation: Suggestions for the Scope and Use of Pre-Analysis Plans for RCTs in Economics," NBER Working Papers 26993, National Bureau of Economic Research, Inc.
    3. De Neve, Jan-Emmanuel & Imbert, Clement & Spinnewijn, Johannes & Tsankova, Teodora & Luts, Maarten, 2020. "How to Improve Tax Compliance? Evidence from Population-wide Experiments in Belgium," CAGE Online Working Paper Series 458, Competitive Advantage in the Global Economy (CAGE).
    4. Briole, Simon & Gurgand, Marc & Maurin, Eric & McNally, Sandra & Ruiz-Valenzuela, Jenifer & Santín, Daniel, 2022. "The Making of Civic Virtues: A School-Based Experiment in Three Countries," IZA Discussion Papers 15141, IZA Network @ LISER.
    5. Devoto, Florencia & Galasso, Emanuela & Beegle, Kathleen G. & Brodmann, Stefanie, 2024. "Women at Work : Evidence from a Randomized Experiment in Urban Djibouti," Policy Research Working Paper Series 10906, The World Bank.
    6. Ubfal, Diego Javier, 2024. "What Works in Supporting Women-Led Businesses ?," Policy Research Working Paper Series 10744, The World Bank.
    7. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    8. M. Adam & O. Bonnet & E. Fize & T. Loisel & M. Rault & L. Wilner, 2023. "How does fuel demand respond to price changes? Quasi-experimental evidence based on high-frequency data," Documents de Travail de l'Insee - INSEE Working Papers 2023-17, Institut National de la Statistique et des Etudes Economiques.
    9. Sendhil Mullainathan & Ashesh Rambachan, 2024. "From Predictive Algorithms to Automatic Generation of Anomalies," Papers 2404.10111, arXiv.org, revised Sep 2025.
    10. Dmitry Arkhangelsky & Kazuharu Yanagimoto & Tom Zohar, 2024. "On Causal Inference with Model-Based Outcomes," Papers 2403.19563, arXiv.org, revised Jan 2026.
    11. Emily Breza & Arun G. Chandrasekhar & Davide Viviano, 2025. "Generalizability with ignorance in mind: learning what we do (not) know for archetypes discovery," Papers 2501.13355, arXiv.org, revised Jul 2025.
    12. Patrick Bajari & Zhihao Cen & Victor Chernozhukov & Manoj Manukonda & Jin Wang & Ramon Huerta & Junbo Li & Ling Leng & George Monokroussos & Suhas Vijaykunar & Shan Wan, 2023. "Hedonic prices and quality adjusted price indices powered by AI," CeMMAP working papers 08/23, Institute for Fiscal Studies.
    13. Stefano Piasenti & Marica Valente & Roel van Veldhuizen & Gregor Pfeifer & Gregor-Gabriel Pfeifer, 2023. "Does Unfairness Hurt Women? The Effects of Losing Unfair Competitions," CESifo Working Paper Series 10572, CESifo.
    14. Gallego, Francisco A. & Malamud, Ofer & Pop-Eleches, Cristian, 2020. "Parental monitoring and children's internet use: The role of information, control, and cues," Journal of Public Economics, Elsevier, vol. 188(C).
    15. Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," BIS Working Papers 1122, Bank for International Settlements.
    16. Susan Athey & Katy Ann Bergstrom & Vitor Hadad & Julian C. Jamison & Berk Özler & Luca Parisotto & Julius Dohbit Sama, 2021. "Shared Decision-Making: Can Improved Counseling Increase Willingness to Pay for Modern Contraceptives?," Discussion Papers 2105, University of Exeter, Department of Economics.
    17. Elek, Péter & Bíró, Anikó, 2021. "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, vol. 40(C).
    18. Simon Briole & Augustin Colette & Emmanuelle Lavaine, 2023. "The Heterogeneous Effects of Lockdown Policies on Air Pollution," CEE-M Working Papers hal-04084912, CEE-M, Universtiy of Montpellier, CNRS, INRA, Montpellier SupAgro.
    19. Robin Gubela & Artem Bequé & Stefan Lessmann & Fabian Gebert, 2019. "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling Strategies," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 747-791, May.
    20. Michela Carlana & Eliana La Ferrara, 2025. "Apart but Connected: Online Tutoring, Cognitive Outcomes, and Soft Skills," American Economic Review, American Economic Association, vol. 115(10), pages 3487-3513, October.
    21. Petru Crudu, 2023. "Long-term effects of early adverse labour market conditions: A Causal Machine Learning approach," Working Papers 2023:21, Department of Economics, University of Venice "Ca' Foscari".
    22. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
    23. Keshav Agrawal & Susan Athey & Ayush Kanodia & Emil Palikot, 2022. "Personalized Recommendations in EdTech: Evidence from a Randomized Controlled Trial," Papers 2208.13940, arXiv.org, revised Dec 2022.
    24. Odran Bonnet & Étienne Fize & Tristan Loisel & Lionel Wilner, 2024. "Compensating against fuel price inflation: Price subsidies or transfers?," PSE-Ecole d'économie de Paris (Postprint) hal-04799412, HAL.
    25. Axenbeck, Janna & Berner, Anne & Kneib, Thomas, 2022. "What drives the relationship between digitalization and industrial energy demand? Exploring firm-level heterogeneity," ZEW Discussion Papers 22-059, ZEW - Leibniz Centre for European Economic Research.
    26. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    27. Pedro Forquesato, 2022. "Who Benefits from Political Connections in Brazilian Municipalities," Papers 2204.09450, arXiv.org.
    28. Conner Mullally & Mayra Rivas & Travis McArthur, 2021. "Using Machine Learning to Estimate the Heterogeneous Effects of Livestock Transfers," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1058-1081, May.
    29. Benjamin A. Olken, 2020. "Banerjee, Duflo, Kremer, and the Rise of Modern Development Economics," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(3), pages 853-878, July.
    30. Ta-Wei Huang & Eva Ascarza, 2024. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals," Marketing Science, INFORMS, vol. 43(4), pages 863-884, July.
    31. Aparajithan Venkateswaran & Anirudh Sankar & Arun G. Chandrasekhar & Tyler H. McCormick, 2024. "Robustly estimating heterogeneity in factorial data using Rashomon Partitions," Papers 2404.02141, arXiv.org, revised Aug 2025.
    32. Jeffrey G. Shrader & Laura Bakkensen & Derek Lemoine, 2023. "Fatal Errors: The Mortality Value of Accurate Weather Forecasts," Working Papers 23-30, Center for Economic Studies, U.S. Census Bureau.
    33. Sandrine Mesplé-Somps and & Björn Nilsson, 2020. "Role models and migration intentions," Working Paper 519bfbde-8d2e-4e86-bd62-0, Agence française de développement.
    34. Muller, Seán M., 2020. "The implications of a fundamental contradiction in advocating randomized trials for policy," World Development, Elsevier, vol. 127(C).
    35. Xunkang Tian, 2025. "Treatment Effects of Multi-Valued Treatments in Hyper-Rectangle Model," Papers 2509.05177, arXiv.org.
    36. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
    37. Martin Kroczek & Philipp Kugler, 2022. "Heterogeneous Effects of Monetary and Non-Monetary Job Characteristics on Job Attractiveness in Nursing," IAW Discussion Papers 139, Institut für Angewandte Wirtschaftsforschung (IAW).
    38. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    39. Mullally, Conner & Rivas, Mayra & McArthur, Travis, 2019. "Livestock Transfers Can Improve Child Health: Evidence from a Randomized Phase-In Design in Guatemala," SocArXiv c6zg5, Center for Open Science.
    40. A. Stefano Caria & Grant Gordon & Maximilian Kasy & Simon Quinn & Soha Shami & Alexander Teytelboym, 2020. "An Adaptive Targeted Field Experiment: Job Search Assistance for Refugees in Jordan," CSAE Working Paper Series 2020-20, Centre for the Study of African Economies, University of Oxford.
    41. Yingheng Zhang & Haojie Li & Gang Ren, 2025. "Data-driven exploration of heterogeneous gasoline price elasticities using generalized random forests," Transportation, Springer, vol. 52(1), pages 215-237, February.
    42. Kroczek, Martin & Kugler, Philipp, 2022. "Heterogeneous Effects of Monetary and Non-Monetary Job Characteristics on Job Attractiveness in Nursing," VfS Annual Conference 2022 (Basel): Big Data in Economics 264108, Verein für Socialpolitik / German Economic Association.
    43. Markus Eyting, 2020. "A Random Forest a Day Keeps the Doctor Away," Working Papers 2026, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    44. David Glynn & John Giardina & Julia Hatamyar & Ankur Pandya & Marta Soares & Noemi Kreif, 2024. "Integrating decision modeling and machine learning to inform treatment stratification," Health Economics, John Wiley & Sons, Ltd., vol. 33(8), pages 1772-1792, August.
    45. David M. Ritzwoller & Joseph P. Romano, 2023. "Reproducible Aggregation of Sample-Split Statistics," Papers 2311.14204, arXiv.org, revised Nov 2024.
    46. Emerick, Kyle & Kelley, Erin & De Janvry, Alain & Sadoulet, Elisabeth, 2019. "Endogenous Information Sharing and the Gains from Using Network Information to Maximize Technology Adoption," CEPR Discussion Papers 13507, C.E.P.R. Discussion Papers.
    47. Yanqing Yang & Nan Zhang & Jinfeng Ge & Yan Xu, 2025. "Sino-US S and T Frictions and Transnational Knowledge Flows: Evidence from machine learning and cross-national patent data," Papers 2503.21822, arXiv.org.
    48. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    49. Cassidy, Rachel & Ebrahim, Menaal Fatima & Ubfal, Diego Javier, 2024. "Testing the Promise of Digital Scaling : In-Person versus App-Based Training for Women Entrepreneurs," Policy Research Working Paper Series 10992, The World Bank.
    50. Minahil Asim & Ronak Jain & Vatsal Khandelwal, 2024. "Great expectations? Experimental evidence from schools in Pakistan," ECON - Working Papers 454, Department of Economics - University of Zurich.
    51. Nathan Kallus, 2022. "Treatment Effect Risk: Bounds and Inference," Papers 2201.05893, arXiv.org, revised Jul 2022.
    52. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    53. Vishalie Shah & Julia Hatamyar & Taufik Hidayat & Noemi Kreif, 2025. "Exploring the heterogeneous impacts of Indonesia's conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests," Papers 2501.12803, arXiv.org.
    54. Galiani, Sebastian & Quistorff, Brian, 2024. "Assessing external validity in practice," Research in Economics, Elsevier, vol. 78(3).
    55. World Bank, 2021. "Promoting Women Entrepreneurs in Mexico Report," World Bank Publications - Reports 37323, The World Bank Group.
    56. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    57. Petru Crudu & Giacomo Pasini, 2023. "The Health Burden of Job Strain: Evidence from Europe," Working Papers 2023:19, Department of Economics, University of Venice "Ca' Foscari".
    58. Strittmatter, Anthony, 2019. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," GLO Discussion Paper Series 336, Global Labor Organization (GLO).
    59. Abhijit Banerjee & Emily Breza & Esther Duflo & Cynthia Kinnan, 2019. "Can Microfinance Unlock a Poverty Trap for Some Entrepreneurs?," NBER Working Papers 26346, National Bureau of Economic Research, Inc.
    60. Chaisemartin, Clement de & Navarrete, Nicolas, 2019. "The direct and spillover effects of a mental health program for disruptive students," CAGE Online Working Paper Series 401, Competitive Advantage in the Global Economy (CAGE).
    61. Taiyo Fukai & Hidehiko Ichimura & Keisuke Kawata, 2021. "Describing the impacts of COVID-19 on the labor market in Japan until June 2020," The Japanese Economic Review, Springer, vol. 72(3), pages 439-470, July.
    62. Jonas Hjort & Golvine de Rochambeau & Vinayak Iyer & Fei Ao, 2020. "Informational Barriers to Market Access: Experimental Evidence from Liberian Firms," NBER Working Papers 27662, National Bureau of Economic Research, Inc.
    63. Maria Nareklishvili & Nicholas Polson & Vadim Sokolov, 2022. "Feature Selection for Personalized Policy Analysis," Papers 2301.00251, arXiv.org, revised Jul 2023.
    64. Lily Davies & Mark Kattenberg & Benedikt Vogt, 2023. "Predicting Firm Exits with Machine Learning: Implications for Selection into COVID-19 Support and Productivity Growth," CPB Discussion Paper 444, CPB Netherlands Bureau for Economic Policy Analysis.
    65. Smith, Jeffrey A., 2022. "Treatment Effect Heterogeneity," IZA Discussion Papers 15151, IZA Network @ LISER.
    66. Dorothee Weiffen & Ghassan Baliki & Tilman Brück, 2022. "Violent conflict moderates food security impacts of agricultural asset transfers in Syria: A heterogeneity analysis using machine learning," HiCN Working Papers 381, Households in Conflict Network.
    67. Kyle Myers & Wei Yang Tham, 2023. "Money, Time, and Grant Design," Papers 2312.06479, arXiv.org.
    68. Kretschmer, Sandra, 2024. "Carbon literacy – Can simple interventions help? Effect of information provision on emissions knowledge of private households," Energy Policy, Elsevier, vol. 188(C).

  44. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.

    Cited by:

    1. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness," Papers 2010.08463, arXiv.org, revised Nov 2025.
    2. Victor Chernozhukov & Chen Huang & Weining Wang, 2021. "Uniform Inference on High-dimensional Spatial Panel Networks," Papers 2105.07424, arXiv.org, revised Jul 2025.
    3. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    4. Manu Navjeevan, 2023. "An Identification and Dimensionality Robust Test for Instrumental Variables Models," Papers 2311.14892, arXiv.org, revised Dec 2024.
    5. Saulius Jokubaitis & Remigijus Leipus, 2022. "Asymptotic Normality in Linear Regression with Approximately Sparse Structure," Mathematics, MDPI, vol. 10(10), pages 1-28, May.
    6. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2019. "Improved Central Limit Theorem and bootstrap approximations in high dimensions," Papers 1912.10529, arXiv.org, revised May 2022.
    7. Baris Ata & Alexandre Belloni & Ozan Candogan, 2018. "Latent Agents in Networks: Estimation and Targeting," Papers 1808.04878, arXiv.org, revised Jan 2022.
    8. Andrii Babii, 2020. "High-dimensional mixed-frequency IV regression," Papers 2003.13478, arXiv.org.
    9. Yoichi Arai & Taisuke Otsu & Myung Hwan Seo, 2021. "Regression Discontinuity Design with Potentially Many Covariates," Papers 2109.08351, arXiv.org, revised Feb 2024.
    10. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    11. Lee, Seong-ho & Ma, Yanyuan & de Luna, Xavier, 2025. "Covariate balancing for causal inference on categorical and continuous treatments," Econometrics and Statistics, Elsevier, vol. 33(C), pages 304-329.
    12. Masayuki Sawada & Kohei Kawaguchi, 2020. "Estimating High-Dimensional Discrete Choice Model of Differentiated Products with Random Coefficients," Papers 2004.08791, arXiv.org.
    13. Denis Chetverikov & Daniel Wilhelm & Dongwoo Kim, 2019. "An adaptive test of stochastic monotonicity," CeMMAP working papers CWP49/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    15. Man Fung Leung & Kin Wai Chan & Xiaofeng Shao, 2025. "Online Generalized Method of Moments for Time Series," Papers 2502.00751, arXiv.org.
    16. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-50, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    17. Adam Baybutt & Manu Navjeevan, 2023. "Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls," Papers 2301.06283, arXiv.org.
    18. Ouyang, Fu & Yang, Thomas T., 2025. "High dimensional binary choice model with unknown heteroskedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 251(C).
    19. Xinwei Ma & Jingshen Wang, 2018. "Robust Inference Using Inverse Probability Weighting," Papers 1810.11397, arXiv.org, revised May 2019.
    20. Dmitry Arkhangelsky & Vasily Korovkin, 2020. "On Policy Evaluation with Aggregate Time-Series Shocks," CERGE-EI Working Papers wp657, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

  45. Xi Chen & Victor Chernozhukov & Iv'an Fern'andez-Val & Scott Kostyshak & Ye Luo, 2018. "Shape-Enforcing Operators for Point and Interval Estimators," Papers 1809.01038, arXiv.org, revised Feb 2021.

    Cited by:

    1. Zheng Fang & Juwon Seo, 2021. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Econometrica, Econometric Society, vol. 89(5), pages 2439-2458, September.
    2. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    3. Harold D. Chiang & Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs," Papers 2102.06586, arXiv.org.
    4. Guido Imbens, 2019. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," NBER Working Papers 26104, National Bureau of Economic Research, Inc.

  46. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "Debiasing and $t$-tests for synthetic control inference on average causal effects," Papers 1812.10820, arXiv.org, revised May 2025.

    Cited by:

    1. Billy Ferguson & Brad Ross, 2020. "Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error," Papers 2012.15367, arXiv.org, revised Feb 2021.
    2. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    3. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    4. Guido Imbens & Nathan Kallus & Xiaojie Mao, 2021. "Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models," Papers 2108.03849, arXiv.org.
    5. Erick Lahura & Rosario Sabrera, 2023. "The effect of infrastructure investment on tourism demand: a synthetic control approach for the case of Kuelap, Peru," Empirical Economics, Springer, vol. 65(1), pages 443-478, July.
    6. Nicolaj S{o}ndergaard Muhlbach & Mikkel Slot Nielsen, 2019. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," Papers 1909.03968, arXiv.org, revised Feb 2021.
    7. Taehyeon Koo & Zijian Guo, 2025. "Distributionally Robust Synthetic Control: Ensuring Robustness Against Highly Correlated Controls and Weight Shifts," Papers 2511.02632, arXiv.org, revised Jan 2026.
    8. Fry, Joseph, 2024. "A method of moments approach to asymptotically unbiased Synthetic Controls," Journal of Econometrics, Elsevier, vol. 244(1).
    9. Luis Alvarez & Bruno Ferman, 2025. "On the relationship between prediction intervals, tests of sharp nulls and inference on realized treatment effects in settings with few treated units," Papers 2506.14998, arXiv.org.
    10. Anish Agarwal & Rahul Singh, 2021. "Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy," Papers 2107.02780, arXiv.org, revised Feb 2024.
    11. Jianfei Cao & Shirley Lu & Hang Wu, 2019. "Synthetic Control Inference for Staggered Adoption," Papers 1912.06320, arXiv.org, revised Nov 2025.
    12. Xingyu Li & Yan Shen & Qiankun Zhou, 2022. "Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects," Papers 2202.12078, arXiv.org.
    13. Bruno Ferman & Cristine Pinto, 2019. "Synthetic Controls with Imperfect Pre-Treatment Fit," Papers 1911.08521, arXiv.org, revised Jan 2021.

  47. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2018. "Exact and robust conformal inference methods for predictive machine learning with dependent data," CeMMAP working papers CWP16/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Fantazzini, Dean, 2024. "Adaptive Conformal Inference for computing Market Risk Measures: an Analysis with Four Thousands Crypto-Assets," MPRA Paper 121214, University Library of Munich, Germany.
    2. Ajroldi, Niccolò & Diquigiovanni, Jacopo & Fontana, Matteo & Vantini, Simone, 2023. "Conformal prediction bands for two-dimensional functional time series," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    3. Borgschulte, Mark & Vogler, Jacob, 2020. "Did the ACA Medicaid expansion save lives?," Journal of Health Economics, Elsevier, vol. 72(C).
    4. Federico A. Bugni & Jia Li & Qiyuan Li, 2023. "Permutation‐based tests for discontinuities in event studies," Quantitative Economics, Econometric Society, vol. 14(1), pages 37-70, January.
    5. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    6. Goulet Coulombe, Philippe, 2025. "Time-varying parameters as ridge regressions," International Journal of Forecasting, Elsevier, vol. 41(3), pages 982-1002.
    7. Matteo Fontana & Gianluca Zeni & Simone Vantini, 2020. "Conformal Prediction: a Unified Review of Theory and New Challenges," Papers 2005.07972, arXiv.org, revised Jul 2022.
    8. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2019. "Distributional conformal prediction," Papers 1909.07889, arXiv.org, revised Aug 2021.
    9. Varun Gupta & Christopher Jung & Georgy Noarov & Mallesh M. Pai & Aaron Roth, 2021. "Online Multivalid Learning: Means, Moments, and Prediction Intervals," Papers 2101.01739, arXiv.org.

  48. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2018. "The Impact of Big Data on Firm Performance: An Empirical Investigation," NBER Working Papers 24334, National Bureau of Economic Research, Inc.

    Cited by:

    1. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
    2. Elena Argentesi & Paolo Buccirossi & Emilio Calvano & Tomaso Duso & Alessia Marrazzo & Salvatore Nava, 2019. "Merger Policy in Digital Markets: An Ex-Post Assessment," CESifo Working Paper Series 7985, CESifo.
    3. Hamid Etemad, 2024. "Transformative potentials of generative artificial intelligence: Should international entrepreneurial enterprises adopt GEN.AI?," Journal of International Entrepreneurship, Springer, vol. 22(2), pages 141-163, June.
    4. Victor Aguirregabiria & Jihye Jeon, 2020. "Firms’ Beliefs and Learning: Models, Identification, and Empirical Evidence," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 56(2), pages 203-235, March.
    5. Huang, Qiongyu & Xu, Chuhong & Xue, Xiaolong & Zhu, Hui, 2023. "Can digital innovation improve firm performance: Evidence from digital patents of Chinese listed firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
    6. Yiquan Gu & Leonardo Madio & Carlo Reggiani, 2021. "Data Brokers Co-Opetition," Working Papers 202101, University of Liverpool, Department of Economics.
    7. Bo Cowgill, 2019. "Bias and Productivity in Humans and Machines," Upjohn Working Papers 19-309, W.E. Upjohn Institute for Employment Research.
    8. Schaefer, Maximilian & Sapi, Geza & Lorincz, Szabolcs, 2018. "The effect of big data on recommendation quality: The example of internet search," DICE Discussion Papers 284, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    9. Catherine Tucker, 2019. "Digital Data, Platforms and the Usual [Antitrust] Suspects: Network Effects, Switching Costs, Essential Facility," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 54(4), pages 683-694, June.
    10. Wang, Jiaxin & Zhao, Mu & Huang, Xiang & Song, Zilong & Sun, Di, 2024. "Supply chain diffusion mechanisms for AI applications: A perspective on audit pricing," International Review of Financial Analysis, Elsevier, vol. 93(C).
    11. Helmut Wasserbacher & Martin Spindler, 2021. "Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls," Papers 2107.04851, arXiv.org.
    12. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    13. Gillian K. Hadfield & Andrew Koh, 2025. "An Economy of AI Agents," Papers 2509.01063, arXiv.org.
    14. Hyunjin Kim & Edward L. Glaeser & Andrew Hillis & Scott Duke Kominers & Michael Luca, 2024. "Decision authority and the returns to algorithms," Strategic Management Journal, Wiley Blackwell, vol. 45(4), pages 619-648, April.
    15. Guy Aridor & Yeon-Koo Che & Tobias Salz, 2020. "The Effect of Privacy Regulation on the Data Industry: Empirical Evidence from GDPR," NBER Working Papers 26900, National Bureau of Economic Research, Inc.
    16. Carballa-Smichowski, Bruno & Lefouili, Yassine & Mantovani, Andrea & Reggiani, Carlo, 2025. "Data Sharing or Analytics Sharing?," TSE Working Papers 25-1615, Toulouse School of Economics (TSE).
    17. Maximilian Schaefer, 2025. "When Should we Expect Non-Decreasing Returns from Data in Prediction Tasks?," Papers 2503.03602, arXiv.org.
    18. Anderton, Robert & Jarvis, Valerie & Labhard, Vincent & Morgan, Julian & Petroulakis, Filippos & Vivian, Lara, 2020. "Virtually everywhere? Digitalisation and the euro area and EU economies," Occasional Paper Series 244, European Central Bank.
    19. Charles B. Weinberg & Cord Otten & Barak Orbach & Jordi McKenzie & Ricard Gil & Darlene C. Chisholm & Suman Basuroy, 2021. "Technological change and managerial challenges in the movie theater industry," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(2), pages 239-262, June.
    20. Charles I. Jones & Christopher Tonetti, 2019. "Nonrivalry and the Economics of Data," NBER Working Papers 26260, National Bureau of Economic Research, Inc.
    21. Marie-Pierre Dargnies & Rustamdjan Hakimov & Dorothea Kübler, 2022. "Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence," CESifo Working Paper Series 9968, CESifo.
    22. Graef, Inge & Prüfer, Jens, 2021. "Governance of data sharing : A law & economics proposal," Other publications TiSEM 4d057c2c-2945-4915-8fe1-4, Tilburg University, School of Economics and Management.
    23. Ehsan Valavi & Joel Hestness & Newsha Ardalani & Marco Iansiti, 2022. "Time and the Value of Data," Papers 2203.09118, arXiv.org.
    24. Norbäck, Pehr-Johan & Persson, Lars, 2023. "Why Big Data Can Make Creative Destruction More Creative – But Less Destructive," Working Paper Series 1454, Research Institute of Industrial Economics.
    25. Koski, Heli & Kässi, Otto & Braesemann, Fabian, 2020. "Killers on the Road of Emerging Start-ups – Implications for Market Entry and Venture Capital Financing," ETLA Working Papers 81, The Research Institute of the Finnish Economy.
    26. Dirk Bergemann & Marco Ottaviani, 2021. "Information Markets and Nonmarkets," Cowles Foundation Discussion Papers 2296, Cowles Foundation for Research in Economics, Yale University.
    27. Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.
    28. Carl-Christian Groh, 2024. "Big Data and Inequality," CRC TR 224 Discussion Paper Series crctr224_2024_555, University of Bonn and University of Mannheim, Germany.
    29. Erik Brynjolfsson & Wang Jin & Kristina McElheran, 2021. "The power of prediction: predictive analytics, workplace complements, and business performance," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 217-239, October.
    30. Gillian K. Hadfield & Andrew Koh, 2025. "An Economy of AI Agents," NBER Chapters, in: The Economics of Transformative AI, National Bureau of Economic Research, Inc.
    31. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
    32. Kwon, Seokbeom & Porter, Alan L., 2025. "Use of exclusive data for corporate research on machine learning and artificial intelligence: Implications for innovation and competition policy," Technology in Society, Elsevier, vol. 81(C).
    33. MARTENS Bertin, 2020. "An economic perspective on data and platform market power," JRC Working Papers on Digital Economy 2020-09, Joint Research Centre.
    34. Fossen, Frank M. & McLemore, Trevor & Sorgner, Alina, 2024. "Artificial Intelligence and Entrepreneurship," IZA Discussion Papers 17055, IZA Network @ LISER.
    35. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
    36. Guy Aridor & Yishay Mansour & Aleksandrs Slivkins & Zhiwei Steven Wu, 2020. "Competing Bandits: The Perils of Exploration Under Competition," Papers 2007.10144, arXiv.org, revised Oct 2024.
    37. Elisa Rodepeter & Christoph Gschnaidtner & Hanna Hottenrott, 2024. "Big Data and Start-up Performance," Working Papers 232, Bavarian Graduate Program in Economics (BGPE).
    38. Kesler, Reinhold & Kummer, Michael E. & Schulte, Patrick, 2019. "Competition and privacy in online markets: Evidence from the mobile app industry," ZEW Discussion Papers 19-064, ZEW - Leibniz Centre for European Economic Research.
    39. Simon Martin & Wolfgang Benedikt Schmal, 2021. "Collusive Compensation Schemes Aided by Algorithms," CESifo Working Paper Series 9481, CESifo.
    40. Calvano, Emilio & Polo, Michele, 2020. "Market Power, Competition and Innovation in digital markets: a survey," CEPR Discussion Papers 14314, C.E.P.R. Discussion Papers.
    41. Pehr-Johan Norbäck & Lars Persson, 2024. "Why generative AI can make creative destruction more creative but less destructive," Small Business Economics, Springer, vol. 63(1), pages 349-377, June.
    42. Li, Yuetong & Wang, Xinyi & Zheng, Xiaojia, 2024. "Data assets and corporate sustainable development: evidence from ESG in China," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    43. Yang, Gangqiang & Nie, Yiming & Li, Honggui & Wang, Haisen, 2023. "Digital transformation and low-carbon technology innovation in manufacturing firms: The mediating role of dynamic capabilities," International Journal of Production Economics, Elsevier, vol. 263(C).
    44. Schaefer, Maximilian & Sapi, Geza, 2023. "Complementarities in learning from data: Insights from general search," Information Economics and Policy, Elsevier, vol. 65(C).
    45. Bertin Martens, 2024. "The Impact of EU Data Regulations on Innovation, Competitiveness, and Growth: How Can Their Quality and Capability Be Improved?," EconPol Forum, CESifo, vol. 25(06), pages 13-17, November.
    46. Maximilian Schäfer & Geza Sapi, 2020. "Learning from Data and Network Effects: The Example of Internet Search," Discussion Papers of DIW Berlin 1894, DIW Berlin, German Institute for Economic Research.
    47. Zhao, Shuang & Guan, Yunfang & Zhou, Haiyan & Hu, Feng, 2024. "Making digital technology innovation happen: The role of the CEO's information technology backgrounds," Economic Modelling, Elsevier, vol. 140(C).
    48. Gao, Dengyun & Liu, Chang & Sun, Zhanwei, 2025. "Data elements and corporate innovation: A discussion of corporate innovation strategy," Finance Research Letters, Elsevier, vol. 76(C).
    49. Federico Etro, 2021. "Product selection in online marketplaces," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 30(3), pages 614-637, August.
    50. Gaspar, José-Miguel & Wang, Sumingyue & Xu, Liang, 2024. "Digitalization and the performance of non-technological firms: Evidence from the COVID-19 and natural disaster shocks," Journal of Corporate Finance, Elsevier, vol. 89(C).

  49. Victor Chernozhukov & Alfred Galichon & Marc Henry & Brendan Pass, 2018. "Single Market Nonparametric Identification of Multi-Attribute Hedonic Equilibrium Models," Sciences Po Economics Publications (main) hal-01169655, HAL.

    Cited by:

    1. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    2. Pierre-André Chiappori & Bernard Salanié, 2016. "The Econometrics of Matching Models," Journal of Economic Literature, American Economic Association, vol. 54(3), pages 832-861, September.
    3. Odran Bonnet & Alfred Galichon & Yu-Wei Hsieh & Keith O'Hara & Matt Shum, 2021. "Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching," Papers 2111.13744, arXiv.org.
    4. Florian Gunsilius, 2018. "Point-identification in multivariate nonseparable triangular models," Papers 1806.09680, arXiv.org.
    5. Dizdar, Deniz & Moldovanu, Benny, 2016. "On the importance of uniform sharing rules for efficient matching," Journal of Economic Theory, Elsevier, vol. 165(C), pages 106-123.

  50. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Adel Daoud, 2021. "The International Monetary Funds intervention in education systems and its impact on childrens chances of completing school," Papers 2201.00013, arXiv.org.
    3. Daoud, Adel & Johansson, Fredrik, 2019. "Estimating Treatment Heterogeneity of International Monetary Fund Programs on Child Poverty with Generalized Random Forest," SocArXiv awfjt, Center for Open Science.
    4. Yiyan Huang & Cheuk Hang Leung & Xing Yan & Qi Wu & Nanbo Peng & Dongdong Wang & Zhixiang Huang, 2020. "The Causal Learning of Retail Delinquency," Papers 2012.09448, arXiv.org.
    5. Victor Chernozhukov & Jerry A. Hausman & Whitney K. Newey, 2019. "Demand Analysis with Many Prices," NBER Working Papers 26424, National Bureau of Economic Research, Inc.
    6. Daoud, Adel, 2021. "The International Monetary Fund’s intervention in education systems and its impact on children’s chances of completing school," SocArXiv kbc34, Center for Open Science.
    7. Jann Spiess & Vasilis Syrgkanis & Victor Yaneng Wang, 2021. "Finding Subgroups with Significant Treatment Effects," Papers 2103.07066, arXiv.org, revised Dec 2023.
    8. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2018. "Locally robust semiparametric estimation," CeMMAP working papers CWP30/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Yang Cai & Alkis Kalavasis & Katerina Mamali & Anay Mehrotra & Manolis Zampetakis, 2025. "What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness," Papers 2506.04194, arXiv.org, revised Jun 2025.

  51. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    2. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    3. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
    4. Manisha Shah & Sarah Baird & Jennifer Seager & Benjamin Avuwadah & Joan Hamory & Shwetlena Sabarwal & Amita Vyas, 2024. "Improving Mental Health of Adolescent Girls in Low- and Middle-Income Countries: Causal Evidence from Life Skills Programming," Journal of Human Resources, University of Wisconsin Press, vol. 59(S), pages 317-364.
    5. Jacob, Daniel & Härdle, Wolfgang Karl & Lessmann, Stefan, 2019. "Group Average Treatment Effects for Observational Studies," IRTG 1792 Discussion Papers 2019-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  52. Victor Chernozhukov & Iv'an Fern'andez-Val & Siyi Luo, 2018. "Distribution Regression with Sample Selection, with an Application to Wage Decompositions in the UK," Papers 1811.11603, arXiv.org, revised Dec 2023.

    Cited by:

    1. Anthony Strittmatter & Conny Wunsch, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," Papers 2102.09207, arXiv.org, revised Feb 2021.
    2. Chernozhukov, Victor & Fernández-Val, Iván & Meier, Jonas & van Vuuren, Aico & Vella, Francis, 2025. "Bivariate Distribution Regression; Theory, Estimation and an Application to Intergenerational Mobility," IZA Discussion Papers 18091, IZA Network @ LISER.
    3. Chen, Songnian & Liu, Nianqing & Zhang, Hanghui, 2025. "Distribution regression with censored selection," Journal of Econometrics, Elsevier, vol. 251(C).
    4. Liu, Ruixuan & Yu, Zhengfei, 2022. "Sample selection models with monotone control functions," Journal of Econometrics, Elsevier, vol. 226(2), pages 321-342.
    5. Miguel A. Delgado & Andr'es Garc'ia-Suaza & Pedro H. C. Sant'Anna, 2019. "Distribution Regression in Duration Analysis: an Application to Unemployment Spells," Papers 1904.06185, arXiv.org, revised Nov 2021.
    6. Dominik Wied, 2022. "Semiparametric Distribution Regression with Instruments and Monotonicity," Papers 2212.03704, arXiv.org.

  53. Victor Chernozhukov & Iv'an Fern'andez-Val & Sukjin Han & Amanda Kowalski, 2018. "Censored Quantile Instrumental Variable Estimation with Stata," Papers 1801.05305, arXiv.org, revised Sep 2019.

    Cited by:

    1. Young-Joo Kim & Vincent Daly, 2019. "The Education Gradient in Health: The Case of Obesity in the UK and US," Economics Discussion Papers 2019-4, School of Economics, Kingston University London.
    2. L. Benfratello & A. Bottasso & C. Piccardo, 2022. "R&D and export performance: exploring heterogeneity along the export intensity distribution," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(2), pages 189-232, June.
    3. Männasoo, Kadri, 2022. "Working hours and gender wage differentials: Evidence from the American Working Conditions Survey," Labour Economics, Elsevier, vol. 76(C).
    4. Kushawaha, Deepak, 2025. "Understanding the role of greenfield and mergers & acquisitions foreign direct investments in renewable energy expansion in developing countries," Energy Economics, Elsevier, vol. 145(C).
    5. David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.
    6. Sanna Nivakoski, 2020. "Wealth and the effect of subjective survival probability," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(2), pages 633-670, April.
    7. Heboyan, Vahé & Hovhannisyan, Vardges & Bakhtavoryan, Rafael, 2023. "A Comprehensive Analysis of Tobacco Control Policies within a Smoothed Instrumental Variables Quantile Regression Framework," 2023 Annual Meeting, July 23-25, Washington D.C. 335614, Agricultural and Applied Economics Association.
    8. Abe Dunn, 2014. "Health Insurance and the Demand for Medical Care: Instrumental Variable Estimates Using Health Insurer Claims Data," BEA Working Papers 0107, Bureau of Economic Analysis.
    9. Sugimoto, Kota, 2021. "Ownership versus legal unbundling of electricity transmission network: Evidence from renewable energy investment in Germany," Energy Economics, Elsevier, vol. 99(C).

  54. Victor Chernozhukov & Denis Nekipelov & Vira Semenova & Vasilis Syrgkanis, 2018. "Plug-in regularized estimation of high dimensional parameters in nonlinear semiparametric models," CeMMAP working papers CWP41/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Uyanga Byambaa & Edward Miguel & Michael W. Walker & Samuel Zicheng Wang, 2025. "Beyond Education and Occupation: Unpacking the Large Gender Wage Gap in Kenya," NBER Working Papers 34375, National Bureau of Economic Research, Inc.
    2. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    3. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    4. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
    5. Jann Spiess & Vasilis Syrgkanis & Victor Yaneng Wang, 2021. "Finding Subgroups with Significant Treatment Effects," Papers 2103.07066, arXiv.org, revised Dec 2023.

  55. Denis Nekipelov & Vira Semenova & Vasilis Syrgkanis, 2018. "Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models," Papers 1806.04823, arXiv.org, revised Sep 2021.

    Cited by:

    1. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    2. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
    3. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.

  56. Victor Chernozhukov & Whitney Newey & Rahul Singh, 2018. "De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers," Papers 1802.08667, arXiv.org, revised Oct 2022.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    3. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org, revised Mar 2025.
    4. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Ben Deaner & Chen-Wei Hsiang & Andrei Zeleneev, 2025. "Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities," Papers 2503.20769, arXiv.org, revised Mar 2025.
    6. Cory McCartan & Shiro Kuriwaki, 2025. "Identification and Semiparametric Estimation of Conditional Means from Aggregate Data," Papers 2509.20194, arXiv.org.
    7. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    8. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2025. "Robust Semiparametric Inference for Bayesian Additive Regression Trees," Papers 2509.24634, arXiv.org, revised Oct 2025.
    9. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    10. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
    11. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    12. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2018. "Locally robust semiparametric estimation," CeMMAP working papers CWP30/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Antonio R. Linero, 2023. "Prior and posterior checking of implicit causal assumptions," Biometrics, The International Biometric Society, vol. 79(4), pages 3153-3164, December.
    14. David Bruns-Smith & Oliver Dukes & Avi Feller & Elizabeth L. Ogburn, 2023. "Augmented balancing weights as linear regression," Papers 2304.14545, arXiv.org, revised Jun 2024.

  57. Victor Chernozhukov & Iv'an Fern'andez-Val & Martin Weidner, 2018. "Network and Panel Quantile Effects Via Distribution Regression," Papers 1803.08154, arXiv.org, revised Jun 2020.

    Cited by:

    1. Botosaru, Irene & Muris, Chris & Pendakur, Krishna, 2023. "Identification of time-varying transformation models with fixed effects, with an application to unobserved heterogeneity in resource shares," Journal of Econometrics, Elsevier, vol. 232(2), pages 576-597.
    2. Alvarado, Rafael & Cuesta, Lizeth & Kumar, Pavan & Rehman, Abdul & Murshed, Muntasir & Işık, Cem & Vega, Nora & Ochoa-Moreno, Santiago & Tillaguango, Brayan, 2022. "Impact of natural resources on economic progress: Evidence for trading blocs in Latin America using non-linear econometric methods," Resources Policy, Elsevier, vol. 79(C).
    3. Neary, Peter & Carrère, Céline & Mrázová, Monika, 2020. "Gravity without Apologies: The Science of Elasticities, Distance, and Trade," CEPR Discussion Papers 14473, C.E.P.R. Discussion Papers.
    4. Peter Neary & Céline Carrère & Monika Mrázová, 2020. "Gravity without Apology: The Science of Elasticities, Distance, and Trade," Economics Series Working Papers 904, University of Oxford, Department of Economics.
    5. Ivan Fernandez-Val & Wayne Yuan Gao & Yuan Liao & Francis Vella, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," Papers 2202.04154, arXiv.org, revised Jul 2025.
    6. Vu, Dung Anh & Van Nguyen, Thinh & Nhu, Quang Minh & Tran, Tuyen Quang, 2024. "Does increased digital transformation promote a firm's financial performance? New insights from the quantile approach," Finance Research Letters, Elsevier, vol. 64(C).
    7. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2024. "Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials," Papers 2407.14074, arXiv.org, revised Jan 2025.
    8. Alnafrah, Ibrahim & Belyaeva, Zhanna, 2024. "The nonlinear road to happiness: Making sense of ESGD impacts on well-being," Structural Change and Economic Dynamics, Elsevier, vol. 70(C), pages 365-381.
    9. Andreas Dzemski, 2019. "An Empirical Model of Dyadic Link Formation in a Network with Unobserved Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 763-776, December.
    10. Maike Hohberg & Peter Pütz & Thomas Kneib, 2020. "Treatment effects beyond the mean using distributional regression: Methods and guidance," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-29, February.
    11. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2022. "Bivariate Distribution Regression with Application to Insurance Data," Papers 2203.12228, arXiv.org, revised Sep 2023.
    12. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2023. "Distributional Vector Autoregression: Eliciting Macro and Financial Dependence," Papers 2303.04994, arXiv.org.
    13. Nathan Kallus & Miruna Oprescu, 2022. "Robust and Agnostic Learning of Conditional Distributional Treatment Effects," Papers 2205.11486, arXiv.org, revised Jun 2025.

  58. Victor Chernozhukov & Wolfgang K. Hardle & Chen Huang & Weining Wang, 2018. "LASSO-Driven Inference in Time and Space," Papers 1806.05081, arXiv.org, revised May 2020.

    Cited by:

    1. Fan, Qingliang & Zhong, Wei, 2018. "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective," IRTG 1792 Discussion Papers 2018-052, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Klochkov, Yegor, 2022. "SONIC: SOcial Network analysis with Influencers and Communities," Journal of Econometrics, Elsevier, vol. 228(2), pages 177-220.
    3. Michael Kostmann & Wolfgang K. Härdle, 2019. "Forecasting in Blockchain-Based Local Energy Markets," Energies, MDPI, vol. 12(14), pages 1-27, July.
    4. Zbonakova, Lenka & Pio Monti, Ricardo & Härdle, Wolfgang Karl, 2018. "Towards the interpretation of time-varying regularization parameters in streaming penalized regression models," IRTG 1792 Discussion Papers 2018-059, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
    6. Zhong, Wei & Liu, Xi & Ma, Shuangge, 2018. "Variable selection and direction estimation for single-index models via DC-TGDR method," IRTG 1792 Discussion Papers 2018-050, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    7. Chen, C. Y-H. & Härdle, W. K. & Klochkov, Y., 2019. "Influencers and Communities in Social Networks," Cambridge Working Papers in Economics 1998, Faculty of Economics, University of Cambridge.
    8. Guo, Li & Tao, Yubo & Härdle, Wolfgang Karl, 2018. "Understanding Latent Group Structure of Cryptocurrencies Market: A Dynamic Network Perspective," IRTG 1792 Discussion Papers 2018-032, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    9. Tim Munday & James Brookes, 2021. "Mark my words: the transmission of central bank communication to the general public via the print media," Bank of England working papers 944, Bank of England.
    10. Packham, Natalie & Woebbeking, Fabian, 2018. "A factor-model approach for correlation scenarios and correlation stress-testing," IRTG 1792 Discussion Papers 2018-034, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Discussion Papers LFIN 2021004, Université catholique de Louvain, Louvain Finance (LFIN).
    12. Smeekes, Stephan & Wijler, Etienne, 2021. "An automated approach towards sparse single-equation cointegration modelling," Journal of Econometrics, Elsevier, vol. 221(1), pages 247-276.
    13. Packham, Natalie & Kalkbrener, Michael & Overbeck, Ludger, 2018. "Default probabilities and default correlations under stress," IRTG 1792 Discussion Papers 2018-037, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    14. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019. "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers 1902.10991, arXiv.org, revised Dec 2020.
    15. Kuczmaszewska, Anna & Yan, Ji Gao, 2018. "On complete convergence in Marcinkiewicz-Zygmund type SLLN for random variables," IRTG 1792 Discussion Papers 2018-041, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    16. Chen, Haiqiang & Li, Yingxing & Lin, Ming & Zhu, Yanli, 2018. "A Regime Shift Model with Nonparametric Switching Mechanism," IRTG 1792 Discussion Papers 2018-048, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. Yatracos, Yannis G., 2018. "Residual'S Influence Index (Rinfin), Bad Leverage And Unmasking In High Dimensional L2-Regression," IRTG 1792 Discussion Papers 2018-060, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    18. Zbonakova, Lenka & Li, Xinjue & Härdle, Wolfgang Karl, 2018. "Penalized Adaptive Forecasting with Large Information Sets and Structural Changes," IRTG 1792 Discussion Papers 2018-039, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    19. Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Klochkov, Yegor, 2019. "SONIC: SOcial Network with Influencers and Communities," IRTG 1792 Discussion Papers 2019-025, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    20. Packham, Natalie, 2018. "Optimal contracts under competition when uncertainty from adverse selection and moral hazard are present," IRTG 1792 Discussion Papers 2018-033, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    21. Cai, Zongwu & Fang, Ying & Lin, Ming & Su, Jia, 2018. "Inferences for a Partially Varying Coefficient Model With Endogenous Regressors," IRTG 1792 Discussion Papers 2018-047, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    22. Wang, Honglin & Yu, Fan & Zhou, Yinggang, 2018. "Property Investment and Rental Rate under Housing Price Uncertainty: A Real Options Approach," IRTG 1792 Discussion Papers 2018-051, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    23. Yan, Ji Gao, 2018. "Complete Convergence and Complete Moment Convergence for Maximal Weighted Sums of Extended Negatively Dependent Random Variables," IRTG 1792 Discussion Papers 2018-040, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    24. Kalkbrener, Michael & Packham, Natalie, 2018. "Correlation Under Stress In Normal Variance Mixture Models," IRTG 1792 Discussion Papers 2018-035, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    25. Chiu, Hsin-Yu & Chiang, Mi-Hsiu & Kuo, Wei-Yu, 2018. "Predicative Ability of Similarity-based Futures Trading Strategies," IRTG 1792 Discussion Papers 2018-045, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    26. Guo, Shaojun & Li, Dong & Li, Muyi, 2018. "Strict Stationarity Testing and GLAD Estimation of Double Autoregressive Models," IRTG 1792 Discussion Papers 2018-049, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    27. Mihoci, Andrija & Althof, Michael & Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl, 2019. "FRM Financial Risk Meter," IRTG 1792 Discussion Papers 2019-021, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    28. Dovì, Max-Sebastian & Koester, Gerrit & Nickel, Christiane, 2021. "Addressing the endogeneity of slack in Phillips Curves," Working Paper Series 2619, European Central Bank.
    29. Panxu Yuan & Xiao Guo, 2022. "High-dimensional inference for linear model with correlated errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 21-52, January.
    30. Koziuk, Andzhey & Spokoiny, Vladimir, 2018. "Toolbox: Gaussian comparison on Eucledian balls," IRTG 1792 Discussion Papers 2018-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    31. Chernozhukov, Victor & Härdle, Wolfgang Karl & Huang, Chen & Wang, Weining, 2018. "LASSO-Driven Inference in Time and Space," IRTG 1792 Discussion Papers 2018-021, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    32. Bao, Xiaojia & Fan, Qingliang, 2018. "The impact of temperature on gaming productivity: evidence from online games," IRTG 1792 Discussion Papers 2018-053, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  59. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.

    Cited by:

    1. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    2. TELLO, Mario D., 2024. "Inversión Pública En Infraestructura Y Crecimiento Regional En Perú, 2005-2020: Un Análisis Basado En Técnicas De Aprendizaje Automático Causal," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 24(2), pages 195-222.
    3. Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024. "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers 202402, University of Barcelona, Research Institute of Applied Economics, revised Feb 2024.
    4. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    5. Soren Blomquist & Anil Kumar & Che-Yuan Liang & Whitney K. Newey, 2022. "Nonlinear Budget Set Regressions for the Random Utility Model," Working Papers 2219, Federal Reserve Bank of Dallas.
    6. Paul S. Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Dec 2024.
    7. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    8. Amarendra Sharma, 2025. "P-CRE-DML: A Novel Approach for Causal Inference in Non-Linear Panel Data," Papers 2506.23297, arXiv.org.
    9. Manu Navjeevan, 2023. "An Identification and Dimensionality Robust Test for Instrumental Variables Models," Papers 2311.14892, arXiv.org, revised Dec 2024.
    10. Juan Carlos Escanciano & Telmo P'erez-Izquierdo, 2023. "Automatic Debiased Estimation with Machine Learning-Generated Regressors," Papers 2301.10643, arXiv.org, revised May 2025.
    11. Jing Kong, 2025. "On the Asymptotics of the Minimax Linear Estimator," Papers 2510.16661, arXiv.org.
    12. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org, revised Mar 2025.
    13. Newham, Melissa & Valente, Marica, 2024. "The cost of influence: How gifts to physicians shape prescriptions and drug costs," Journal of Health Economics, Elsevier, vol. 95(C).
    14. Fabian Muny, 2025. "Evaluating Program Sequences with Double Machine Learning: An Application to Labor Market Policies," Papers 2506.11960, arXiv.org.
    15. Paul Goldsmith-Pinkham & Peter Hull & Michal Koles'ar, 2021. "Contamination Bias in Linear Regressions," Papers 2106.05024, arXiv.org, revised Jun 2024.
    16. Chenyu Hou, 2023. "Learning and Subjective Expectation Formation: A Recurrent Neural Network Approach," Discussion Papers dp23-13, Department of Economics, Simon Fraser University.
    17. Sven Klaassen & Jan Rabenseifner & Jannis Kueck & Philipp Bach, 2025. "Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators," Papers 2503.17290, arXiv.org, revised May 2025.
    18. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
    19. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Jun 2025.
    20. Sören Blomquist & Jerry A. Hausman & Whitney K. Newey, 2023. "The Econometrics of Nonlinear Budget Sets," Annual Review of Economics, Annual Reviews, vol. 15(1), pages 287-306, September.
    21. Jing Kong, 2025. "Causal Inference in High-Dimensional Generalized Linear Models with Binary Outcomes," Papers 2510.16669, arXiv.org.
    22. Soren Blomquist & Anil Kumar & Whitney K. Newey, 2024. "Panel Estimation of Taxable Income Elasticities with Heterogeneity and Endogenous Budget Sets," Papers 2501.00633, arXiv.org.
    23. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    24. Manu Navjeevan & Rodrigo Pinto & Andres Santos, 2023. "Identification and Estimation in a Class of Potential Outcomes Models," Papers 2310.05311, arXiv.org.
    25. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    26. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    27. Julius Schaper, 2025. "Residualised Treatment Intensity and the Estimation of Average Partial Effects," Papers 2502.10301, arXiv.org.
    28. Jin, Zequn & Sun, Jisheng, 2025. "Neyman-orthogonal moment for instrumental variable quantile regression model with high dimensional data," Economics Letters, Elsevier, vol. 253(C).
    29. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2025. "Robust Semiparametric Inference for Bayesian Additive Regression Trees," Papers 2509.24634, arXiv.org, revised Oct 2025.
    30. Amandeep Singh & Ye Liu & Hema Yoganarasimhan, 2023. "Choice Models and Permutation Invariance: Demand Estimation in Differentiated Products Markets," Papers 2307.07090, arXiv.org, revised Feb 2024.
    31. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    32. Geonwoo Kim & Suyong Song, 2024. "Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables," Papers 2408.14671, arXiv.org.
    33. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
    34. Zhang, Jeffrey & Li, Wei & Miao, Wang & Tchetgen Tchetgen, Eric, 2023. "Proximal causal inference without uniqueness assumptions," Statistics & Probability Letters, Elsevier, vol. 198(C).
    35. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    36. Semenova, Vira, 2025. "Generalized Lee bounds," Journal of Econometrics, Elsevier, vol. 251(C).
    37. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "LASSO Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Jan 2026.
    38. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects," Econometrica, Econometric Society, vol. 90(3), pages 967-1027, May.
    39. Yikun Zhang & Yen-Chi Chen, 2025. "Doubly Robust Inference on Causal Derivative Effects for Continuous Treatments," Papers 2501.06969, arXiv.org, revised Apr 2025.
    40. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
    41. Jikai Jin & Lester Mackey & Vasilis Syrgkanis, 2025. "It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation," Papers 2507.02275, arXiv.org, revised Nov 2025.
    42. Philipp Bach & Victor Chernozhukov & Carlos Cinelli & Lin Jia & Sven Klaassen & Nils Skotara & Martin Spindler, 2025. "Sensitivity Analysis for Causal ML: A Use Case at Booking.com," Papers 2510.09109, arXiv.org.
    43. Paul Goldsmith-Pinkham & Peter Hull & Michal Kolesár, 2021. "On Estimating Multiple Treatment Effects with Regression," Working Papers 2021-41, Princeton University. Economics Department..

  60. Victor Chernozhukov & Iv'an Fern'andez-Val & Whitney Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models," Papers 1711.02184, arXiv.org, revised Oct 2019.

    Cited by:

    1. Romuald Meango & Esther Mirjam Girsberger, 2023. "Just Ask Them Twice: Choice Probabilities and Identification of Ex ante returns and Willingness-To-Pay," Papers 2303.03009, arXiv.org, revised May 2025.
    2. Victor Chernozhukov & Iván Fernández‐Val & Whitney Newey & Sami Stouli & Francis Vella, 2020. "Semiparametric estimation of structural functions in nonseparable triangular models," Quantitative Economics, Econometric Society, vol. 11(2), pages 503-533, May.
    3. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    4. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    5. Whitney K. Newey & Sami Stouli, 2018. "Heterogenous coefficients, discrete instruments, and identification of treatment effects," CeMMAP working papers CWP66/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Nir Billfeld & Moshe Kim, 2024. "Context-dependent Causality (the Non-Nonotonic Case)," Papers 2404.05021, arXiv.org.
    7. Fernández-Val, Iván & van Vuuren, Aico & Vella, Francis & Peracchi, Franco, 2022. "Selection and the Distribution of Female Hourly Wages in the U.S," IZA Discussion Papers 15028, IZA Network @ LISER.
    8. Chernozhukov, Victor & Fernández-Val, Iván & Meier, Jonas & van Vuuren, Aico & Vella, Francis, 2025. "Bivariate Distribution Regression; Theory, Estimation and an Application to Intergenerational Mobility," IZA Discussion Papers 18091, IZA Network @ LISER.
    9. Whitney Newey & Sami Stouli, 2018. "Control Variables, Discrete Instruments, and Identification of Structural Functions," Bristol Economics Discussion Papers 18/702, School of Economics, University of Bristol, UK.
    10. Romauld Méango, 2023. "Identification of ex ante returns using elicited choice probabilities," Economics Series Working Papers 1007, University of Oxford, Department of Economics.
    11. Richard H. Spady & Sami Stouli, 2025. "Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions," Econometrica, Econometric Society, vol. 93(5), pages 1885-1913, September.
    12. Romuald Meango, 2025. "Is the distribution of resolvable uncertainty Type I extreme value? A Test for Random Coefficient Models using Choice Probabilities," Papers 2503.13901, arXiv.org.
    13. Jayeeta Bhattacharya, 2020. "Quantile regression with generated dependent variable and covariates," Papers 2012.13614, arXiv.org.
    14. Luigi Garzon & Vitor Possebom, 2025. "Nonlinear Treatment Effects in Shift-Share Designs," Papers 2507.21915, arXiv.org, revised Nov 2025.
    15. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    16. Tadao Hoshino, 2021. "Estimating a Continuous Treatment Model with Spillovers: A Control Function Approach," Papers 2112.15114, arXiv.org, revised Jan 2023.
    17. Meango, Romuald & Girsberger, Esther Mirjam, 2024. "Identification of Ex Ante Returns Using Elicited Choice Probabilities: An Application to Preferences for Public-Sector Jobs," IZA Discussion Papers 17174, IZA Network @ LISER.
    18. Nagasawa, Kenichi, 2020. "Identification and Estimation of Group-Level Partial Effects," The Warwick Economics Research Paper Series (TWERPS) 1243, University of Warwick, Department of Economics.
    19. Iván Fernández‐Val & Aico van Vuuren & Francis Vella & Franco Peracchi, 2023. "Selection and the distribution of female real hourly wages in the United States," Quantitative Economics, Econometric Society, vol. 14(2), pages 571-607, May.
    20. Fernández-Val, Ivan & van Vuuren, Aico & Vella, Francis, 2024. "Nonseparable sample selection models with censored selection rules," Journal of Econometrics, Elsevier, vol. 240(2).
    21. Christian Gische & Manuel C. Voelkle, 2022. "Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 868-901, September.
    22. Jonas Meier, 2020. "Multivariate Distribution Regression," Diskussionsschriften dp2023, Universitaet Bern, Departement Volkswirtschaft.
    23. Whitney K. Newey & Sami Stouli, 2018. "Identification of Treatment Effects under Limited Exogenous Variation," Papers 1811.09837, arXiv.org, revised Jan 2025.
    24. Wied, Dominik, 2024. "Semiparametric distribution regression with instruments and monotonicity," Labour Economics, Elsevier, vol. 90(C).

  61. Alexandre Belloni & Christian Hansen & Whitney Newey, 2017. "Simultaneous Confidence Intervals for High-dimensional Linear Models with Many Endogenous Variables," Papers 1712.08102, arXiv.org, revised Aug 2019.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Victor Chernozhukov & Chen Huang & Weining Wang, 2021. "Uniform Inference on High-dimensional Spatial Panel Networks," Papers 2105.07424, arXiv.org, revised Jul 2025.
    3. Caner, Mehmet, 2023. "Generalized linear models with structured sparsity estimators," Journal of Econometrics, Elsevier, vol. 236(2).
    4. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2020. "Ill-posed estimation in high-dimensional models with instrumental variables," Journal of Econometrics, Elsevier, vol. 219(1), pages 171-200.
    5. Baris Ata & Alexandre Belloni & Ozan Candogan, 2018. "Latent Agents in Networks: Estimation and Targeting," Papers 1808.04878, arXiv.org, revised Jan 2022.

  62. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul & Mathieu Rosenbaum & Alexandre B. Tsybakov, 2017. "Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables," Working Papers 2017-26, Center for Research in Economics and Statistics.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.

  63. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2017. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Papers 1712.09089, arXiv.org, revised May 2021.

    Cited by:

    1. Masahiro Kato & Akari Ohda, 2023. "Asymptotically Unbiased Synthetic Control Methods by Moment Matching," Papers 2307.11127, arXiv.org, revised Nov 2025.
    2. Lionel Fontagné & Francesca Micocci & Armando Rungi, 2025. "The heterogeneous impact of the EU-Canada agreement with causal machine learning," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-04913313, HAL.
    3. Nuno Garoupa & Rok Spruk, 2024. "Populist Constitutional Backsliding and Judicial Independence: Evidence from Turkiye," Papers 2410.02439, arXiv.org.
    4. Billy Ferguson & Brad Ross, 2020. "Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error," Papers 2012.15367, arXiv.org, revised Feb 2021.
    5. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    6. Yihong Xu & Quan Zhou, 2025. "Bayesian Synthetic Control with a Soft Simplex Constraint," Papers 2503.06454, arXiv.org, revised Dec 2025.
    7. Ignacio Martinez & Jaume Vives-i-Bastida, 2022. "Bayesian and Frequentist Inference for Synthetic Controls," Papers 2206.01779, arXiv.org, revised Jul 2024.
    8. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2019. "Synthetic Difference In Differences," NBER Working Papers 25532, National Bureau of Economic Research, Inc.
    9. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    10. Chen, Qian & Koch, Christoffer & Richardson, Gary & Sharma, Padma, 2025. "Suspensions of payments and their consequences," Journal of Financial Stability, Elsevier, vol. 78(C).
    11. Joseph Fry, 2023. "A Method of Moments Approach to Asymptotically Unbiased Synthetic Controls," Papers 2312.01209, arXiv.org, revised Mar 2024.
    12. Alberto Abadie & Jinglong Zhao, 2021. "Synthetic Controls for Experimental Design," Papers 2108.02196, arXiv.org, revised Apr 2025.
    13. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    14. Douglas Kiarelly Godoy de Araujo, 2024. "Synthetic controls with machine learning: application on the effect of labour deregulation on worker productivity in Brazil," BIS Working Papers 1181, Bank for International Settlements.
    15. Eli Ben-Michael & Avi Feller & Jesse Rothstein, 2018. "The Augmented Synthetic Control Method," Papers 1811.04170, arXiv.org, revised Jul 2020.
    16. Lucke, Bernd, 2022. "Growth Effects of European Monetary Union: A Synthetic Control Approach," MPRA Paper 120662, University Library of Munich, Germany, revised 27 Mar 2024.
    17. Robert Messerle & Jonas Schreyögg, 2024. "Country-level effects of diagnosis-related groups: evidence from Germany’s comprehensive reform of hospital payments," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(6), pages 1013-1030, August.
    18. Michael Funke & Kadri Männasoo & Helery Tasane, 2023. "Regional Economic Impacts of the Øresund Cross-Border Fixed Link: Cui Bono?," CESifo Working Paper Series 10557, CESifo.
    19. Achille Nazaret & Claudia Shi & David M. Blei, 2023. "On the Misspecification of Linear Assumptions in Synthetic Control," Papers 2302.12777, arXiv.org.
    20. Carlos J. Charotti & Nuno Palma & João Pereira dos Santos, 2022. "American Treasure and the Decline of Spain," Economics Discussion Paper Series 2201, Economics, The University of Manchester, revised Oct 2025.
    21. Justin C. Wiltshire, 2023. "Walmart Supercenters and Monopsony Power: How A Large, Low-Wage Employer Impacts Local Labor Markets," Department Discussion Papers 2304, Department of Economics, University of Victoria.
    22. Priscila Espinosa & Daniel Aparicio-Pérez & José Manuel Pavía & Emili Tortosa-Ausina, 2023. "On the Impact of Next Generation EU Funds: A Regional Synthetic Control Method Approach," Working Papers 2023/07, Economics Department, Universitat Jaume I, Castellón (Spain).
    23. Giovanni Mellace & Alessandra Pasquini, 2022. "Mediation Analysis Synthetic Control," Temi di discussione (Economic working papers) 1389, Bank of Italy, Economic Research and International Relations Area.
    24. Peter Backus & Thien Nguyen, 2021. "The Effect of the Sex Buyer Law on the Market for Sex, Sexual Health and Sexual Violence," Economics Discussion Paper Series 2106, Economics, The University of Manchester.
    25. Alberto Abadie & Jaume Vives-i-Bastida, 2022. "Synthetic Controls in Action," Papers 2203.06279, arXiv.org.
    26. Ben Deaner & Chen-Wei Hsiang & Andrei Zeleneev, 2025. "Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities," Papers 2503.20769, arXiv.org, revised Mar 2025.
    27. Ruiz, Miguel Haro & Schult, Christoph & Wunder, Christoph, 2024. "The effects of the Iberian exception mechanism on wholesale electricity prices and consumer inflation: A synthetic-controls approach," IWH Discussion Papers 5/2024, Halle Institute for Economic Research (IWH).
    28. Su, Liangjun & Jin, Sainan & Wang, Xia, 2025. "Sieve estimation of state-varying factor models," Journal of Econometrics, Elsevier, vol. 251(C).
    29. Claudia Shi & Dhanya Sridhar & Vishal Misra & David M. Blei, 2021. "On the Assumptions of Synthetic Control Methods," Papers 2112.05671, arXiv.org, revised Dec 2021.
    30. Bruno Ferman, 2019. "On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls," Papers 1906.06665, arXiv.org, revised May 2020.
    31. Maximilian Langer & Joshua Hassib & Lars P. Feld & Daniel Nientiedt, 2025. "Evaluating the Effects of the German Debt Brake: A Synthetic Control Approach," CESifo Working Paper Series 11933, CESifo.
    32. Demirci, Murat, 2023. "Youth responses to political populism: Education abroad as a step toward emigration," Journal of Comparative Economics, Elsevier, vol. 51(2), pages 653-673.
    33. Li, Xingyu & Shen, Yan & Zhou, Qiankun, 2024. "Confidence intervals of treatment effects in panel data models with interactive fixed effects," Journal of Econometrics, Elsevier, vol. 240(1).
    34. Lea Bottmer & Guido Imbens & Jann Spiess & Merrill Warnick, 2021. "A Design-Based Perspective on Synthetic Control Methods," Papers 2101.09398, arXiv.org, revised Jul 2023.
    35. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
    36. Shosei Sakaguchi & Hayato Tagawa, 2024. "Identification and Inference for Synthetic Control Methods with Spillover Effects: Estimating the Economic Cost of the Sudan Split," Papers 2408.00291, arXiv.org, revised Oct 2024.
    37. Yan Zhang & Zudi Lu, 2024. "A Time Series Synthetic Control Causal Evaluation of the UK’s Mini-Budget Policy on Stock Market," Mathematics, MDPI, vol. 12(20), pages 1-25, October.
    38. Jiafeng Chen, 2023. "Synthetic Control as Online Linear Regression," Econometrica, Econometric Society, vol. 91(2), pages 465-491, March.
    39. Barber, Andrew & West, Jeremy, 2022. "Conditional cash lotteries increase COVID-19 vaccination rates," Journal of Health Economics, Elsevier, vol. 81(C).
    40. Jonathan Roth & Pedro H. C. Sant'Anna & Alyssa Bilinski & John Poe, 2022. "What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature," Papers 2201.01194, arXiv.org, revised Jan 2023.
    41. Jianfei Cao & Connor Dowd, 2019. "Estimation and Inference for Synthetic Control Methods with Spillover Effects," Papers 1902.07343, arXiv.org, revised Jan 2026.
    42. Zhentao Shi & Yishu Wang, 2025. "L2-relaxation for Economic Prediction," Papers 2510.12183, arXiv.org.
    43. Nuno Garoupa & Virginia Rosales & Rok Spruk, 2025. "Assaults on Judicial Independence under the Pretense of Modernization: Evidence from Venezuela," Papers 2511.10681, arXiv.org.
    44. Jiafeng Chen, 2022. "Synthetic Control As Online Linear Regression," Papers 2202.08426, arXiv.org, revised Nov 2022.
    45. Simon Freyaldenhoven & Christian Hansen & Jorge Perez Perez & Jesse Shapiro, 2021. "Visualization, Identification, and stimation in the Linear Panel Event-Study Design," Working Papers 21-44, Federal Reserve Bank of Philadelphia.
    46. Erick Lahura & Rosario Sabrera, 2023. "The effect of infrastructure investment on tourism demand: a synthetic control approach for the case of Kuelap, Peru," Empirical Economics, Springer, vol. 65(1), pages 443-478, July.
    47. Luis A. F. Alvarez & Bruno Ferman, 2024. "On "Imputation of Counterfactual Outcomes when the Errors are Predictable": Discussions on Misspecification and Suggestions of Sensitivity Analyses," Working Papers, Department of Economics 2024_16, University of São Paulo (FEA-USP).
    48. Kuosmanen, Timo & Zhou, Xun & Eskelinen, Juha & Malo, Pekka, 2021. "Design Flaw of the Synthetic Control Method," MPRA Paper 106328, University Library of Munich, Germany.
    49. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2018. "Exact and robust conformal inference methods for predictive machine learning with dependent data," CeMMAP working papers CWP16/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    50. Debdeep Chattopadhyay, 2023. "Did the Massachusetts Health Reform Program increase self-employment?," Empirical Economics, Springer, vol. 65(3), pages 1309-1344, September.
    51. Nicolaj S{o}ndergaard Muhlbach & Mikkel Slot Nielsen, 2019. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," Papers 1909.03968, arXiv.org, revised Feb 2021.
    52. Woo-Mora, L. Guillermo, 2025. "Populism’s original sin: Short-term populist penalties and uncertainty traps," European Economic Review, Elsevier, vol. 172(C).
    53. Roberta Di Stefano & Giovanni Mellace, 2024. "The inclusive Synthetic Control Method," Papers 2403.17624, arXiv.org, revised Nov 2024.
    54. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    55. Taehyeon Koo & Zijian Guo, 2025. "Distributionally Robust Synthetic Control: Ensuring Robustness Against Highly Correlated Controls and Weight Shifts," Papers 2511.02632, arXiv.org, revised Jan 2026.
    56. Angelo Forino & Andrea Mercatanti & Giacomo Morelli, 2025. "Propensity score with factor loadings: the effect of the Paris Agreement," Papers 2507.08764, arXiv.org.
    57. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    58. Dmitry Arkhangelsky & David Hirshberg, 2023. "Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables," Papers 2311.13575, arXiv.org, revised Dec 2023.
    59. Fry, Joseph, 2024. "A method of moments approach to asymptotically unbiased Synthetic Controls," Journal of Econometrics, Elsevier, vol. 244(1).
    60. Luis Alvarez & Bruno Ferman, 2025. "On the relationship between prediction intervals, tests of sharp nulls and inference on realized treatment effects in settings with few treated units," Papers 2506.14998, arXiv.org.
    61. Jason Poulos, 2019. "State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction," Papers 1903.08028, arXiv.org, revised Dec 2023.
    62. Matias D. Cattaneo & Yingjie Feng & Filippo Palomba & Rocio Titiunik, 2022. "scpi: Uncertainty Quantification for Synthetic Control Methods," Papers 2202.05984, arXiv.org, revised Oct 2022.
    63. Jorge A. Arroyo, 2025. "Short-Run Multi-Outcome Effects of Nightlife Regulation in San Juan," Papers 2510.25782, arXiv.org.
    64. Yinchu Zhu, 2019. "How well can we learn large factor models without assuming strong factors?," Papers 1910.10382, arXiv.org, revised Nov 2019.
    65. Chen, Qiang & Xiao, Zhijie & Yao, Qingsong, 2025. "Quantile control via random forest," Journal of Econometrics, Elsevier, vol. 249(PA).
    66. Silvia Goncalves & Serena Ng, 2024. "Imputation of Counterfactual Outcomes when the Errors are Predictable," Papers 2403.08130, arXiv.org, revised May 2024.
    67. Cong Wang, 2024. "Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis," Papers 2408.09271, arXiv.org, revised Sep 2024.
    68. Klößner, Stefan & Pfeifer, Gregor, 2018. "Synthesizing Cash for Clunkers: Stabilizing the Car Market, Hurting the Environment?," MPRA Paper 88175, University Library of Munich, Germany.
    69. Luis Alvarez & Bruno Ferman, 2023. "Extensions for Inference in Difference-in-Differences with Few Treated Clusters," Papers 2302.03131, arXiv.org.
    70. Isaiah Andrews & Drew Fudenberg & Lihua Lei & Annie Liang & Chaofeng Wu, 2022. "The Transfer Performance of Economic Models," Papers 2202.04796, arXiv.org, revised Mar 2025.
    71. Doerr, Luisa & Dorn, Florian & Gaebler, Stefanie & Potrafke, Niklas, 2020. "How new airport infrastructure promotes tourism: evidence from a synthetic control approach in German regions," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 54(10), pages 1402-1412.
    72. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2019. "Inference on average treatment effects in aggregate panel data settings," CeMMAP working papers CWP32/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    73. Vivek F. Farias & Andrew A. Li & Tianyi Peng, 2021. "Learning Treatment Effects in Panels with General Intervention Patterns," Papers 2106.02780, arXiv.org, revised Mar 2023.
    74. Alexander Almeida & Susan Athey & Guido Imbens & Eva Lestant & Alexia Olaizola, 2025. "Estimating Variances for Causal Panel Data Estimators," Papers 2510.11841, arXiv.org, revised Nov 2025.
    75. Luis Costa & Vivek F. Farias & Patricio Foncea & Jingyuan (Donna) Gan & Ayush Garg & Ivo Rosa Montenegro & Kumarjit Pathak & Tianyi Peng & Dusan Popovic, 2023. "Generalized Synthetic Control for TestOps at ABI: Models, Algorithms, and Infrastructure," Interfaces, INFORMS, vol. 53(5), pages 336-349, September.
    76. Sang‐Wook (Stanley) Cho & Hansoo Choi, 2025. "Unlocking the impact of US free trade agreements on industries with a synthetic control approach," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 58(2), pages 716-746, May.
    77. Giovanni Peri & Derek Rury & Justin C. Wiltshire, 2020. "The Economic Impact of Migrants from Hurricane Maria," NBER Working Papers 27718, National Bureau of Economic Research, Inc.
    78. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2023. "Same Root Different Leaves: Time Series and Cross‐Sectional Methods in Panel Data," Econometrica, Econometric Society, vol. 91(6), pages 2125-2154, November.
    79. Bruno Ferman, 2023. "Inference in difference‐in‐differences: How much should we trust in independent clusters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 358-369, April.
    80. Zongwu Cai & Ying Fang & Ming Lin & Zixuan Wu, 2023. "A Quasi Synthetic Control Method for Nonlinear Models With High-Dimensional Covariates," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202305, University of Kansas, Department of Economics, revised Aug 2023.
    81. Eli Ben‐Michael & Avi Feller & Jesse Rothstein, 2022. "Synthetic controls with staggered adoption," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 351-381, April.
    82. Cao, Jing & Ho, Mun S. & Ma, Rong & Teng, Fei, 2021. "When carbon emission trading meets a regulated industry: Evidence from the electricity sector of China," Journal of Public Economics, Elsevier, vol. 200(C).
    83. Guido W. Imbens & Davide Viviano, 2023. "Identification and Inference for Synthetic Controls with Confounding," Papers 2312.00955, arXiv.org.
    84. Ye Shen & Rui Song & Alberto Abadie, 2025. "Efficiently Learning Synthetic Control Models for High-dimensional Disaggregated Data," Papers 2510.22828, arXiv.org.
    85. Justin Wiltshire, 2021. "allsynth: Synthetic control bias-corrections utilities for Stata," 2021 Stata Conference 15, Stata Users Group.
    86. Bruno Ferman, 2019. "Matching Estimators with Few Treated and Many Control Observations," Papers 1909.05093, arXiv.org, revised Mar 2021.
    87. Melissa Dell, 2024. "Deep Learning for Economists," Papers 2407.15339, arXiv.org, revised Nov 2024.
    88. Chengwang Liao & Zhentao Shi & Yapeng Zheng, 2025. "A Relaxation Approach to Synthetic Control," Papers 2508.01793, arXiv.org.
    89. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2018. "Debiasing and $t$-tests for synthetic control inference on average causal effects," Papers 1812.10820, arXiv.org, revised May 2025.
    90. Rong J. B. Zhu, 2023. "Synthetic Regressing Control," Papers 2306.02584, arXiv.org, revised Jan 2026.
    91. Hideki Shimada & Kenji Asano & Yu Nagai & Akito Ozawa, 2022. "Assessing the Impact of Offshore Wind Power Deployment on Fishery: A Synthetic Control Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 83(3), pages 791-829, November.
    92. David M. Ritzwoller & Joseph P. Romano & Azeem M. Shaikh, 2024. "Randomization Inference: Theory and Applications," Papers 2406.09521, arXiv.org, revised Feb 2025.
    93. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández‐Val, 2025. "Fisher–Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India," Econometrica, Econometric Society, vol. 93(4), pages 1121-1164, July.
    94. Bülte, Christopher & Buse, Rebekka, 2025. "Disentangling the economic effects of Brexit and COVID-19 in the UK," Working Paper Series in Economics 174, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    95. Niklas Potrafke & Luisa Dörr & Klaus Gründler & Tuuli Tähtinen & Luisa Dörr, 2025. "Female Leaders and the Representation of Women in Government," CESifo Working Paper Series 11851, CESifo.
    96. Roberto Duncan & Enrique Martínez García & Luke Miller, 2025. "Tempting FAIT: Flexible Average Inflation Targeting and the Post-COVID U.S. Inflation Surge," Working Papers 2511, Federal Reserve Bank of Dallas.
    97. Sandro Heiniger, 2024. "Data-driven model selection within the matrix completion method for causal panel data models," Papers 2402.01069, arXiv.org.

  64. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers 22/17, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Xiaobo Wang & Jiayu Huang & Guosheng Yin & Jian Huang & Yuanshan Wu, 2023. "Double bias correction for high-dimensional sparse additive hazards regression with covariate measurement errors," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 115-141, January.
    3. Fan, Jinlin & Zhang, Yaowu & Zhu, Liping, 2022. "Independence tests in the presence of measurement errors: An invariance law," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    4. Li, Mengyan & Li, Runze & Ma, Yanyuan, 2021. "Inference in high dimensional linear measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    5. Galea, Manuel & de Castro, Mário, 2017. "Robust inference in a linear functional model with replications using the t distribution," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 134-145.
    6. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul & Mathieu Rosenbaum & Alexandre B. Tsybakov, 2017. "Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables," Working Papers 2017-26, Center for Research in Economics and Statistics.

  65. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2017. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Sciences Po Economics Publications (main) hal-03391975, HAL.

    Cited by:

    1. Marcel Klatt & Axel Munk & Yoav Zemel, 2022. "Limit laws for empirical optimal solutions in random linear programs," Annals of Operations Research, Springer, vol. 315(1), pages 251-278, August.
    2. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    3. Gunsilius, Florian F., 2023. "A condition for the identification of multivariate models with binary instruments," Journal of Econometrics, Elsevier, vol. 235(1), pages 220-238.
    4. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Sciences Po Economics Publications (main) hal-03936221, HAL.
    5. Hamel, Andreas H. & Kostner, Daniel, 2018. "Cone distribution functions and quantiles for multivariate random variables," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 97-113.
    6. Jun Yu & Mingyao Ai & Zhiqiang Ye, 2024. "A review on design inspired subsampling for big data," Statistical Papers, Springer, vol. 65(2), pages 467-510, April.
    7. Marc Hallin & Hongjian Shi & Mathias Drton & Fang Han, 2021. "Center-Outward Sign- and Rank-Based Quadrant, Spearman, and Kendall Tests for Multivariate Independence," Working Papers ECARES 2021-27, ULB -- Universite Libre de Bruxelles.
    8. Manuel Arellano & Stéphane Bonhomme, 2019. "Recovering Latent Variables by Matching," Working Papers wp2019_1914, CEMFI.
    9. María Edo & Walter Sosa Escudero & Marcela Svarc, 2021. "A multidimensional approach to measuring the middle class," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(1), pages 139-162, March.
    10. Lixiong Li & Marc Henry, 2022. "Finite Sample Inference in Incomplete Models," Papers 2204.00473, arXiv.org, revised Oct 2025.
    11. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
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    23. Jan Bierlant & Sven Buitendag & Eustasio Del Barrio & Marc Hallin, 2019. "Center-Outward Quantiles And The Measurement Of Multivariate Risk," Working Papers ECARES 2019-30, ULB -- Universite Libre de Bruxelles.
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    26. Marc Hallin & Hang Liu, 2022. "Center-outward Rank- and Sign-based VARMA Portmanteau Tests," Working Papers ECARES 2022-27, ULB -- Universite Libre de Bruxelles.
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  66. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers 28/17, Institute for Fiscal Studies.

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    3. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    4. Asanov, Anastasiya-Mariya & Asanov, Igor & Buenstorf, Guido, 2024. "A low-cost digital first aid tool to reduce psychological distress in refugees: A multi-country randomized controlled trial of self-help online in the first months after the invasion of Ukraine," Social Science & Medicine, Elsevier, vol. 362(C).
    5. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
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    772. Youmi Suk & Hyunseung Kang, 2022. "Robust Machine Learning for Treatment Effects in Multilevel Observational Studies Under Cluster-level Unmeasured Confounding," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 310-343, March.
    773. Pietro Emilio Spini, 2021. "Robustness, Heterogeneous Treatment Effects and Covariate Shifts," Papers 2112.09259, arXiv.org, revised Aug 2024.
    774. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2024. "The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting," Health Economics, John Wiley & Sons, Ltd., vol. 33(6), pages 1387-1411, June.
    775. Kyle Myers & Wei Yang Tham, 2023. "Money, Time, and Grant Design," Papers 2312.06479, arXiv.org.
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    784. Fang, Yan & Liu, Yinglin & Yang, Yi & Lucey, Brian & Abedin, Mohammad Zoynul, 2025. "How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning," Research in International Business and Finance, Elsevier, vol. 74(C).
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    791. Rahul Singh & Hannah Zhou, 2022. "Kernel methods for long term dose response curves," Papers 2201.05139, arXiv.org, revised Dec 2024.
    792. Domguia, Edmond Noubissi & Mouafo, Paul Tadzong & Kountchou, Armand Mboutchounag & Nkengfack, Hilaire & Nchinda, Cerapis Mbognou, 2025. "From ashes to opportunity? Reimagining job creation in a smoke-free Africa," World Development Perspectives, Elsevier, vol. 39(C).
    793. Drukker, David M. & Liu, Di, 2025. "A cluster plugin method for selecting the GLM lasso tuning parameters in models for unbalanced panel data," Econometrics and Statistics, Elsevier, vol. 34(C), pages 14-31.
    794. Yantuan Yu & Ling Liu & Yayun Ren, 2025. "Effect of climate change adaptation on energy productivity: evidence from China," Economic Change and Restructuring, Springer, vol. 58(4), pages 1-22, August.
    795. Patrick Rehill & Nicholas Biddle, 2024. "Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning," Papers 2401.07075, arXiv.org, revised Mar 2024.
    796. Ying Jin & Dominik Rothenhäusler, 2024. "Tailored inference for finite populations: conditional validity and transfer across distributions," Biometrika, Biometrika Trust, vol. 111(1), pages 215-233.
    797. Abadie, Alberto & Gu, Jiaying & Shen, Shu, 2024. "Instrumental variable estimation with first-stage heterogeneity," Journal of Econometrics, Elsevier, vol. 240(2).
    798. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2024. "Semiparametric Bayesian Difference-in-Differences," Papers 2412.04605, arXiv.org, revised Jun 2025.
    799. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    800. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
    801. Tatsuru Kikuchi, 2025. "Stochastic Boundaries in Spatial General Equilibrium: A Diffusion-Based Approach to Causal Inference with Spillover Effects," Papers 2508.06594, arXiv.org.
    802. Koen Pauwels & Michael Peran & Zee Shah & German Schnaidt & Dauwe Vercamer, 2023. "Sponsored brands video rings up clicks and sales in the short and long run," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 275-286, September.
    803. Han, Yonghui & Mai, Jinghua & Zhang, Fan & Luo, Ruilin, 2025. "Leveraging external debt: Stimulate innovation by infrastructure development in Belt and Road countries," Economic Analysis and Policy, Elsevier, vol. 86(C), pages 1214-1243.
    804. Li, Libo & Yu, Huan & Kunc, Martin, 2024. "The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    805. Linarez, Misael, 2025. "Rethinking Total Factor Productivity: Beyond the Residual," OSF Preprints 2vfqc_v1, Center for Open Science.
    806. Aditya Ghosh & Dominik Rothenhausler, 2025. "Assumption-robust Causal Inference," Papers 2505.08729, arXiv.org, revised Jun 2025.
    807. McNamara, Sarah, 2020. "Returns to higher education and dropouts: A double machine learning approach," ZEW Discussion Papers 20-084, ZEW - Leibniz Centre for European Economic Research.
    808. Jinghao Sun & Eric J. Tchetgen Tchetgen, 2025. "On a Debiased and Semiparametric Efficient Changes-in-Changes Estimator," Papers 2507.07228, arXiv.org, revised Aug 2025.
    809. Anders Munch & Marie Skov Breum & Torben Martinussen & Thomas A. Gerds, 2023. "Targeted estimation of state occupation probabilities for the non‐Markov illness‐death model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 1532-1551, September.
    810. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.
    811. Ke Sun & Linglong Kong & Hongtu Zhu & Chengchun Shi, 2024. "ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments," Papers 2408.05342, arXiv.org, revised Jan 2025.
    812. Zhihao Zhao & Congyang Zhou, 2025. "A Meta-Learning Approach for Estimating Heterogeneous Treatment Effects Under Hölder Continuity," Mathematics, MDPI, vol. 13(11), pages 1-25, May.
    813. Gozde Sert & Abhishek Chakrabortty & Anirban Bhattacharya, 2025. "Bayesian Semi-supervised Inference via a Debiased Modeling Approach," Papers 2509.17385, arXiv.org.
    814. Philipp Bach & Victor Chernozhukov & Carlos Cinelli & Lin Jia & Sven Klaassen & Nils Skotara & Martin Spindler, 2025. "Sensitivity Analysis for Causal ML: A Use Case at Booking.com," Papers 2510.09109, arXiv.org.
    815. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
    816. David Bruns-Smith & Oliver Dukes & Avi Feller & Elizabeth L. Ogburn, 2023. "Augmented balancing weights as linear regression," Papers 2304.14545, arXiv.org, revised Jun 2024.
    817. Guido Imbens, 2019. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," NBER Working Papers 26104, National Bureau of Economic Research, Inc.
    818. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers 41/17, Institute for Fiscal Studies.
    819. Pedro Carneiro & Sokbae (Simon) Lee & Daniel Wilhelm, 2019. "Optimal Data Collection for Randomized Control Trials," CeMMAP working papers CWP21/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    820. Di Liu, 2022. "Treatment-effects estimation using lasso," 2022 Stata Conference 07, Stata Users Group.
    821. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2021. "Feasible generalized least squares for panel data with cross-sectional and serial correlations," Empirical Economics, Springer, vol. 60(1), pages 309-326, January.
    822. Wang, Man & Yang, Qiuping, 2022. "The heterogeneous treatment effect of low-carbon city pilot policy on stock return: A generalized random forests approach," Finance Research Letters, Elsevier, vol. 47(PA).
    823. Lucas Z. Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org, revised Dec 2025.
    824. Erfan Loghmani, 2025. "Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective," Papers 2506.00152, arXiv.org.
    825. Michael P. Leung & Pantelis Loupos, 2022. "Graph Neural Networks for Causal Inference Under Network Confounding," Papers 2211.07823, arXiv.org, revised Dec 2025.
    826. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.
    827. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression," Papers 2112.14249, arXiv.org, revised May 2025.
    828. Alberto Caron & Gianluca Baio & Ioanna Manolopoulou, 2022. "Estimating individual treatment effects using non‐parametric regression models: A review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1115-1149, July.
    829. Kai Feng & Han Hong & Denis Nekipelov, 2024. "Statistical Inference of Optimal Allocations I: Regularities and their Implications," Papers 2403.18248, arXiv.org, revised Feb 2026.
    830. Sloczynski, Tymon & Uysal, Derya & Wooldridge, Jeffrey M., 2022. "Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment," IZA Discussion Papers 15727, IZA Network @ LISER.
    831. Christopher D. Walker, 2023. "Parametrization, Prior Independence, and the Semiparametric Bernstein-von Mises Theorem for the Partially Linear Model," Papers 2306.03816, arXiv.org, revised Apr 2025.
    832. Jiaming Zeng & Michael F. Gensheimer & Daniel L. Rubin & Susan Athey & Ross D. Shachter, 2022. "Uncovering interpretable potential confounders in electronic medical records," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    833. Johannes Jakubik & Stefan Feuerriegel, 2022. "Data‐driven allocation of development aid toward sustainable development goals: Evidence from HIV/AIDS," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2739-2756, June.

  67. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2017. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Sciences Po Economics Publications (main) hal-03391975, HAL.

    Cited by:

    1. Marcel Klatt & Axel Munk & Yoav Zemel, 2022. "Limit laws for empirical optimal solutions in random linear programs," Annals of Operations Research, Springer, vol. 315(1), pages 251-278, August.
    2. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    3. Gunsilius, Florian F., 2023. "A condition for the identification of multivariate models with binary instruments," Journal of Econometrics, Elsevier, vol. 235(1), pages 220-238.
    4. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Sciences Po Economics Publications (main) hal-03936221, HAL.
    5. Hamel, Andreas H. & Kostner, Daniel, 2018. "Cone distribution functions and quantiles for multivariate random variables," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 97-113.
    6. Jun Yu & Mingyao Ai & Zhiqiang Ye, 2024. "A review on design inspired subsampling for big data," Statistical Papers, Springer, vol. 65(2), pages 467-510, April.
    7. Marc Hallin & Hongjian Shi & Mathias Drton & Fang Han, 2021. "Center-Outward Sign- and Rank-Based Quadrant, Spearman, and Kendall Tests for Multivariate Independence," Working Papers ECARES 2021-27, ULB -- Universite Libre de Bruxelles.
    8. Manuel Arellano & Stéphane Bonhomme, 2019. "Recovering Latent Variables by Matching," Working Papers wp2019_1914, CEMFI.
    9. María Edo & Walter Sosa Escudero & Marcela Svarc, 2021. "A multidimensional approach to measuring the middle class," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(1), pages 139-162, March.
    10. Lixiong Li & Marc Henry, 2022. "Finite Sample Inference in Incomplete Models," Papers 2204.00473, arXiv.org, revised Oct 2025.
    11. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    12. Florian F Gunsilius, 2025. "A primer on optimal transport for causal inference with observational data," Papers 2503.07811, arXiv.org, revised Mar 2025.
    13. Hudecová, Šárka & Šiman, Miroslav, 2022. "Multivariate ranks based on randomized lift-interdirections," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    14. Yanqin Fan & Marc Henry, 2020. "Vector copulas," Papers 2009.06558, arXiv.org, revised Apr 2021.
    15. Marc Hallin & Davide La Vecchia & Hang Liu, 2020. "Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach," Working Papers ECARES 2020-47, ULB -- Universite Libre de Bruxelles.
    16. Kotík, Lukáš & Hlubinka, Daniel, 2017. "A weighted localization of halfspace depth and its properties," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 53-69.
    17. Alberto González-Sanz & Marc Hallin & Bodhisattva Sen, 2023. "Monotone Measure-Preserving Maps in Hilbert Spaces: Existence, Uniqueness, and Stability," Working Papers ECARES 2023-10, ULB -- Universite Libre de Bruxelles.
    18. Olivier Paul Faugeras & Ludger Rüschendorf, 2021. "Functional, randomized and smoothed multivariate quantile regions," Post-Print hal-03352330, HAL.
    19. Marc Hallin & Davide La Vecchia & H Liu, 2019. "Center-Outward R-Estimation for Semiparametric VARMA Models," Working Papers ECARES 2019-25, ULB -- Universite Libre de Bruxelles.
    20. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich towards a “General Generalised Distance”," Working Papers ECARES 2018-12, ULB -- Universite Libre de Bruxelles.
    21. Fan, Yanqin & Henry, Marc, 2023. "Vector copulas," Journal of Econometrics, Elsevier, vol. 234(1), pages 128-150.
    22. Marc Hallin & Daniel Hlubinka & Sarka Hudecova, 2020. "Fully Distribution-free Center-outward Rank Tests for Multiple-output Regression and Manova," Working Papers ECARES 2020-32, ULB -- Universite Libre de Bruxelles.
    23. Jan Bierlant & Sven Buitendag & Eustasio Del Barrio & Marc Hallin, 2019. "Center-Outward Quantiles And The Measurement Of Multivariate Risk," Working Papers ECARES 2019-30, ULB -- Universite Libre de Bruxelles.
    24. Petra Laketa & Stanislav Nagy, 2022. "Halfspace depth for general measures: the ray basis theorem and its consequences," Statistical Papers, Springer, vol. 63(3), pages 849-883, June.
    25. Davy Paindaveine & Germain Van Bever, 2017. "Halfspace Depths for Scatter, Concentration and Shape Matrices," Working Papers ECARES ECARES 2017-19, ULB -- Universite Libre de Bruxelles.
    26. Marc Hallin & Hang Liu, 2022. "Center-outward Rank- and Sign-based VARMA Portmanteau Tests," Working Papers ECARES 2022-27, ULB -- Universite Libre de Bruxelles.
    27. Hudecová, Šárka & Šiman, Miroslav, 2024. "Stochastic hyperplane-based ranks and their use in multivariate portmanteau tests," Journal of Multivariate Analysis, Elsevier, vol. 204(C).
    28. Marc Hallin & Daniel Hlubinka & Sarka Hudecova, 2021. "Efficient Fully Distribution-Free Center-Outward Rank Tests for Multiple-Output Regression and MANOVA," Working Papers ECARES 2021-13, ULB -- Universite Libre de Bruxelles.
    29. Florian Gunsilius, 2018. "Point-identification in multivariate nonseparable triangular models," Papers 1806.09680, arXiv.org.
    30. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 135-146, December.
    31. Mendroš, Erik & Nagy, Stanislav, 2025. "Explicit bivariate simplicial depth," Journal of Multivariate Analysis, Elsevier, vol. 205(C).
    32. Yanqin Fan & Marc Henry & Brendan Pass & Jorge A. Rivero, 2022. "Lorenz map, inequality ordering and curves based on multidimensional rearrangements," Papers 2203.09000, arXiv.org, revised Apr 2024.
    33. Faugeras, Olivier P. & Rüschendorf, Ludger, 2021. "Functional, randomized and smoothed multivariate quantile regions," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    34. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    35. Marc Hallin & H Lui & Thomas Verdebout, 2022. "Nonparametric Measure-transportation-based Methods for Directional Data," Working Papers ECARES 2022-18, ULB -- Universite Libre de Bruxelles.
    36. Marc Hallin & Gilles Mordant, 2021. "On the Finite-Sample Performance of Measure Transportation-Based Multivariate Rank Tests," Working Papers ECARES 2021-24, ULB -- Universite Libre de Bruxelles.
    37. Hongjian Shi & Marc Hallin & Mathias Drton & Fang Han, 2020. "Rate-Optimality of Consistent Distribution-Free Tests of Independence Based on Center-Outward Ranks and Signs," Working Papers ECARES 2020-23, ULB -- Universite Libre de Bruxelles.
    38. Eustasio Del Barrio & Juan Cuesta Albertos & Marc Hallin & Carlos Matran, 2018. "Smooth Cyclically Monotone Interpolation and Empirical Center-Outward Distribution Functions," Working Papers ECARES 2018-15, ULB -- Universite Libre de Bruxelles.
    39. Eustasio del Barrio & Alberto González-Sanz & Marc Hallin, 2022. "Nonparametric Multiple-Output Center-Outward Quantile Regression," Working Papers ECARES 2022-10, ULB -- Universite Libre de Bruxelles.
    40. Alfred Galichon & Bernard Salani'e, 2021. "Cupid's Invisible Hand: Social Surplus and Identification in Matching Models," Papers 2106.02371, arXiv.org, revised Jan 2023.
    41. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.
    42. Florian Gunsilius & Susanne M. Schennach, 2019. "Independent nonlinear component analysis," CeMMAP working papers CWP46/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  68. Victor Chernozhukov & Alfred Galichon & Marc Henry & Brendan Pass, 2017. "Identification of hedonic equilibrium and nonseparable simultaneous equations," Papers 1709.09570, arXiv.org, revised Feb 2021.

    Cited by:

    1. Gunsilius, Florian F., 2023. "A condition for the identification of multivariate models with binary instruments," Journal of Econometrics, Elsevier, vol. 235(1), pages 220-238.
    2. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Sciences Po Economics Publications (main) hal-03936221, HAL.
    3. Florian F Gunsilius, 2025. "A primer on optimal transport for causal inference with observational data," Papers 2503.07811, arXiv.org, revised Mar 2025.
    4. Paula Calvo & Ilse Lindenlaub & Ana Reynoso, 2024. "Marriage Market and Labour Market Sorting," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(6), pages 3316-3361.
    5. Tongseok Lim, 2023. "Replication of financial derivatives under extreme market models given marginals," Papers 2307.00807, arXiv.org.
    6. William Torous & Florian Gunsilius & Philippe Rigollet, 2021. "An Optimal Transport Approach to Estimating Causal Effects via Nonlinear Difference-in-Differences," Papers 2108.05858, arXiv.org, revised Mar 2024.
    7. Steven C. Bourassa & Martijn I. Dröes & Martin Hoesli, 2024. "Housing Market Segmentation: A Finite Mixture Approach," De Economist, Springer, vol. 172(4), pages 291-337, December.
    8. Bishop, Kelly C. & Timmins, Christopher, 2019. "Estimating the marginal willingness to pay function without instrumental variables," Journal of Urban Economics, Elsevier, vol. 109(C), pages 66-83.
    9. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.
    10. Aguiar, Victor H. & Kashaev, Nail & Allen, Roy, 2023. "Prices, profits, proxies, and production," Journal of Econometrics, Elsevier, vol. 235(2), pages 666-693.
    11. Alfred Galichon, 2021. "The unreasonable effectiveness of optimal transport in economics," Papers 2107.04700, arXiv.org.

  69. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers 61/17, Institute for Fiscal Studies.

    Cited by:

    1. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, IZA Network @ LISER.
    2. Alex Armand & Britta Augsburg & Antonella Bancalari, 2021. "Coordination and the poor maintenance trap: an experiment on public infrastructure in India," IFS Working Papers W21/16, Institute for Fiscal Studies.
    3. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised May 2025.
    4. Alex Armand & Britta Augsburg & Antonella Bancalari & Maitreesh Ghatak, 2023. "Public service delivery, exclusion and externalities: theory and experimental evidence from India," NOVAFRICA Working Paper Series wp2303, Universidade Nova de Lisboa, Nova School of Business and Economics, NOVAFRICA.
    5. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2021. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Working Paper Series 2614, European Central Bank.
    6. Eoghan O'Neill & Melvyn Weeks, 2018. "Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes," Papers 1810.09179, arXiv.org, revised Oct 2019.
    7. Paul B. Ellickson & Wreetabrata Kar & James C. Reeder, 2023. "Estimating Marketing Component Effects: Double Machine Learning from Targeted Digital Promotions," Marketing Science, INFORMS, vol. 42(4), pages 704-728, July.
    8. Daniel J. Lewis & Davide Melcangi & Laura Pilossoph, 2019. "Latent Heterogeneity in the Marginal Propensity to Consume," Staff Reports 902, Federal Reserve Bank of New York.
    9. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org, revised Oct 2025.
    10. Stephen Coussens & Jann Spiess, 2021. "Improving Inference from Simple Instruments through Compliance Estimation," Papers 2108.03726, arXiv.org.
    11. Matias D. Cattaneo & Max H. Farrell & Yingjie Feng, 2018. "Large Sample Properties of Partitioning-Based Series Estimators," Papers 1804.04916, arXiv.org, revised Jun 2019.

  70. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2017. "Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," Papers 1712.04802, arXiv.org, revised Oct 2023.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Christiansen, T. & Weeks, M., 2020. "Distributional Aspects of Microcredit Expansions," Cambridge Working Papers in Economics 20100, Faculty of Economics, University of Cambridge.
    3. Arkadiusz Szyd{l}owski, 2025. "Testing Shape Restrictions with Continuous Treatment: A Transformation Model Approach," Papers 2506.08914, arXiv.org, revised Dec 2025.
    4. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    5. Kayo MURAKAMI & Hideki SHIMADA & Yoshiaki USHIFUSA & Takanori IDA, 2020. "Heterogeneous Treatment Effects of Nudge and Rebate:Causal Machine Learning in a Field Experiment on Electricity Conservation," Discussion papers e-20-003, Graduate School of Economics , Kyoto University.
    6. Vlassopoulos, Michael & Siddique, Abu & Rahman, Tabassum & Pakrashi, Debayan & Islam, Asadul & Ahmed, Firoz, 2021. "Improving Women's Mental Health during a Pandemic," IZA Discussion Papers 14786, IZA Network @ LISER.
    7. Susan Athey & Herman Brunborg & Tianyu Du & Ayush Kanodia & Keyon Vafa, 2024. "LABOR-LLM: Language-Based Occupational Representations with Large Language Models," Papers 2406.17972, arXiv.org, revised Jan 2026.
    8. Dinarte Diaz,Lelys Ileana & Ravindran,Saravana & Shah,Manisha & Powers,Shawn Michael & Baker-Henningham,Helen, 2023. "Violent Discipline and Parental Behavior : Short- and Medium-term Effects of VirtualParenting Support to Caregivers," Policy Research Working Paper Series 10507, The World Bank.
    9. Girma, Sourafel & Paton, David, 2024. "Using double-debiased machine learning to estimate the impact of Covid-19 vaccination on mortality and staff absences in elderly care homes," European Economic Review, Elsevier, vol. 170(C).
    10. Bassi, Marina & Besbas,Bruno & Dinarte, Lelys & Ravindran,Saravana & Reynoso,Ana, 2024. "From Access to Achievement : The Primary School-Age Impacts of an At-Scale Preschool Construction Program in Highly Deprived Communities," Policy Research Working Paper Series 10814, The World Bank.
    11. Hoong, Ruru, 2021. "Self control and smartphone use: An experimental study of soft commitment devices," European Economic Review, Elsevier, vol. 140(C).
    12. Patrick Rehill, 2024. "How do applied researchers use the Causal Forest? A methodological review of a method," Papers 2404.13356, arXiv.org, revised Dec 2024.
    13. Andrew Baker & Brantly Callaway & Scott Cunningham & Andrew Goodman-Bacon & Pedro H. C. Sant'Anna, 2025. "Difference-in-Differences Designs: A Practitioner's Guide," Papers 2503.13323, arXiv.org, revised Jun 2025.
    14. Emerick, Kyle & Kelley, Erin & De Janvry, Alain & Sadoulet, Elisabeth, 2019. "Endogenous Information Sharing and the Gains from Using Network Information to Maximize Technology Adoption," CEPR Discussion Papers 13507, C.E.P.R. Discussion Papers.
    15. Bertrand,Marianne & Crepon,Bruno Jacques Jean Philippe & Marguerie,Alicia Charlene & Premand,Patrick, 2021. "Do Workfare Programs Live Up to Their Promises ? Experimental Evidence from Côte d’Ivoire," Policy Research Working Paper Series 9611, The World Bank.
    16. Strittmatter, Anthony, 2019. "What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," GLO Discussion Paper Series 336, Global Labor Organization (GLO).
    17. Chaisemartin, Clement de & Navarrete, Nicolas, 2019. "The direct and spillover effects of a mental health program for disruptive students," CAGE Online Working Paper Series 401, Competitive Advantage in the Global Economy (CAGE).
    18. Siddique, Abu & Islam, Asad & Mozumder, Tanvir Ahmed & Rahman, Tabassum & Shatil, Tanvir, 2022. "Forced Displacement, Mental Health, and Child Development: Evidence from the Rohingya Refugees," SocArXiv b4fc7, Center for Open Science.
    19. Suarez Castillo, Milena & Benatia, David & Thi, Christine Le, 2025. "Air pollution and children’s health inequalities," Journal of Environmental Economics and Management, Elsevier, vol. 131(C).
    20. Pramod Kumar Sur, 2021. "Understanding Vaccine Hesitancy: Empirical Evidence from India," Papers 2103.02909, arXiv.org, revised Feb 2023.
    21. Bruno Fava, 2024. "Predicting the Distribution of Treatment Effects via Covariate-Adjustment, with an Application to Microcredit," Papers 2407.14635, arXiv.org, revised Jul 2025.
    22. Carcillo, Stéphane & Valfort, Marie-Anne & Vergara Merino, Pedro, 2025. "Combating LGBTphobia in Schools: Evidence from a Field Experiment in France," IZA Discussion Papers 17683, IZA Network @ LISER.

  71. Victor Chernozhukov & Iv'an Fern'andez-Val & Whitney Newey, 2017. "Nonseparable Multinomial Choice Models in Cross-Section and Panel Data," Papers 1706.08418, arXiv.org, revised May 2018.

    Cited by:

    1. Bhattacharya, Debopam, 2025. "Integrability and identification in multinomial choice models," Journal of Economic Theory, Elsevier, vol. 223(C).
    2. Cizek, Pavel & Sadikoglu, Serhan, 2022. "Nonseparable Panel Models with Index Structure and Correlated Random Effects," Discussion Paper 2022-009, Tilburg University, Center for Economic Research.
    3. Wang, Ao, 2023. "Sieve BLP: A semi-nonparametric model of demand for differentiated products," Journal of Econometrics, Elsevier, vol. 235(2), pages 325-351.
    4. Victor Chernozhukov & Jerry A. Hausman & Whitney K. Newey, 2019. "Demand Analysis with Many Prices," NBER Working Papers 26424, National Bureau of Economic Research, Inc.
    5. Wang, Ao, 2020. "Identifying the Distribution of Random Coefficients in BLP Demand Models Using One Single Variation in Product Characteristics," The Warwick Economics Research Paper Series (TWERPS) 1304, University of Warwick, Department of Economics.
    6. Laurent Davezies & Xavier D’Haultfoeuille & Louise Laage, 2025. "Identification and Estimation of Average Causal Effects in Fixed Effects Logit Models," Working Papers 2025-02, Center for Research in Economics and Statistics.

  72. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.

    Cited by:

    1. Matthew A. Masten & Alexandre Poirier, 2018. "Interpreting Quantile Independence," Papers 1804.10957, arXiv.org.
    2. Matthew A Masten & Alexandre Poirier, 2023. "Choosing exogeneity assumptions in potential outcome models," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 327-349.
    3. Li‐Pang Chen, 2024. "Estimation of Graphical Models: An Overview of Selected Topics," International Statistical Review, International Statistical Institute, vol. 92(2), pages 194-245, August.
    4. Hossein Alidaee & Eric Auerbach & Michael P. Leung, 2020. "Recovering Network Structure from Aggregated Relational Data using Penalized Regression," Papers 2001.06052, arXiv.org.

  73. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "hdm: High-Dimensional Metrics," Papers 1608.00354, arXiv.org.

    Cited by:

    1. Daniels, David P. & Zlatev, Julian J., 2019. "Choice architects reveal a bias toward positivity and certainty," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 132-149.
    2. Godzinski, Alexandre & Suarez Castillo, Milena, 2021. "Disentangling the effects of air pollutants with many instruments," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    3. Hannes Wallimann & David Imhof & Martin Huber, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers 2004.05629, arXiv.org.
    4. Alexandre Belloni & Victor Chernozhukov & Ivan Fern'andez-Val & Christian Hansen, 2013. "Program Evaluation and Causal Inference with High-Dimensional Data," Papers 1311.2645, arXiv.org, revised Jan 2018.
    5. Ismael Mourifié, 2019. "A marriage matching function with flexible spillover and substitution patterns," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 67(2), pages 421-461, March.
    6. Altman, Edward I. & Balzano, Marco & Giannozzi, Alessandro & Srhoj, Stjepan, 2022. "Revisiting SME default predictors: The Omega Score," GLO Discussion Paper Series 1207, Global Labor Organization (GLO).
    7. Stefan Seifert & Marica Valente, 2018. "An Offer that you Can't Refuse? Agrimafias and Migrant Labor on Vineyards in Southern Italy," Discussion Papers of DIW Berlin 1735, DIW Berlin, German Institute for Economic Research.
    8. Selina Gangl & Martin Huber, 2021. "From homemakers to breadwinners? How mandatory kindergarten affects maternal labour market outcomes," Papers 2111.14524, arXiv.org, revised Mar 2025.
    9. Gangl, Selina & Huber, Martin, 2021. "From homemakers to breadwinners? How mandatory kindergarten affects maternal labour market attachment," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203636, Verein für Socialpolitik / German Economic Association, revised 2021.
    10. Elena Denisova-Schmidt & Martin Huber & Elvira Leontyeva & Anna Solovyeva, 2021. "Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students," Empirical Economics, Springer, vol. 60(4), pages 1661-1684, April.
    11. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 685-719, December.
    12. Pawel Dlotko & Simon Rudkin & Wanling Qiu, 2019. "Topologically Mapping the Macroeconomy," Papers 1911.10476, arXiv.org.
    13. Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
    14. Sullivan Hu'e & S'ebastien Laurent & Ulrich Aiounou & Emmanuel Flachaire, 2025. "Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso," Papers 2511.21257, arXiv.org.
    15. Fergusson Leopoldo & Natalia Garbiras-Díaz & Michael Weintraub, 2026. "Accents as Capital," Documentos CEDE 2026-1, Universidad de los Andes, Facultad de Economía, CEDE.
    16. Marica Valente & Timm Gries & Lorenzo Trapani, 2023. "Informal employment from migration shocks," Working Papers 2023-09, Faculty of Economics and Statistics, Universität Innsbruck.
    17. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    18. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2019. "Valid simultaneous inference in high-dimensional settings (with the HDM package for R)," CeMMAP working papers CWP30/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    20. Harold D. Chiang, 2018. "Many Average Partial Effects: with An Application to Text Regression," Papers 1812.09397, arXiv.org, revised Jan 2022.
    21. Simone Maxand & Hend Sallam, 2024. "Local Fiscal Effects of Immigration in Germany," CESifo Working Paper Series 11162, CESifo.
    22. Abadie, Alberto & Gu, Jiaying & Shen, Shu, 2024. "Instrumental variable estimation with first-stage heterogeneity," Journal of Econometrics, Elsevier, vol. 240(2).

  74. Guillaume Carlier & Victor Chernozhukov & Alfred Galichon, 2016. "Vector Quantile Regression: An Optimal Transport Approach," Sciences Po Economics Publications (main) hal-03567920, HAL.

    Cited by:

    1. Florian Gunsilius & Susanne M. Schennach, 2017. "A nonlinear principal component decomposition," CeMMAP working papers CWP16/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Kobus, Martyna & Kapera, Marek & Maasoumi, Esfandiar, 2024. "Gap in many dimensions: Application to gender," Labour Economics, Elsevier, vol. 89(C).
    3. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    4. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Sciences Po Economics Publications (main) hal-03936221, HAL.
    5. Yang, Xuzhi & Wang, Tengyao, 2024. "Multiple-output composite quantile regression through an optimal transport lens," LSE Research Online Documents on Economics 125589, London School of Economics and Political Science, LSE Library.
    6. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    7. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Transmission of US and EU Economic Policy Uncertainty Shock to Asian Economies in Bad and Good Times," IZA Discussion Papers 13274, IZA Network @ LISER.
    8. Camehl, Annika & Fok, Dennis & Gruber, Kathrin, 2025. "On superlevel sets of conditional densities and multivariate quantile regression," Journal of Econometrics, Elsevier, vol. 249(PA).
    9. Gabriel Montes-Rojas & Nicolás Bertholet, 2022. "When Are Devaluations More Contractionary? A Quantile Var Estimation For Argentina," Documentos de trabajo del Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET) 2022-71, Universidad de Buenos Aires, Facultad de Ciencias Económicas, Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET).
    10. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org, revised Feb 2025.
    11. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2017. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Post-Print hal-03391975, HAL.
    12. Nadja Klein & Thomas Kneib, 2020. "Directional bivariate quantiles: a robust approach based on the cumulative distribution function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 225-260, June.
    13. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    14. Florian F Gunsilius, 2025. "A primer on optimal transport for causal inference with observational data," Papers 2503.07811, arXiv.org, revised Mar 2025.
    15. Richard Blundell & Dennis Kristensen & Rosa Matzkin, 2017. "Individual counterfactuals with multidimensional unobserved heterogeneity," CeMMAP working papers CWP60/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Tongseok Lim, 2023. "Replication of financial derivatives under extreme market models given marginals," Papers 2307.00807, arXiv.org.
    17. Schennach, Susanne M., 2020. "Mismeasured and unobserved variables," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 487-565, Elsevier.
    18. Richard Spady & Sami Stouli, 2012. "Dual Regression," Papers 1210.6958, arXiv.org, revised Sep 2018.
    19. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich towards a “General Generalised Distance”," Working Papers ECARES 2018-12, ULB -- Universite Libre de Bruxelles.
    21. Nadja Klein & Torsten Hothorn & Luisa Barbanti & Thomas Kneib, 2022. "Multivariate conditional transformation models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 116-142, March.
    22. Walter W. Zhang & Sanjog Misra, 2022. "Coarse Personalization," Papers 2204.05793, arXiv.org, revised Jun 2025.
    23. Daouia, Abdelaati & Paindaveine, Davy, 2019. "Multivariate Expectiles, Expectile Depth and Multiple-Output Expectile Regression," TSE Working Papers 19-1022, Toulouse School of Economics (TSE), revised Feb 2023.
    24. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 135-146, December.
    25. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    26. Agarwal, Gaurav & Tu, Wei & Sun, Ying & Kong, Linglong, 2022. "Flexible quantile contour estimation for multivariate functional data: Beyond convexity," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    27. Marc Hallin & Gilles Mordant, 2021. "On the Finite-Sample Performance of Measure Transportation-Based Multivariate Rank Tests," Working Papers ECARES 2021-24, ULB -- Universite Libre de Bruxelles.
    28. Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling and Estimation," Papers 2206.14275, arXiv.org, revised Jan 2025.
    29. Ruodu Wang & Zhenyuan Zhang, 2022. "Simultaneous Optimal Transport," Papers 2201.03483, arXiv.org, revised Dec 2024.
    30. Daniel Hlubinka & Lukáš Kotík & Miroslav Šiman, 2022. "Multivariate quantiles with both overall and directional probability interpretation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1586-1604, December.
    31. Eustasio del Barrio & Alberto González-Sanz & Marc Hallin, 2022. "Nonparametric Multiple-Output Center-Outward Quantile Regression," Working Papers ECARES 2022-10, ULB -- Universite Libre de Bruxelles.
    32. Montes-Rojas, Gabriel, 2017. "Reduced form vector directional quantiles," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 20-30.
    33. Sulkhan Chavleishvili & Simone Manganelli, 2024. "Forecasting and stress testing with quantile vector autoregression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 66-85, January.
    34. Donald Lien & Zijun Wang, 2019. "Quantile information share," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(1), pages 38-55, January.
    35. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.
    36. Carlier, Guillaume & Chernozhukov, Victor & Galichon, Alfred, 2017. "Vector quantile regression beyond the specified case," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 96-102.
    37. Alfred Galichon, 2021. "The unreasonable effectiveness of optimal transport in economics," Papers 2107.04700, arXiv.org.
    38. Hubner, Stefan, 2023. "Identification of unobserved distribution factors and preferences in the collective household model," Journal of Econometrics, Elsevier, vol. 234(1), pages 301-326.
    39. Florian Gunsilius & Susanne M. Schennach, 2019. "Independent nonlinear component analysis," CeMMAP working papers CWP46/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  75. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Comparison and anti-concentration bounds for maxima of Gaussian random vectors," CeMMAP working papers 40/16, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Liang, Chong & Schienle, Melanie, 2019. "Determination of vector error correction models in high dimensions," Working Paper Series in Economics 124, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    3. Andrews, Donald W.K. & Shi, Xiaoxia, 2017. "Inference based on many conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 196(2), pages 275-287.
    4. Demian Pouzo, 2014. "Bootstrap Consistency for Quadratic Forms of Sample Averages with Increasing Dimension," Papers 1411.2701, arXiv.org, revised Aug 2015.

  76. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2016. "Locally Robust Semiparametric Estimation," Papers 1608.00033, arXiv.org, revised Aug 2020.

    Cited by:

    1. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    2. Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
    3. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    5. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    6. Phillip Heiler & Michael C. Knaus, 2025. "Heterogeneity Analysis with Heterogeneous Treatments," Papers 2507.01517, arXiv.org, revised Feb 2026.
    7. Cordier, J.; & Salvi, I.; & Steinbeck, V.; & Geissler, A.; & Vogel, J.;, 2023. "Is rapid recovery always the best recovery? - Developing a machine learning approach for optimal assignment rules under capacity constraints for knee replacement patients," Health, Econometrics and Data Group (HEDG) Working Papers 23/08, HEDG, c/o Department of Economics, University of York.
    8. Yi Zhang & Eli Ben-Michael & Kosuke Imai, 2022. "Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs," Papers 2208.13323, arXiv.org, revised Sep 2024.
    9. Du, Zaichao & Escanciano, Juan Carlos & Zhu, Guangwei, 2023. "The case for CASE: Estimating heterogeneous systemic effects," Journal of Banking & Finance, Elsevier, vol. 157(C).
    10. Lee, Myoung-jae & Han, Chirok, . "Ordinary least squares and instrumental-variables estimators for any outcome and heterogeneity," Gospodarka Narodowa-The Polish Journal of Economics, Szkoła Główna Handlowa w Warszawie / SGH Warsaw School of Economics, vol. 2024(1).
    11. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    12. Lewbel, Arthur & Choi, Jin Young & Zhou, Zhuzhu, 2023. "Over-identified Doubly Robust identification and estimation," Journal of Econometrics, Elsevier, vol. 235(1), pages 25-42.
    13. Abhinandan Dalal & Patrick Blobaum & Shiva Kasiviswanathan & Aaditya Ramdas, 2024. "Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters," Papers 2408.09598, arXiv.org, revised Sep 2024.
    14. Santiago Pereda Fernández, 2019. "Identification and estimation of triangular models with a binary treatment," Temi di discussione (Economic working papers) 1210, Bank of Italy, Economic Research and International Relations Area.
    15. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    16. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    17. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    18. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    19. Jacob, Daniel, 2020. "Cross-Fitting and Averaging for Machine Learning Estimation of Heterogeneous Treatment Effects," IRTG 1792 Discussion Papers 2020-014, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    20. Chaudhuri, Saraswata & Renault, Eric, 2023. "Efficient estimation of regression models with user-specified parametric model for heteroskedasticty," The Warwick Economics Research Paper Series (TWERPS) 1473, University of Warwick, Department of Economics.
    21. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org, revised Mar 2025.
    22. Daniel Jacob, 2021. "CATE meets ML -- The Conditional Average Treatment Effect and Machine Learning," Papers 2104.09935, arXiv.org, revised Apr 2021.
    23. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    24. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    25. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    26. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    27. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Jun 2025.
    28. Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.
    29. Greg Lewis & Vasilis Syrgkanis, 2020. "Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation," Papers 2002.07285, arXiv.org, revised Jun 2021.
    30. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Jul 2025.
    31. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    32. Zhengyuan Zhou & Susan Athey & Stefan Wager, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Papers 1810.04778, arXiv.org, revised Nov 2018.
    33. Thomas H. J{o}rgensen, 2020. "Sensitivity to Calibrated Parameters," Papers 2004.12100, arXiv.org, revised Mar 2021.
    34. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2021. "Linear IV regression estimators for structural dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 222(1), pages 778-804.
    35. Jeonghwan Lee & Cong Ma, 2025. "Learning bounds for doubly-robust covariate shift adaptation," Papers 2511.11003, arXiv.org.
    36. Maximilian Blesch & Philipp Eisenhauer, 2021. "Robust decision-making under risk and ambiguity," Papers 2104.12573, arXiv.org, revised Oct 2021.
    37. Zequn Jin & Gaoqian Xu & Xi Zheng & Yahong Zhou, 2025. "Policy Learning under Unobserved Confounding: A Robust and Efficient Approach," Papers 2507.20550, arXiv.org.
    38. Stéphane Bonhomme & Martin Weidner, 2020. "Minimizing Sensitivity to Model Misspecification," CeMMAP working papers CWP37/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    39. Xingyu Chen & Lin Liu & Rajarshi Mukherjee, 2024. "Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptotics," Papers 2408.06103, arXiv.org, revised May 2025.
    40. Rahul Singh, 2020. "Kernel Methods for Unobserved Confounding: Negative Controls, Proxies, and Instruments," Papers 2012.10315, arXiv.org, revised Mar 2023.
    41. Dylan J. Foster & Vasilis Syrgkanis, 2019. "Orthogonal Statistical Learning," Papers 1901.09036, arXiv.org, revised Jun 2023.
    42. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    43. Matias D. Cattaneo & Michael Jansson, 2018. "Kernel†Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency," Econometrica, Econometric Society, vol. 86(3), pages 955-995, May.
    44. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series 9037, CESifo.
    45. Manu Navjeevan & Rodrigo Pinto & Andres Santos, 2023. "Identification and Estimation in a Class of Potential Outcomes Models," Papers 2310.05311, arXiv.org.
    46. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised May 2025.
    47. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    48. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    49. Stéphane Bonhomme & Martin Weidner, 2022. "Minimizing sensitivity to model misspecification," Quantitative Economics, Econometric Society, vol. 13(3), pages 907-954, July.
    50. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers CWP41/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    51. Minkyung Kim & K. Sudhir & Kosuke Uetake, 2022. "A Structural Model of a Multitasking Salesforce: Incentives, Private Information, and Job Design," Management Science, INFORMS, vol. 68(6), pages 4602-4630, June.
    52. Mengshan Xu & Taisuke Otsu, 2022. "Isotonic propensity score matching," Papers 2207.08868, arXiv.org, revised Jan 2025.
    53. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    54. Bertille Antoine & Xiaolin Sun, 2020. "Partially Linear Models with Endogeneity: a conditional moment based approach," Discussion Papers dp20-06, Department of Economics, Simon Fraser University.
    55. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    56. Rahul Singh, 2021. "Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension," Papers 2102.11076, arXiv.org, revised Jul 2024.
    57. Karun Adusumilli & Dita Eckardt, 2019. "Temporal-Difference estimation of dynamic discrete choice models," Papers 1912.09509, arXiv.org, revised Dec 2022.
    58. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    59. Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2019. "Two-Step Estimation and Inference with Possibly Many Included Covariates," University of California at San Diego, Economics Working Paper Series qt86c7x315, Department of Economics, UC San Diego.
    60. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2025. "Robust Semiparametric Inference for Bayesian Additive Regression Trees," Papers 2509.24634, arXiv.org, revised Oct 2025.
    61. Cattaneo, Matias D. & Jansson, Michael & Ma, Xinwei, 2024. "Local regression distribution estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    62. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    63. Phillip Heiler & Asbj{o}rn Kaufmann & Bezirgen Veliyev, 2024. "Treatment Evaluation at the Intensive and Extensive Margins," Papers 2412.11179, arXiv.org.
    64. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    65. Sokbae Lee & Ryo Okui & Yoon-Jae Whang, 2016. "Doubly Robust Uniform Confidence Band for the Conditional Average Treatment Effect Function," Papers 1601.02801, arXiv.org, revised Oct 2016.
    66. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    67. Chen, Jiafeng & Ritzwoller, David M., 2023. "Semiparametric estimation of long-term treatment effects," Journal of Econometrics, Elsevier, vol. 237(2).
    68. Taisuke Otsu & Mengshan Xu, 2022. "Isotonic propensity score matching," STICERD - Econometrics Paper Series 623, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    69. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    70. Matias D. Cattaneo & Michael Jansson, 2019. "Average Density Estimators: Efficiency and Bootstrap Consistency," Papers 1904.09372, arXiv.org, revised Dec 2020.
    71. Undral Byambadalai & Tomu Hirata & Tatsushi Oka & Shota Yasui, 2025. "Beyond the Average: Distributional Causal Inference under Imperfect Compliance," Papers 2509.15594, arXiv.org, revised Oct 2025.
    72. Christopher D. Walker, 2024. "Semiparametric Bayesian Inference for a Conditional Moment Equality Model," Papers 2410.16017, arXiv.org.
    73. Riccardo D'Adamo, 2021. "Orthogonal Policy Learning Under Ambiguity," Papers 2111.10904, arXiv.org, revised Dec 2022.
    74. Anish Agarwal & Rahul Singh, 2021. "Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy," Papers 2107.02780, arXiv.org, revised Feb 2024.
    75. Nathan Kallus, 2023. "Treatment Effect Risk: Bounds and Inference," Management Science, INFORMS, vol. 69(8), pages 4579-4590, August.
    76. Rahul Singh & Liyang Sun, 2019. "Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics," Papers 1909.05244, arXiv.org, revised Dec 2022.
    77. Ben Deaner, 2021. "Many Proxy Controls," Papers 2110.03973, arXiv.org.
    78. Mello, Ursula & Nybom, Martin & Stuhler, Jan, 2025. "A Lifecycle Estimator of Intergenerational Income Mobility," IZA Discussion Papers 18329, IZA Network @ LISER.
    79. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    80. Jooyoung Cha & Harold D. Chiang & Yuya Sasaki, 2021. "Inference in high-dimensional regression models without the exact or $L^p$ sparsity," Papers 2108.09520, arXiv.org, revised Dec 2022.
    81. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2021. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees," Papers 2105.15197, arXiv.org, revised Oct 2022.
    82. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    83. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    84. Neng-Chieh Chang, 2018. "Semiparametric Difference-in-Differences with Potentially Many Control Variables," Papers 1812.10846, arXiv.org, revised Jan 2019.
    85. Enoch H. Kang & Hema Yoganarasimhan & Lalit Jain, 2025. "An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model," Papers 2502.14131, arXiv.org, revised Jan 2026.
    86. Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2022. "Weighted-average quantile regression," Papers 2203.03032, arXiv.org.
    87. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    88. Keith Battocchi & Eleanor Dillon & Maggie Hei & Greg Lewis & Miruna Oprescu & Vasilis Syrgkanis, 2021. "Estimating the Long-Term Effects of Novel Treatments," Papers 2103.08390, arXiv.org, revised Feb 2022.
    89. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
    90. Semenova, Vira, 2025. "Generalized Lee bounds," Journal of Econometrics, Elsevier, vol. 251(C).
    91. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    92. Liu, Yanghui & Li, Yehua & Carroll, Raymond J. & Wang, Naisyin, 2022. "Predictive functional linear models with diverging number of semiparametric single-index interactions," Journal of Econometrics, Elsevier, vol. 230(2), pages 221-239.
    93. Wei, Bo & Tan, Kean Ming & He, Xuming, 2024. "Estimation of complier expected shortfall treatment effects with a binary instrumental variable," Journal of Econometrics, Elsevier, vol. 238(2).
    94. Florian Gunsilius & Lonjezo Sithole, 2025. "Nonparametric Testability of Slutsky Symmetry," Papers 2505.05603, arXiv.org, revised Feb 2026.
    95. Zhan Gao & Ji Hyung Lee & Ziwei Mei & Zhentao Shi, 2024. "LASSO Inference for High Dimensional Predictive Regressions," Papers 2409.10030, arXiv.org, revised Jan 2026.
    96. Krantz, Sebastian, 2024. "Mapping Africa's infrastructure potential with geospatial big data and causal ML," Kiel Working Papers 2276, Kiel Institute for the World Economy.
    97. Jean-Pierre Florens & Elia Lapenta, 2024. "Partly linear instrumental variables regressions without smoothing on the instruments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(3), pages 897-920, September.
    98. Mert Demirer & Vasilis Syrgkanis & Greg Lewis & Victor Chernozhukov, 2019. "Semi-Parametric Efficient Policy Learning with Continuous Actions," CeMMAP working papers CWP34/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    99. Rahul Singh & Liyuan Xu & Arthur Gretton, 2021. "Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves," Papers 2111.03950, arXiv.org, revised Mar 2025.
    100. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    101. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2018. "Deep Neural Networks for Estimation and Inference," Papers 1809.09953, arXiv.org, revised Sep 2019.
    102. Hidehiko Ichimura & Whitney K. Newey, 2017. "The influence function of semiparametric estimators," CeMMAP working papers CWP06/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    103. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
    104. Ouyang, Fu & Yang, Thomas T., 2025. "High dimensional binary choice model with unknown heteroskedasticity or instrumental variables," Journal of Econometrics, Elsevier, vol. 251(C).
    105. Yusuke Narita & Shota Yasui & Kohei Yata, 2020. "Debiased Off-Policy Evaluation for Recommendation Systems," Papers 2002.08536, arXiv.org, revised Aug 2021.
    106. Pietro Emilio Spini, 2021. "Robustness, Heterogeneous Treatment Effects and Covariate Shifts," Papers 2112.09259, arXiv.org, revised Aug 2024.
    107. Kim, Bora & Lee, Myoung-jae, 2024. "Instrument-residual estimator for multi-valued instruments under full monotonicity," Statistics & Probability Letters, Elsevier, vol. 213(C).
    108. Xinwei Ma & Jingshen Wang, 2018. "Robust Inference Using Inverse Probability Weighting," Papers 1810.11397, arXiv.org, revised May 2019.
    109. Tomu Hirata & Undral Byambadalai & Tatsushi Oka & Shota Yasui & Shingo Uto, 2025. "Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks," Papers 2507.07738, arXiv.org.
    110. Maximilian Blesch & Philipp Eisenhauer, 2021. "Robust Decision-Making Under Risk and Ambiguity," ECONtribute Discussion Papers Series 104, University of Bonn and University of Cologne, Germany.
    111. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
    112. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers 41/17, Institute for Fiscal Studies.
    113. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression," Papers 2112.14249, arXiv.org, revised May 2025.
    114. Kai Feng & Han Hong & Denis Nekipelov, 2024. "Statistical Inference of Optimal Allocations I: Regularities and their Implications," Papers 2403.18248, arXiv.org, revised Feb 2026.
    115. Vasilis Syrgkanis & Ruohan Zhan, 2023. "Post Reinforcement Learning Inference," Papers 2302.08854, arXiv.org, revised Oct 2025.

  77. Victor Chernozhukov & Iv'an Fern'andez-Val & Blaise Melly & Kaspar Wuthrich, 2016. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Papers 1608.05142, arXiv.org, revised Aug 2018.

    Cited by:

    1. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    3. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2018. "Distribution regression with sample selection, with an application to wage decompositions in the UK," CeMMAP working papers CWP68/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    5. Valentina Corradi & Daniel Gutknecht, 2019. "Testing for Quantile Sample Selection," Papers 1907.07412, arXiv.org, revised Jan 2021.
    6. Lamarche, Carlos & Shi, Xuan & Young, Derek S., 2024. "Conditional Quantile Functions for Zero-Inflated Longitudinal Count Data," Econometrics and Statistics, Elsevier, vol. 31(C), pages 49-65.
    7. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2024. "Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials," Papers 2407.14074, arXiv.org, revised Jan 2025.
    8. Victor Chernozhukov & Iv'an Fern'andez-Val & Martin Weidner, 2018. "Network and Panel Quantile Effects Via Distribution Regression," Papers 1803.08154, arXiv.org, revised Jun 2020.
    9. Chen, Songnian & Liu, Nianqing & Zhang, Hanghui, 2025. "Distribution regression with censored selection," Journal of Econometrics, Elsevier, vol. 251(C).
    10. Ferdi Botha & John P. de New, 2020. "COVID-19 infections, labour market shocks, and subjective well-being," Melbourne Institute Working Paper Series wp2020n14, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    11. Ferdi Botha & John P. de New & Sonja C. de New & David C. Ribar & Nicolás Salamanca, 2020. "COVID-19 labour market shocks and their inequality implications for financial wellbeing," Melbourne Institute Working Paper Series wp2020n15, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    12. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2019. "Distributional conformal prediction," Papers 1909.07889, arXiv.org, revised Aug 2021.
    13. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2023. "Distribution regression with sample selection and UK wage decomposition," CeMMAP working papers 09/23, Institute for Fiscal Studies.
    14. Tomu Hirata & Undral Byambadalai & Tatsushi Oka & Shota Yasui & Shingo Uto, 2025. "Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks," Papers 2507.07738, arXiv.org.

  78. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers 42/16, Institute for Fiscal Studies.

    Cited by:

    1. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    2. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Andres Aradillas-Lopez & Adam Rosen, 2014. "Inference in Ordered Response Games with Complete Information," CeMMAP working papers CWP36/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Andrew Chesher & Adam Rosen, 2016. "Characterizations of identified sets delivered by structural econometric models," CeMMAP working papers CWP44/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Li, Jia & Liao, Zhipeng, 2020. "Uniform nonparametric inference for time series," Journal of Econometrics, Elsevier, vol. 219(1), pages 38-51.
    6. Allen, Roy, 2018. "Testing moment inequalities: Selection versus recentering," Economics Letters, Elsevier, vol. 162(C), pages 124-126.
    7. Andrew Chesher & Adam Rosen, 2014. "Generalized instrumental variable models," CeMMAP working papers 04/14, Institute for Fiscal Studies.
    8. Baris Ata & Alexandre Belloni & Ozan Candogan, 2018. "Latent Agents in Networks: Estimation and Targeting," Papers 1808.04878, arXiv.org, revised Jan 2022.
    9. Chen, Likai & Wang, Weining & Wu, Wei Biao, 2019. "Inference of Break-Points in High-Dimensional Time Series," IRTG 1792 Discussion Papers 2019-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Andrews, Donald W.K. & Shi, Xiaoxia, 2017. "Inference based on many conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 196(2), pages 275-287.
    11. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," Journal of Econometrics, Elsevier, vol. 206(1), pages 57-82.
    12. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," LSE Research Online Documents on Economics 87513, London School of Economics and Political Science, LSE Library.
    13. Christian Bontemps & Thierry Magnac, 2017. "Set identification, moment restrictions, and inference," Post-Print hal-01575813, HAL.
    14. Matias D. Cattaneo & Xinwei Ma & Yusufcan Masatlioglu & Elchin Suleymanov, 2017. "A Random Attention Model," Papers 1712.03448, arXiv.org, revised Aug 2019.
    15. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    16. Andrew Chesher & Adam Rosen, 2018. "Generalized instrumental variable models, methods, and applications," CeMMAP working papers CWP43/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Bontemps, Christian & Kumar, Rohit, 2020. "A geometric approach to inference in set-identified entry games," Journal of Econometrics, Elsevier, vol. 218(2), pages 373-389.

  79. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers 49/16, Institute for Fiscal Studies.

    Cited by:

    1. Horowitz, Joel L. & Rafi, Ahnaf, 2025. "Bootstrap based asymptotic refinements for high-dimensional nonlinear models," Journal of Econometrics, Elsevier, vol. 249(PB).
    2. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    4. Alexandre Belloni & Victor Chernozhukov & Ivan Fern'andez-Val & Christian Hansen, 2013. "Program Evaluation and Causal Inference with High-Dimensional Data," Papers 1311.2645, arXiv.org, revised Jan 2018.
    5. Alexandrov, Alexei & Pittman, Russell & Ukhaneva, Olga, 2018. "Pricing of Complements in the U.S. freight railroads: Cournot versus Coase," MPRA Paper 86279, University Library of Munich, Germany.
    6. Bernard Koch & Tim Sainburg & Pablo Geraldo & Song Jiang & Yizhou Sun & Jacob Gates Foster, 2021. "A Primer on Deep Learning for Causal Inference," Papers 2110.04442, arXiv.org, revised Nov 2023.
    7. Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
    8. Damian Kozbur, 2015. "Testing-Based Forward Model Selection," ECON - Working Papers 283, Department of Economics - University of Zurich, revised Apr 2018.
    9. Alexandrov, Alexei & Pittman, Russell & Ukhaneva, Olga, 2017. "Royalty stacking in the U.S. freight railroads: Cournot vs. Coase," MPRA Paper 78249, University Library of Munich, Germany.
    10. Uyanga Byambaa & Edward Miguel & Michael W. Walker & Samuel Zicheng Wang, 2025. "Beyond Education and Occupation: Unpacking the Large Gender Wage Gap in Kenya," NBER Working Papers 34375, National Bureau of Economic Research, Inc.
    11. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    12. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    13. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2016. "Locally Robust Semiparametric Estimation," Papers 1608.00033, arXiv.org, revised Aug 2020.
    14. Zhengyuan Zhou & Susan Athey & Stefan Wager, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Papers 1810.04778, arXiv.org, revised Nov 2018.
    15. Kueck, Jannis & Luo, Ye & Spindler, Martin & Wang, Zigan, 2023. "Estimation and inference of treatment effects with L2-boosting in high-dimensional settings," Journal of Econometrics, Elsevier, vol. 234(2), pages 714-731.
    16. Lauren Cappiello & Zhiwei Zhang & Changyu Shen & Neel M. Butala & Xinping Cui & Robert W. Yeh, 2021. "Adjusting for population differences using machine learning methods," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 750-769, June.
    17. Yan-Yu Chiou & Mei-Yuan Chen & Jau-er Chen, 2017. "Nonparametric Regression with Multiple Thresholds: Estimation and Inference," Papers 1705.09418, arXiv.org, revised Feb 2018.
    18. Jannis Kueck & Ye Luo & Martin Spindler & Zigan Wang, 2017. "Estimation and Inference of Treatment Effects with $L_2$-Boosting in High-Dimensional Settings," Papers 1801.00364, arXiv.org, revised Jul 2021.
    19. Jiaming Mao & Zhesheng Zheng, 2020. "Structural Regularization," Papers 2004.12601, arXiv.org, revised Jun 2020.
    20. Hansen, Christian & Liao, Yuan, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," MPRA Paper 75313, University Library of Munich, Germany.
    21. Ngueuleweu Tiwang Gildas & Ningaye paul & Fon Dorothy Engwali, 2025. "Do demographic structure conditions sector contribution to economic growth? A machine learning approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(2), pages 2901-2941, February.
    22. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    23. Dmitry Arkhangelsky & Guido W. Imbens, 2019. "The Role of the Propensity Score in Fixed Effect Models," Working Papers wp2019_1905, CEMFI.
    24. Mochen Yang & Edward McFowland III & Gordon Burtch & Gediminas Adomavicius, 2020. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," Papers 2012.10790, arXiv.org.
    25. Jean-Pierre Dubé & Sanjog Misra, 2017. "Personalized Pricing and Consumer Welfare," NBER Working Papers 23775, National Bureau of Economic Research, Inc.
    26. Chiou, Yan-Yu & Chen, Mei-Yuan & Chen, Jau-er, 2018. "Nonparametric regression with multiple thresholds: Estimation and inference," Journal of Econometrics, Elsevier, vol. 206(2), pages 472-514.
    27. Daniel L. Chen & Markus Loecher, 2022. "Mood and the Malleability of Moral Reasoning: The Impact of Irrelevant Factors on Judicial Decisions," Working Papers hal-03864854, HAL.
    28. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    29. Ryo Aruga & Keiichi Goshima & Takashi Chiba, 2022. "CO2 Emissions and Corporate Performance: Japan's Evidence with Double Machine Learning," IMES Discussion Paper Series 22-E-01, Institute for Monetary and Economic Studies, Bank of Japan.
    30. Lester Mackey & Vasilis Syrgkanis & Ilias Zadik, 2017. "Orthogonal Machine Learning: Power and Limitations," Papers 1711.00342, arXiv.org, revised Aug 2018.
    31. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2019. "Feasible Generalized Least Squares for Panel Data with Cross-sectional and Serial Correlations," Papers 1910.09004, arXiv.org, revised Aug 2020.
    32. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    33. Crane-Droesch, Andrew, "undated". "Semiparametric Panel Data Using Neural Networks," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258128, Agricultural and Applied Economics Association.
    34. Thai T. Pham & Yuanyuan Shen, 2017. "A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform," Papers 1706.02795, arXiv.org.

  80. Michael Lipsitz & Alexandre Belloni & Victor Chernozhukov & Iv'an Fern'andez-Val, 2016. "quantreg.nonpar: An R Package for Performing Nonparametric Series Quantile Regression," Papers 1610.08329, arXiv.org.

    Cited by:

    1. Bruneel-Zupanc, Christophe Alain, 2021. "Discrete-Continuous Dynamic Choice Models: Identification and Conditional Choice Probability Estimation," TSE Working Papers 21-1185, Toulouse School of Economics (TSE).
    2. Clarke, Damian & Llorca-Jaña, Manuel & Pailañir, Daniel, 2021. "The Use of Quantile Methods in Economic History," IZA Discussion Papers 14659, IZA Network @ LISER.
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.

  81. Victor Chernozhukov & Iv'an Fern'andez-Val & Tetsuya Kaji, 2016. "Extremal Quantile Regression: An Overview," Papers 1612.06850, arXiv.org, revised Feb 2017.

    Cited by:

    1. Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2020. "Factorisable Multitask Quantile Regression," IRTG 1792 Discussion Papers 2020-004, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Matthew A Masten & Alexandre Poirier, 2023. "Choosing exogeneity assumptions in potential outcome models," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 327-349.
    3. Li, Xuetao & Zhang, Minqi & Xin, Xing & Yang, Chengying & Zhang, Yonghong & Dai, Jianglai, 2025. "Advancing renewable energy innovation through digital technology and supply chain digitization," Energy Economics, Elsevier, vol. 147(C).
    4. D’Haultfœuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2018. "Extremal quantile regressions for selection models and the black–white wage gap," Journal of Econometrics, Elsevier, vol. 203(1), pages 129-142.
    5. Chavleishvili, Sulkhan & Moench, Emanuel, 2025. "Natural disasters as macroeconomic tail risks," Journal of Econometrics, Elsevier, vol. 247(C).
    6. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    7. Yuya Sasaki & Yulong Wang, 2019. "Fixed-k Inference for Conditional Extremal Quantiles," Papers 1909.00294, arXiv.org, revised Jul 2020.
    8. Marian Vavra, 2023. "Bias-Correction in Time Series Quantile Regression Models," Working and Discussion Papers WP 3/2023, Research Department, National Bank of Slovakia.
    9. D'Haultfoeuille, Xavier & Maurel, Arnaud & Qiu, Xiaoyun & Zhang, Yichong, 2019. "Estimating Selection Models without Instrument with Stata," IZA Discussion Papers 12486, IZA Network @ LISER.
    10. Matthias Fischer & Daniel Kraus & Marius Pfeuffer & Claudia Czado, 2017. "Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression," Risks, MDPI, vol. 5(3), pages 1-13, July.
    11. Matthew A. Masten & Alexandre Poirier & Linqi Zhang, 2021. "Assessing Sensitivity to Unconfoundedness: Estimation and Inference," Working Papers gueconwpa~21-21-08, Georgetown University, Department of Economics.

  82. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Central limit theorems and bootstrap in high dimensions," CeMMAP working papers 39/16, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Fabian Dunker & Konstantin Eckle & Katharina Proksch & Johannes Schmidt-Hieber, 2017. "Tests for qualitative features in the random coefficients model," Papers 1704.01066, arXiv.org, revised Mar 2018.
    3. Ebert, Johannes & Spokoiny, Vladimir & Suvorikova, Alexandra, 2018. "Construction of Non-asymptotic Confidence Sets in 2 -Wasserstein Space," IRTG 1792 Discussion Papers 2018-025, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings," CeMMAP working papers CWP38/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers 47/16, Institute for Fiscal Studies.
    6. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 685-719, December.
    7. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers CWP47/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Naumov, A. & Spokoiny, V. & Ulyanovk, V., 2018. "Bootstrap Confidence Sets for Spectral Projectors of Sample Covariance," IRTG 1792 Discussion Papers 2018-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers CWP22/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Zhilova, Mayya, 2015. "Simultaneous likelihood-based bootstrap confidence sets for a large number of models," SFB 649 Discussion Papers 2015-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  83. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings," CeMMAP working papers 38/16, Institute for Fiscal Studies.

    Cited by:

    1. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
    2. Jun Ma & Vadim Marmer & Artyom Shneyerov & Pai Xu, 2019. "Monotonicity-Constrained Nonparametric Estimation and Inference for First-Price Auctions," Papers 1909.12974, arXiv.org.
    3. Arkadiusz Szyd{l}owski, 2025. "Testing Shape Restrictions with Continuous Treatment: A Transformation Model Approach," Papers 2506.08914, arXiv.org, revised Dec 2025.
    4. Steland, Ansgar, 2024. "Flexible nonlinear inference and change-point testing of high-dimensional spectral density matrices," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    5. Jun Ma & Vadim Marmer & Zhengfei Yu, 2021. "Inference on Individual Treatment Effects in Nonseparable Triangular Models," Papers 2107.05559, arXiv.org, revised Feb 2023.
    6. Haoze Hou & Wei Huang & Zheng Zhang, 2025. "Non-parametric Quantile Regression and Uniform Inference with Unknown Error Distribution," Papers 2504.01761, arXiv.org.
    7. Andrii Babii, 2016. "Honest Confidence Sets in Nonparametric IV Regression and Other Ill-Posed Models," Papers 1611.03015, arXiv.org, revised Dec 2020.
    8. Victor Chernozhukov & Iv'an Fern'andez-Val & Martin Weidner, 2018. "Network and Panel Quantile Effects Via Distribution Regression," Papers 1803.08154, arXiv.org, revised Jun 2020.
    9. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    10. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    11. Ma, Jun & Marmer, Vadim & Shneyerov, Artyom, 2019. "Inference for first-price auctions with Guerre, Perrigne, and Vuong’s estimator," Journal of Econometrics, Elsevier, vol. 211(2), pages 507-538.
    12. Shi, Chengchun & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2021. "An online sequential test for qualitative treatment effects," LSE Research Online Documents on Economics 112521, London School of Economics and Political Science, LSE Library.
    13. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2017. "Quantile graphical models: prediction and conditional independence with applications to systemic risk," CeMMAP working papers CWP54/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers 22/17, Institute for Fiscal Studies.
    15. Yuta Koike, 2023. "High-Dimensional Central Limit Theorems for Homogeneous Sums," Journal of Theoretical Probability, Springer, vol. 36(1), pages 1-45, March.
    16. Peccati, Giovanni & Turchi, Nicola, 2023. "The discrepancy between min–max statistics of Gaussian and Gaussian-subordinated matrices," Stochastic Processes and their Applications, Elsevier, vol. 158(C), pages 315-341.
    17. Kato, Kengo & Kurisu, Daisuke, 2020. "Bootstrap confidence bands for spectral estimation of Lévy densities under high-frequency observations," Stochastic Processes and their Applications, Elsevier, vol. 130(3), pages 1159-1205.
    18. Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
    19. Daisuke Kurisu & Taisuke Otsu, 2021. "On linearization of nonparametric deconvolution estimators for repeated measurements model," STICERD - Econometrics Paper Series 615, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    20. Byunghoon Kang, 2019. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Papers 1909.12162, arXiv.org, revised Feb 2020.
    21. Matias D. Cattaneo & Richard K. Crump & Weining Wang, 2023. "Beta-Sorted Portfolios," Staff Reports 1068, Federal Reserve Bank of New York.
    22. Zincenko, Federico, 2024. "Estimation and inference of seller’s expected revenue in first-price auctions," Journal of Econometrics, Elsevier, vol. 241(1).
    23. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    24. Kurisu, Daisuke & Otsu, Taisuke, 2022. "On linearization of nonparametric deconvolution estimators for repeated measurements model," LSE Research Online Documents on Economics 112676, London School of Economics and Political Science, LSE Library.
    25. Hao Dong & Luke Taylor, 2020. "Nonparametric Significance Testing in Measurement Error Models," Departmental Working Papers 2003, Southern Methodist University, Department of Economics.

  84. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "High-Dimensional Metrics in R," Papers 1603.01700, arXiv.org, revised Aug 2016.

    Cited by:

    1. Daniels, David P. & Zlatev, Julian J., 2019. "Choice architects reveal a bias toward positivity and certainty," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 132-149.
    2. Godzinski, Alexandre & Suarez Castillo, Milena, 2021. "Disentangling the effects of air pollutants with many instruments," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    3. Hannes Wallimann & David Imhof & Martin Huber, 2020. "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers 2004.05629, arXiv.org.
    4. Alexandre Belloni & Victor Chernozhukov & Ivan Fern'andez-Val & Christian Hansen, 2013. "Program Evaluation and Causal Inference with High-Dimensional Data," Papers 1311.2645, arXiv.org, revised Jan 2018.
    5. Ismael Mourifié, 2019. "A marriage matching function with flexible spillover and substitution patterns," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 67(2), pages 421-461, March.
    6. Altman, Edward I. & Balzano, Marco & Giannozzi, Alessandro & Srhoj, Stjepan, 2022. "Revisiting SME default predictors: The Omega Score," GLO Discussion Paper Series 1207, Global Labor Organization (GLO).
    7. Michael C. Knaus, 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," Papers 1805.10300, arXiv.org, revised Jan 2019.
    8. Stefan Seifert & Marica Valente, 2018. "An Offer that you Can't Refuse? Agrimafias and Migrant Labor on Vineyards in Southern Italy," Discussion Papers of DIW Berlin 1735, DIW Berlin, German Institute for Economic Research.
    9. Höschle, Lisa & Trestini, Samuele & Giampietri, Elisa, . "Participation in a mutual fund covering losses due to pest infestation: analyzing key predictors of farmers’ interest through machine learning," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 26(3).
    10. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    11. Selina Gangl & Martin Huber, 2021. "From homemakers to breadwinners? How mandatory kindergarten affects maternal labour market outcomes," Papers 2111.14524, arXiv.org, revised Mar 2025.
    12. Gangl, Selina & Huber, Martin, 2021. "From homemakers to breadwinners? How mandatory kindergarten affects maternal labour market attachment," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203636, Verein für Socialpolitik / German Economic Association, revised 2021.
    13. Elena Denisova-Schmidt & Martin Huber & Elvira Leontyeva & Anna Solovyeva, 2021. "Combining experimental evidence with machine learning to assess anti-corruption educational campaigns among Russian university students," Empirical Economics, Springer, vol. 60(4), pages 1661-1684, April.
    14. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 685-719, December.
    15. Pawel Dlotko & Simon Rudkin & Wanling Qiu, 2019. "Topologically Mapping the Macroeconomy," Papers 1911.10476, arXiv.org.
    16. Imhof, David & Wallimann, Hannes, 2021. "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, vol. 68(C).
    17. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    18. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2019. "Valid simultaneous inference in high-dimensional settings (with the HDM package for R)," CeMMAP working papers CWP30/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    20. Harold D. Chiang, 2018. "Many Average Partial Effects: with An Application to Text Regression," Papers 1812.09397, arXiv.org, revised Jan 2022.

  85. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.

    Cited by:

    1. Xing, Lu & Han, DongHao & Hui, Xie, 2023. "The impact of carbon policy on corporate risk-taking with a double/debiased machine learning based difference-in-differences approach," Finance Research Letters, Elsevier, vol. 58(PC).
    2. Delprato, Marcos & Frola, Alessia & Antequera, Germán, 2022. "Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach," International Journal of Educational Development, Elsevier, vol. 93(C).
    3. Xiong, Ruoxuan & Koenecke, Allison & Powell, Michael & Shen, Zhu & Vogelstein, Joshua T. & Athey, Susan, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Research Papers 3990, Stanford University, Graduate School of Business.
    4. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers 21-001/V, Tinbergen Institute.
    5. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    6. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    7. Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    8. Oliver Hines & Stijn Vansteelandt & Karla Diaz-Ordaz, 2021. "Robust Inference for Mediated Effects in Partially Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 595-618, June.
    9. Jong Hee Park & Byung Koo Kim, 2020. "Why your neighbor matters: Positions in preferential trade agreement networks and export growth in global value chains," Economics and Politics, Wiley Blackwell, vol. 32(3), pages 381-410, November.
    10. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    11. Vira Semenova, 2017. "Debiased Machine Learning of Set-Identified Linear Models," Papers 1712.10024, arXiv.org, revised Dec 2022.
    12. Ziwei Cong & Jia Liu & Puneet Manchanda, 2021. "The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest," Papers 2107.01629, arXiv.org, revised Sep 2022.
    13. Amit Sharma & Emre Kiciman, 2020. "DoWhy: An End-to-End Library for Causal Inference," Papers 2011.04216, arXiv.org.
    14. Feng, Sanying & Kong, Kaidi & Kong, Yinfei & Li, Gaorong & Wang, Zhaoliang, 2022. "Statistical inference of heterogeneous treatment effect based on single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    15. Miquel Oliu-Barton & Bary S R Pradelski & Nicolas Woloszko & Lionel Guetta-Jeanrenaud & Philippe Aghion & Patrick Artus & Arnaud Fontanet & Philippe Martin & Guntram B Wolff, 2022. "The Effect of COVID Certificates on Vaccine Uptake, Health Outcomes, and the Economy," Post-Print hal-03813557, HAL.
    16. Victor Chernozhukov & Whitney Newey & Vira Semenova, 2019. "Welfare Analysis in Dynamic Models," Papers 1908.09173, arXiv.org, revised May 2025.
    17. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    18. Jinyong Hahn & Jerry Hausman, 2021. "Problems with the Control Variable Approach in Achieving Unbiased Estimates in Nonlinear Models in the Presence of Many Instruments," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 39-58, December.
    19. Sven Klaassen & Jannis Kuck & Martin Spindler & Victor Chernozhukov, 2018. "Uniform Inference in High-Dimensional Gaussian Graphical Models," Papers 1808.10532, arXiv.org, revised Dec 2018.
    20. Wang, Hongning & Ma, Sanjun, 2022. "Preventing crimes against public health with artificial intelligence and machine learning capabilities," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    21. Yong Bian & Xiqian Wang & Qin Zhang, 2023. "How Does China's Household Portfolio Selection Vary with Financial Inclusion?," Papers 2311.01206, arXiv.org.
    22. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
    23. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    24. Hirofumi Fukuyama & Mike Tsionas & Yong Tan, 2024. "Incorporating causal modeling into data envelopment analysis for performance evaluation," Annals of Operations Research, Springer, vol. 342(3), pages 1865-1904, November.
    25. Sven Klaassen & Jannis Kueck & Martin Spindler, 2017. "Transformation Models in High-Dimensions," Papers 1712.07364, arXiv.org.
    26. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    27. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Jun 2025.
    28. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
    29. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Source Condition Double Robust Inference on Functionals of Inverse Problems," Papers 2307.13793, arXiv.org.
    30. Vira Semenova, 2018. "Machine Learning for Dynamic Discrete Choice," Papers 1808.02569, arXiv.org, revised Nov 2018.
    31. Anna Baiardi & Andrea A. Naghi, 2024. "The effect of plough agriculture on gender roles: A machine learning approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1396-1402, November.
    32. Carl Bonander & Mikael Svensson, 2021. "Using causal forests to assess heterogeneity in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1818-1832, August.
    33. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    34. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    35. Maria Cuellar & Edward H. Kennedy, 2020. "A non‐parametric projection‐based estimator for the probability of causation, with application to water sanitation in Kenya," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1793-1818, October.
    36. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
    37. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    38. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    39. Andr'es Ram'irez-Hassan & Raquel Vargas-Correa & Gustavo Garc'ia & Daniel Londo~no, 2020. "Optimal selection of the number of control units in kNN algorithm to estimate average treatment effects," Papers 2008.06564, arXiv.org.
    40. Anna Baiardi & Andrea A Naghi, 2024. "The value added of machine learning to causal inference: evidence from revisited studies," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 213-234.
    41. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    42. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    43. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers CWP41/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    44. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
    45. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    46. Wang, Xiqian & Bian, Yong & Zhang, Qin, 2023. "The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods," Energy Economics, Elsevier, vol. 125(C).
    47. Elliott Ash & Daniel L. Chen & Sergio Galletta, 2022. "Measuring Judicial Sentiment: Methods and Application to US Circuit Courts," Economica, London School of Economics and Political Science, vol. 89(354), pages 362-376, April.
    48. Heiler, Phillip & Kazak, Ekaterina, 2021. "Valid inference for treatment effect parameters under irregular identification and many extreme propensity scores," Journal of Econometrics, Elsevier, vol. 222(2), pages 1083-1108.
    49. Pradhi Aggarwal & Alec Brandon & Ariel Goldszmidt & Justin Holz & John List & Ian Muir & Gregory Sun & Thomas Yu, 2022. "High-frequency location data shows that race affects the likelihood of being stopped and fined for speeding," Natural Field Experiments 00764, The Field Experiments Website.
    50. Songul Cinaroglu, 2020. "Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 168-181, October.
    51. Jiaming Mao & Zhesheng Zheng, 2020. "Structural Regularization," Papers 2004.12601, arXiv.org, revised Jun 2020.
    52. Matt Goldman & Brian Quistorff, 2018. "Pricing Engine: Estimating Causal Impacts in Real World Business Settings," Papers 1806.03285, arXiv.org, revised Jun 2018.
    53. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
    54. Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
    55. Gareth Liu-Evans & Shalini Mitra, 2023. "Formal sector enforcement and welfare," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(3), pages 706-728, June.
    56. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    57. Mochen Yang & Edward McFowland & Gordon Burtch & Gediminas Adomavicius, 2022. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 138-155, October.
    58. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    59. Fernando Delbianco & Fernando Tohmé, 2023. "Individualized Conformal," Working Papers 247, Red Nacional de Investigadores en Economía (RedNIE).
    60. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
    61. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    62. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
    63. Fukuyama, Hirofumi & Tsionas, Mike & Tan, Yong, 2023. "Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: Application to the Chinese banking industry," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1360-1373.
    64. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers 61/17, Institute for Fiscal Studies.
    65. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    66. Gazeaud, Jules & Khan, Nausheen & Mvukiyehe, Eric & Sterck, Olivier, 2023. "With or without him? Experimental evidence on cash grants and gender-sensitive trainings in Tunisia," Journal of Development Economics, Elsevier, vol. 165(C).
    67. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    68. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
    69. Dongcheng Zhang & Kunpeng Zhang, 2020. "Weighting-Based Treatment Effect Estimation via Distribution Learning," Papers 2012.13805, arXiv.org, revised May 2023.
    70. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
    71. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    72. Alexander Buchholz & Vito Bellini & Giuseppe Di Benedetto & Yannik Stein & Matteo Ruffini & Fabian Moerchen, 2022. "Fair Effect Attribution in Parallel Online Experiments," Papers 2210.08338, arXiv.org.
    73. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges," Management Science, INFORMS, vol. 66(6), pages 2495-2522, June.
    74. Mei, Ziwei & Shi, Zhentao, 2024. "On LASSO for high dimensional predictive regression," Journal of Econometrics, Elsevier, vol. 242(2).
    75. Heigle, Julia & Pfeiffer, Friedhelm, 2019. "An analysis of selected labor market outcomes of college dropouts in Germany: A machine learning estimation approach. Research report," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 222378.
    76. Krantz, Sebastian, 2024. "Mapping Africa's infrastructure potential with geospatial big data and causal ML," Kiel Working Papers 2276, Kiel Institute for the World Economy.
    77. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    78. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
    79. Ashkan Ertefaie & Nima S. Hejazi & Mark J. van der Laan, 2023. "Nonparametric inverse‐probability‐weighted estimators based on the highly adaptive lasso," Biometrics, The International Biometric Society, vol. 79(2), pages 1029-1041, June.
    80. Athey, Susan & Imbens, Guido W. & Metzger, Jonas & Munro, Evan, 2024. "Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 240(2).
    81. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    82. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
    83. Pranjal Rawat, 2024. "A Deep Learning Approach to Heterogeneous Consumer Aesthetics in Retail Fashion," Papers 2405.10498, arXiv.org.
    84. Retsef Levi & Elisabeth Paulson & Georgia Perakis & Emily Zhang, 2024. "Heterogeneous Treatment Effects in Panel Data," Papers 2406.05633, arXiv.org.
    85. Abadie, Alberto & Gu, Jiaying & Shen, Shu, 2024. "Instrumental variable estimation with first-stage heterogeneity," Journal of Econometrics, Elsevier, vol. 240(2).
    86. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2024. "Semiparametric Bayesian Difference-in-Differences," Papers 2412.04605, arXiv.org, revised Jun 2025.
    87. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    88. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.
    89. Ke Sun & Linglong Kong & Hongtu Zhu & Chengchun Shi, 2024. "ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments," Papers 2408.05342, arXiv.org, revised Jan 2025.
    90. Guido Imbens, 2019. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," NBER Working Papers 26104, National Bureau of Economic Research, Inc.
    91. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers 41/17, Institute for Fiscal Studies.
    92. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2021. "Feasible generalized least squares for panel data with cross-sectional and serial correlations," Empirical Economics, Springer, vol. 60(1), pages 309-326, January.
    93. Johannes Jakubik & Stefan Feuerriegel, 2022. "Data‐driven allocation of development aid toward sustainable development goals: Evidence from HIV/AIDS," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2739-2756, June.

  86. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Anti-concentration and honest, adaptive confidence bands," CeMMAP working papers 43/16, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    2. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers 41/16, Institute for Fiscal Studies.
    3. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Testing Many Moment Inequalities," CeMMAP working papers CWP65/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Comparison and anti-concentration bounds for maxima of Gaussian random vectors," CeMMAP working papers 40/16, Institute for Fiscal Studies.

  87. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers 41/16, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.
    2. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, "undated". "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    3. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    4. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Anti-concentration and honest, adaptive confidence bands," CeMMAP working papers 43/16, Institute for Fiscal Studies.
    5. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    6. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors," CeMMAP working papers 76/13, Institute for Fiscal Studies.
    7. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    8. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Central limit theorems and multiplier bootstrap when p is much larger than n," CeMMAP working papers CWP45/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers CWP28/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Kato, Kengo, 2015. "Some new asymptotic theory for least squares series: Pointwise and uniform results," Journal of Econometrics, Elsevier, vol. 186(2), pages 345-366.
    11. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2014. "Program evaluation with high-dimensional data," CeMMAP working papers CWP33/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Papers 1312.7186, arXiv.org, revised Jun 2016.
    13. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression and other z-estimation problems," CeMMAP working papers CWP74/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  88. Mingli Chen & Victor Chernozhukov & Iv'an Fern'andez-Val & Blaise Melly, 2016. "Counterfactual: An R Package for Counterfactual Analysis," Papers 1610.07894, arXiv.org.

    Cited by:

    1. Qiu, Wanling & Rudkin, Simon & Sharma, Abhijit, 2017. "An Analysis of the Impact of Low Cost Airlines on Tourist Stay Duration and Expenditures," MPRA Paper 81428, University Library of Munich, Germany.
    2. Delprato, Marcos & Chudgar, Amita, 2018. "Factors associated with private-public school performance: Analysis of TALIS-PISA link data," International Journal of Educational Development, Elsevier, vol. 61(C), pages 155-172.

  89. Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," Papers 1502.03155, arXiv.org, revised Mar 2015.

    Cited by:

    1. Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," CeMMAP working papers CWP56/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  90. Patrick Bajari & Victor Chernozhukov & Han Hong & Denis Nekipelov, 2015. "Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game," NBER Working Papers 21125, National Bureau of Economic Research, Inc.

    Cited by:

    1. Cook, Jonathan A. & Lin, C.-Y. Cynthia, 2015. "Wind Turbine Shutdowns and Upgrades in Denmark: Timing Decisions and the Impact of Government Policy," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 204960, Agricultural and Applied Economics Association.
    2. Jackson Bunting, 2022. "Continuous permanent unobserved heterogeneity in dynamic discrete choice models," Papers 2202.03960, arXiv.org, revised Sep 2025.
    3. Taiga Tsubota, 2021. "Identifying Dynamic Discrete Choice Models with Hyperbolic Discounting," Papers 2111.10721, arXiv.org, revised Oct 2024.
    4. Evgeny Yakovlev, 2016. "Demand for Alcohol Consumption and Implication for Mortality: Evidence from Russia," Working Papers w0221, Center for Economic and Financial Research (CEFIR).
    5. Jaap H. Abbring & Jeffrey R. Campbell & Jan Tilly & Nan Yang, 2018. "Very Simple Markov-Perfect Industry Dynamics: Empirics," Working Paper Series WP-2018-17, Federal Reserve Bank of Chicago.
    6. Abbring, Jaap & Daljord, Øystein, 2016. "Identifying the Discount Factor in Dynamic Discrete Choice Models," CEPR Discussion Papers 11133, C.E.P.R. Discussion Papers.
    7. Song Yao & Carl F. Mela, 2011. "A Dynamic Model of Sponsored Search Advertising," Marketing Science, INFORMS, vol. 30(3), pages 447-468, 05-06.
    8. Haitian Xie, 2020. "Efficient and Robust Estimation of the Generalized LATE Model," Papers 2001.06746, arXiv.org, revised Feb 2022.
    9. Abbring, Jaap & Campbell, J.R. & Tilly, J. & Yang, N., 2018. "Very Simple Markov-Perfect Industry Dynamics (revision of 2017-021) : Empirics," Other publications TiSEM 3a12f099-900b-44ac-b692-a, Tilburg University, School of Economics and Management.
    10. Zhaohui (Zoey) Jiang & Yan Huang & Damian R. Beil, 2022. "The Role of Feedback in Dynamic Crowdsourcing Contests: A Structural Empirical Analysis," Management Science, INFORMS, vol. 68(7), pages 4858-4877, July.
    11. A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2018. "The Dynamic Spillovers of Entry: An Application to the Generic Drug Industry," Management Science, INFORMS, vol. 64(3), pages 1189-1211, March.
    12. Rojas Valdes, Ruben I. & Lin Lawell, C.-Y. Cynthia & Taylor, J. Edward, "undated". "The Dynamic Migration Game: A Structural Econometric Model and Application to Rural Mexico," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 259184, Agricultural and Applied Economics Association.
    13. Lawell, Cynthia Lin & Yi, Fujin & Thome, Karen E, 2017. "The Effects of Subsidies and Mandates: A Dynamic Model of the Ethanol Industry," Institute of Transportation Studies, Working Paper Series qt73n0t4pv, Institute of Transportation Studies, UC Davis.
    14. Kheiravar, Khaled H, 2019. "Economic and Econometric Analyses of the World Petroleum Industry, Energy Subsidies, and Air Pollution," Institute of Transportation Studies, Working Paper Series qt3gj151w9, Institute of Transportation Studies, UC Davis.

  91. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.

    Cited by:

    1. Zhong, Wei & Gao, Yang & Zhou, Wei & Fan, Qingliang, 2021. "Endogenous treatment effect estimation using high-dimensional instruments and double selection," Statistics & Probability Letters, Elsevier, vol. 169(C).
    2. Bottmer, Lea & Croux, Christophe & Wilms, Ines, 2022. "Sparse regression for large data sets with outliers," European Journal of Operational Research, Elsevier, vol. 297(2), pages 782-794.
    3. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    4. Brunstein, Daniel & Casamatta, Georges & Giannoni, Sauveur, 2025. "Using machine learning to estimate the heterogeneous impact of Airbnb on house prices: Evidence from Corsica," Journal of Housing Economics, Elsevier, vol. 67(C).
    5. Franz Huber & Alan Ponce & Francesco Rentocchini & Thomas Wainwright, 2020. "The Wealth of (Open Data) Nations? Examining the interplay of open government data and country-level institutions for entrepreneurial activity at the country-level," SEEDS Working Papers 1120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Nov 2020.
    6. Ariadna Jou & Tommy Morgan, 2025. "Do Relief Programs Compensate For Longevity Losses From Reccesions? Evidence From The Great Depression And The New Deal," Working Papers wp562, University of Chile, Department of Economics.
    7. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    8. Shukla, Pallavi & Pullabhotla, Hemant K. & Baylis, Kathy, 2022. "Trouble with zero: The limits of subsidizing technology adoption," Journal of Development Economics, Elsevier, vol. 158(C).
    9. Krüger, Jens J. & Rhiel, Mathias, 2016. "Determinants of ICT infrastructure: A cross-country statistical analysis," Darmstadt Discussion Papers in Economics 228, Darmstadt University of Technology, Department of Law and Economics.
    10. Tomaso Duso & Claus Michelsen & Maximilian Schäfer & Kevin Ducbao Tran, 2024. "Airbnb and rental markets: evidence from Berlin," Post-Print hal-04551670, HAL.
    11. Godzinski, Alexandre & Suarez Castillo, Milena, 2021. "Disentangling the effects of air pollutants with many instruments," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    12. Giuseppe de Luca & Jan Magnus & Franco Peracchi, 2017. "Weighted-Average Least Squares Estimation of Generalized Linear Models," Tinbergen Institute Discussion Papers 17-029/III, Tinbergen Institute.
    13. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    14. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    15. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    16. Alexandre Belloni & Victor Chernozhukov & Ivan Fern'andez-Val & Christian Hansen, 2013. "Program Evaluation and Causal Inference with High-Dimensional Data," Papers 1311.2645, arXiv.org, revised Jan 2018.
    17. Woodruff, Christopher & Menzel, Andreas, 2019. "Gender Wage Gaps and Worker Mobility: Evidence from the Garment Sector in Bangladesh," CEPR Discussion Papers 13577, C.E.P.R. Discussion Papers.
    18. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019. "lassopack: Model selection and prediction with regularized regression in Stata," Papers 1901.05397, arXiv.org.
    19. Gal Amedi, 2023. "The Determinants of the Transit Accessibility Premium," Bank of Israel Working Papers 2023.12, Bank of Israel.
    20. Cameron, Lisa & Gertler, Paul & Shah, Manisha & Alzua, Maria Laura & Martinez, Sebastian & Patil, Sumeet, 2022. "The dirty business of eliminating open defecation: The effect of village sanitation on child height from field experiments in four countries," Journal of Development Economics, Elsevier, vol. 159(C).
    21. Alessandro V. M. Oliveira & Bruno F. Oliveira & Moises D. Vassallo, 2024. "Airport service quality perception and flight delays: examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys," Papers 2401.02139, arXiv.org.
    22. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    23. Zhaonan Qu & Yongchan Kwon, 2024. "Distributionally Robust Instrumental Variables Estimation," Papers 2410.15634, arXiv.org, revised Dec 2024.
    24. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
    25. Magdalena Bendini & Lelys Dinarte, 2020. "Does Maternal Depression Undermine Childhood Cognitive Development? Evidence from the Young Lives Survey in Peru," IJERPH, MDPI, vol. 17(19), pages 1-18, October.
    26. Kaila, Heidi & Azad, Abul, 2023. "The effects of crime and violence on food insecurity and consumption in Nigeria," Food Policy, Elsevier, vol. 115(C).
    27. Ruf, Daniel, 2017. "Agglomeration Effects and Liquidity Gradients in Local Rental Housing Markets," Working Papers on Finance 1702, University of St. Gallen, School of Finance.
    28. Li, Jing & Li, Liyao & Liu, Shimeng, 2022. "Attenuation of agglomeration economies: Evidence from the universe of Chinese manufacturing firms," Journal of Urban Economics, Elsevier, vol. 130(C).
    29. Susan Athey & Guido Imbens, 2016. "The Econometrics of Randomized Experiments," Papers 1607.00698, arXiv.org.
    30. Ofori, Isaac K. & Quaidoo, Christopher & Ofori, Pamela E., 2021. "What Drives Financial Sector Development in Africa? Insights from Machine Learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue forthcomi.
    31. Max-Sebastian Dov`i, 2021. "Inference on the New Keynesian Phillips Curve with Very Many Instrumental Variables," Papers 2101.09543, arXiv.org, revised Mar 2021.
    32. Marianne Bl'ehaut & Xavier D'Haultfoeuille & J'er'emy L'Hour & Alexandre B. Tsybakov, 2020. "An alternative to synthetic control for models with many covariates under sparsity," Papers 2005.12225, arXiv.org, revised Jun 2021.
    33. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2017. "Quantile graphical models: prediction and conditional independence with applications to systemic risk," CeMMAP working papers CWP54/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    34. Pettersson-Lidbom, Per, 2022. "Exit, Voice and Political Change: Evidence from Swedish Mass Migration to the United States. A Comment on Karadja and Prawitz (Journal of Political Economy, 2019)," Journal of Comments and Replications in Economics (JCRE), ZBW - Leibniz Information Centre for Economics, vol. 1, pages 1-13.
    35. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2020. "Ill-posed estimation in high-dimensional models with instrumental variables," Journal of Econometrics, Elsevier, vol. 219(1), pages 171-200.
    36. Cheng, Yifan & Yu, Jianyu & Min, Shi & Wang, Xiaobing, 2024. "The effects of land titling on intergenerational transfers in rural China," Journal of Economic Behavior & Organization, Elsevier, vol. 225(C), pages 228-251.
    37. Samarth Gupta, 2023. "Model-Selection Inference for Causal Impact of Clusters and Collaboration on MSMEs in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(3), pages 641-662, September.
    38. Danquah, Michael & Iddrisu, Abdul Malik & Boakye, Ernest Owusu & Owusu, Solomon, 2021. "Do gender wage differences within households influence women's empowerment and welfare? Evidence from Ghana," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 916-932.
    39. Wang, Steven Shuye & Xu, Kuan & Zhang, Hao, 2019. "A microstructure study of circuit breakers in the Chinese stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    40. Madina Kurmangaliyeva & Matteo Sostero, 2022. "Walking while Black :Racial Gaps in Hit-and-Run Cases," Working Papers ECARES 2022-08, ULB -- Universite Libre de Bruxelles.
    41. Natalia Garbiras-Díaz & Mateo Montenegro, 2022. "All Eyes on Them: A Field Experiment on Citizen Oversight and Electoral Integrity," American Economic Review, American Economic Association, vol. 112(8), pages 2631-2668, August.
    42. Alessandro V. M. Oliveira & Thiago Caliari & Rodolfo R. Narcizo, 2024. "An empirical model of fleet modernization: on the relationship between market concentration and innovation adoption in the Brazilian airline industry," Papers 2401.06876, arXiv.org.
    43. Li, Wenchao, 2024. "Do surging house prices discourage fertility? Global evidence, 1870–2012," Labour Economics, Elsevier, vol. 90(C).
    44. Jad Beyhum & Martin Mugnier, 2024. "Inference after discretizing unobserved heterogeneity," PSE Working Papers halshs-04840588, HAL.
    45. Lai Xinglin, 2021. "Modelling hetegeneous treatment effects by quantitle local polynomial decision tree and forest," Papers 2111.15320, arXiv.org, revised Mar 2022.
    46. Windmeijer, F.; Farbmacher, H.; Davies, N.; Davey Smith, G.;, 2017. "On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments," Health, Econometrics and Data Group (HEDG) Working Papers 17/22, HEDG, c/o Department of Economics, University of York.
    47. Joshua Angrist & Brigham Frandsen, 2019. "Machine Labor," NBER Working Papers 26584, National Bureau of Economic Research, Inc.
    48. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
    49. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    50. Sophie Brana & Dalila Chenaf-Nicet & Delphine Lahet, 2023. "Drivers of cross-border bank claims: The role of foreign-owned banks in emerging countries," Working Papers 2023.06, International Network for Economic Research - INFER.
    51. Sun, Weixin & Wang, Yong & Zhang, Li & Chen, Xihui Haviour & Hoang, Yen Hai, 2025. "Enhancing economic cycle forecasting based on interpretable machine learning and news narrative sentiment," Technological Forecasting and Social Change, Elsevier, vol. 215(C).
    52. James M. Carson & Cameron M. Ellis & Robert E. Hoyt & Krzysztof Ostaszewski, 2020. "Sunk Costs and Screening: Two‐Part Tariffs in Life Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(3), pages 689-718, September.
    53. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2023. "ddml: Double/debiased machine learning in Stata," Papers 2301.09397, arXiv.org, revised Jan 2024.
    54. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
    55. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
    56. Pushan Dutt & Ilia Tsetlin, 2021. "Income distribution and economic development: Insights from machine learning," Economics and Politics, Wiley Blackwell, vol. 33(1), pages 1-36, March.
    57. Jad Beyhum & Martin Mugnier, 2024. "Inference after discretizing time-varying unobserved heterogeneity," Papers 2412.07352, arXiv.org, revised Oct 2025.
    58. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    59. Tomaso Duso & Claus Michelsen & Maximilian Schäfer & Kevin Ducbao Tran, 2020. "Airbnb and Rents: Evidence from Berlin," Discussion Papers of DIW Berlin 1890, DIW Berlin, German Institute for Economic Research.
    60. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    61. Tom L. Dudda & Lars Hornuf, 2025. "The Perks and Perils of Machine Learning in Business and Economic Research," CESifo Working Paper Series 11721, CESifo.
    62. Spika, Devon & Wickström Östervall, Linnea & Gerdtham, Ulf & Wengström, Erik, 2024. "Put a bet on it: Can self-funded commitment contracts curb fitness procrastination?," Journal of Health Economics, Elsevier, vol. 98(C).
    63. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," American Economic Review, American Economic Association, vol. 105(5), pages 486-490, May.
    64. Seojeong Lee & Youngki Shin, 2018. "Complete Subset Averaging with Many Instruments," Papers 1811.08083, arXiv.org, revised Aug 2020.
    65. Brito, Igor R.S. & Oliveira, Alessandro V.M. & Dresner, Martin E., 2021. "An econometric study of the effects of airport privatization on airfares in Brazil," Transport Policy, Elsevier, vol. 114(C), pages 338-349.
    66. Xiduo Chen & Xingdong Feng & Antonio F. Galvao & Yeheng Ge, 2025. "Treatment Effects Inference with High-Dimensional Instruments and Control Variables," Papers 2503.20149, arXiv.org, revised Oct 2025.
    67. Seojeong Lee & Youngki Shin, 2018. "Optimal Estimation with Complete Subsets of Instruments," Department of Economics Working Papers 2018-15, McMaster University.
    68. Hoeschle, Lisa & Maruejols, Lucie & Yu, Xiaohua, 2025. "The impact of energy justice on local economic outcomes: Evidence from the bioenergy village program in Germany," Energy Economics, Elsevier, vol. 145(C).
    69. Eufrásio, Ana Beatriz R. & Eller, Rogéria A.G. & Oliveira, Alessandro V.M., 2021. "Are on-time performance statistics worthless? An empirical study of the flight scheduling strategies of Brazilian airlines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    70. Bai Huang & Tae-Hwy Lee & Aman Ullah, 2017. "A combined estimator of regression models with measurement errors," Indian Economic Review, Springer, vol. 52(1), pages 73-91, December.
    71. Daniel Brunstein & Georges Casamatta & Sauveur Giannoni, 2023. "Hétérogénéité de l'impact du développement d'Airbnb sur le marché immobilier en Corse," Post-Print hal-04122192, HAL.
    72. Liu, Yan & Chen, Minjie & Yu, Jianyu & Wang, Xiaobing, 2024. "Being a happy farmer: Technology adoption and subjective well-being," Journal of Economic Behavior & Organization, Elsevier, vol. 221(C), pages 385-405.
    73. Luca J. Santos & Alessandro V. M. Oliveira & Dante Mendes Aldrighi, 2021. "Testing the differentiated impact of the COVID-19 pandemic on air travel demand considering social inclusion," Papers 2108.00850, arXiv.org.
    74. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    75. Cocker Liu & Adam Nowak & Patrick Smith, 2017. "Some Remarks on Real Estate Pricing," Working Papers 17-20, Department of Economics, West Virginia University.
    76. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    77. Koen van Ruijven & Joep Tijm, 2021. "Housing Market Effects of a Railroad Tunneling: Evidence from a quasi-experiment," CPB Discussion Paper 423, CPB Netherlands Bureau for Economic Policy Analysis.
    78. Kyle Myers & Wei Yang Tham, 2023. "Money, Time, and Grant Design," Papers 2312.06479, arXiv.org.
    79. Rishabh Tyagi & Peter Eibich & Vegard Skirbekk, 2024. "Gender norms and partnership dissolution following involuntary job loss in Germany," MPIDR Working Papers WP-2024-027, Max Planck Institute for Demographic Research, Rostock, Germany.
    80. Oliveira, Alessandro V.M. & Oliveira, Bruno F. & Vassallo, Moisés D., 2023. "Airport service quality perception and flight delays: Examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys," Research in Transportation Economics, Elsevier, vol. 102(C).
    81. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2015. "Program evaluation with high-dimensional data," CeMMAP working papers CWP55/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  92. Guillaume Carlier & Victor Chernozhukov & Alfred Galichon, 2015. "Vector quantile regression: an optimal transport approach," CeMMAP working papers 58/15, Institute for Fiscal Studies.

    Cited by:

    1. Florian Gunsilius & Susanne M. Schennach, 2017. "A nonlinear principal component decomposition," CeMMAP working papers CWP16/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    3. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Sciences Po Economics Publications (main) hal-03936221, HAL.
    4. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    5. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Transmission of US and EU Economic Policy Uncertainty Shock to Asian Economies in Bad and Good Times," IZA Discussion Papers 13274, IZA Network @ LISER.
    6. Camehl, Annika & Fok, Dennis & Gruber, Kathrin, 2025. "On superlevel sets of conditional densities and multivariate quantile regression," Journal of Econometrics, Elsevier, vol. 249(PA).
    7. Gabriel Montes-Rojas & Nicolás Bertholet, 2022. "When Are Devaluations More Contractionary? A Quantile Var Estimation For Argentina," Documentos de trabajo del Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET) 2022-71, Universidad de Buenos Aires, Facultad de Ciencias Económicas, Instituto Interdisciplinario de Economía Política IIEP (UBA-CONICET).
    8. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org, revised Feb 2025.
    9. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2017. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Post-Print hal-03391975, HAL.
    10. Nadja Klein & Thomas Kneib, 2020. "Directional bivariate quantiles: a robust approach based on the cumulative distribution function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 225-260, June.
    11. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    12. Richard Blundell & Dennis Kristensen & Rosa Matzkin, 2017. "Individual counterfactuals with multidimensional unobserved heterogeneity," CeMMAP working papers CWP60/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Tongseok Lim, 2023. "Replication of financial derivatives under extreme market models given marginals," Papers 2307.00807, arXiv.org.
    14. Schennach, Susanne M., 2020. "Mismeasured and unobserved variables," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 487-565, Elsevier.
    15. Richard Spady & Sami Stouli, 2012. "Dual Regression," Papers 1210.6958, arXiv.org, revised Sep 2018.
    16. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Nadja Klein & Torsten Hothorn & Luisa Barbanti & Thomas Kneib, 2022. "Multivariate conditional transformation models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 116-142, March.
    18. Walter W. Zhang & Sanjog Misra, 2022. "Coarse Personalization," Papers 2204.05793, arXiv.org, revised Jun 2025.
    19. Daouia, Abdelaati & Paindaveine, Davy, 2019. "Multivariate Expectiles, Expectile Depth and Multiple-Output Expectile Regression," TSE Working Papers 19-1022, Toulouse School of Economics (TSE), revised Feb 2023.
    20. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    21. Agarwal, Gaurav & Tu, Wei & Sun, Ying & Kong, Linglong, 2022. "Flexible quantile contour estimation for multivariate functional data: Beyond convexity," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    22. Marc Hallin & Gilles Mordant, 2021. "On the Finite-Sample Performance of Measure Transportation-Based Multivariate Rank Tests," Working Papers ECARES 2021-24, ULB -- Universite Libre de Bruxelles.
    23. Timo Dimitriadis & Yannick Hoga, 2022. "Dynamic CoVaR Modeling and Estimation," Papers 2206.14275, arXiv.org, revised Jan 2025.
    24. Ruodu Wang & Zhenyuan Zhang, 2022. "Simultaneous Optimal Transport," Papers 2201.03483, arXiv.org, revised Dec 2024.
    25. Daniel Hlubinka & Lukáš Kotík & Miroslav Šiman, 2022. "Multivariate quantiles with both overall and directional probability interpretation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1586-1604, December.
    26. Eustasio del Barrio & Alberto González-Sanz & Marc Hallin, 2022. "Nonparametric Multiple-Output Center-Outward Quantile Regression," Working Papers ECARES 2022-10, ULB -- Universite Libre de Bruxelles.
    27. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.
    28. Carlier, Guillaume & Chernozhukov, Victor & Galichon, Alfred, 2017. "Vector quantile regression beyond the specified case," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 96-102.
    29. Hubner, Stefan, 2023. "Identification of unobserved distribution factors and preferences in the collective household model," Journal of Econometrics, Elsevier, vol. 234(1), pages 301-326.
    30. Florian Gunsilius & Susanne M. Schennach, 2019. "Independent nonlinear component analysis," CeMMAP working papers CWP46/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  93. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers 59/15, Institute for Fiscal Studies.

    Cited by:

    1. Zheng Fang & Juwon Seo, 2021. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Econometrica, Econometric Society, vol. 89(5), pages 2439-2458, September.
    2. Vira Semenova, 2023. "Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters," Papers 2303.00982, arXiv.org, revised May 2025.
    3. Chen, Qihui, 2021. "Robust and optimal estimation for partially linear instrumental variables models with partial identification," Journal of Econometrics, Elsevier, vol. 221(2), pages 368-380.
    4. Chen, Xiaohong & Liao, Yuan & Wang, Weichen, 2025. "Inference on time series nonparametric conditional moment restrictions using nonlinear sieves," Journal of Econometrics, Elsevier, vol. 249(PA).
    5. Babii, Andrii & Kumar, Rohit, 2023. "Isotonic regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 234(2), pages 371-393.
    6. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    7. Kline, Patrick & Walters, Christopher, 2019. "Audits as Evidence: Experiments, Ensembles, and Enforcement," Institute for Research on Labor and Employment, Working Paper Series qt3z72m9kn, Institute of Industrial Relations, UC Berkeley.
    8. Roy Allen & John Rehbeck, 2020. "Identification of Random Coefficient Latent Utility Models," Papers 2003.00276, arXiv.org.
    9. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    10. Denis Chetverikov & Daniel Wilhelm, 2017. "Nonparametric instrumental variable estimation under monotonicity," CeMMAP working papers CWP14/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Isaac Loh, 2024. "Inference under partial identification with minimax test statistics," Papers 2401.13057, arXiv.org, revised Apr 2024.
    12. Lee, Y-Y. & Bhattacharya, D., 2018. "Applied Welfare Analysis for Discrete Choice with Interval-data on Income," Cambridge Working Papers in Economics 1882, Faculty of Economics, University of Cambridge.
    13. Christoph Breunig & Xiaohong Chen, 2020. "Adaptive, Rate-Optimal Hypothesis Testing in Nonparametric IV Models," Cowles Foundation Discussion Papers 2238R, Cowles Foundation for Research in Economics, Yale University, revised Dec 2021.
    14. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    15. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Fernández-Val, Iván, 2019. "Conditional quantile processes based on series or many regressors," Journal of Econometrics, Elsevier, vol. 213(1), pages 4-29.
    16. Christoph Breunig & Xiaohong Chen, 2020. "Adaptive, Rate-Optimal Hypothesis Testing in Nonparametric IV Models," Papers 2006.09587, arXiv.org, revised Nov 2024.
    17. Xiaohong Chen & Yin Jia Qiu, 2016. "Methods for Nonparametric and Semiparametric Regressions with Endogeneity: a Gentle Guide," Cowles Foundation Discussion Papers 2032, Cowles Foundation for Research in Economics, Yale University.
    18. Patrick Kline & Christopher Walters, 2021. "Reasonable Doubt: Experimental Detection of Job‐Level Employment Discrimination," Econometrica, Econometric Society, vol. 89(2), pages 765-792, March.
    19. Ivan Korolev, 2018. "A Consistent Heteroskedasticity Robust LM Type Specification Test for Semiparametric Models," Papers 1810.07620, arXiv.org, revised Nov 2019.
    20. Matias D. Cattaneo & Xinwei Ma & Yusufcan Masatlioglu & Elchin Suleymanov, 2017. "A Random Attention Model," Papers 1712.03448, arXiv.org, revised Aug 2019.
    21. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    22. Isaiah Andrews & Jonathan Roth & Ariel Pakes, 2019. "Inference for Linear Conditional Moment Inequalities," NBER Working Papers 26374, National Bureau of Economic Research, Inc.
    23. Matias D. Cattaneo & Max H. Farrell & Yingjie Feng, 2018. "Large Sample Properties of Partitioning-Based Series Estimators," Papers 1804.04916, arXiv.org, revised Jun 2019.
    24. Zheng Fang & Andres Santos & Azeem M. Shaikh & Alexander Torgovitsky, 2023. "Inference for Large‐Scale Linear Systems With Known Coefficients," Econometrica, Econometric Society, vol. 91(1), pages 299-327, January.
    25. Giovanni Compiani, 2022. "Market counterfactuals and the specification of multiproduct demand: A nonparametric approach," Quantitative Economics, Econometric Society, vol. 13(2), pages 545-591, May.
    26. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.

  94. Victor Chernozhukov & Ivan Fernandez-Val & Ye Luo, 2015. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Papers 1512.05635, arXiv.org, revised May 2018.

    Cited by:

    1. Matthew A. Masten & Alexandre Poirier & Muyang Ren, 2025. "A General Approach to Relaxing Unconfoundedness," Papers 2501.15400, arXiv.org.
    2. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    3. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers CWP61/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    5. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    6. Diego Marino Fages, 2023. "Migration and trust: Evidence on assimilation from internal migrants," Discussion Papers 2023-08, Nottingham Interdisciplinary Centre for Economic and Political Research (NICEP).
    7. Sallin, Aurelién, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Economics Working Paper Series 2109, University of St. Gallen, School of Economics and Political Science.
    8. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    9. Xiaohong Chen & Wayne Yuan Gao, 2025. "Semiparametric Learning of Integral Functionals on Submanifolds," Cowles Foundation Discussion Papers 2450, Cowles Foundation for Research in Economics, Yale University.
    10. Florent Dubois & Christophe Muller, 2022. "Residential segregation matters to racial income gaps: Evidence from South Africa," AMSE Working Papers 2205, Aix-Marseille School of Economics, France.
    11. Anthony Strittmatter & Conny Wunsch, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," Papers 2102.09207, arXiv.org, revised Feb 2021.
    12. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, IZA Network @ LISER.
    13. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
    14. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    15. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Jan 2025.
    16. Florent Dubois & Christophe Muller, 2022. "Residential segregation matters to racial income gaps," Working Papers hal-03622711, HAL.
    17. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2017. "Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," Papers 1712.04802, arXiv.org, revised Oct 2023.
    18. Marzi, Giacomo & Balzano, Marco, 2025. "Artificial intelligence and the reconfiguration of NPD Teams: Adaptability and skill differentiation in sustainable product innovation," Technovation, Elsevier, vol. 145(C).
    19. Bin Xiong & Qi Sui, 2025. "The effect of digital economy on rural workforce occupation transformation ability: Evidence from China," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-15, December.
    20. Kroczek, Martin & Kugler, Philipp, 2024. "Heterogeneous effects of monetary and non-monetary job characteristics on job attractiveness in nursing," Labour Economics, Elsevier, vol. 91(C).
    21. Strittmatter, Anthony, 2019. "What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203499, Verein für Socialpolitik / German Economic Association.
    22. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    23. Florent Dubois & Christophe Muller, 2020. "The Contribution of Residential Segregation to Racial Income Gaps: Evidence from South Africa," Working Papers halshs-02944720, HAL.
    24. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," CESifo Working Paper Series 9037, CESifo.
    25. Posel, Dorrit & Oyenubi, Adeola, 2023. "Heterogeneous gender gaps in mental wellbeing: Do women with low economic status face the biggest gender gaps?," Social Science & Medicine, Elsevier, vol. 332(C).
    26. Martin Kroczek & Philipp Kugler, 2022. "Heterogeneous Effects of Monetary and Non-Monetary Job Characteristics on Job Attractiveness in Nursing," IAW Discussion Papers 139, Institut für Angewandte Wirtschaftsforschung (IAW).
    27. Lopez Garcia, Italo & Luoto, Jill E. & Aboud, Frances E. & Fernald, Lia C.H., 2023. "Group Meetings and Boosters to Sustain Early Impacts on Child Development: Experimental Evidence from Kenya," IZA Discussion Papers 16392, IZA Network @ LISER.
    28. Tsionas, Mike, 2022. "Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries," International Journal of Production Economics, Elsevier, vol. 249(C).
    29. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    30. Andrew Baker & Brantly Callaway & Scott Cunningham & Andrew Goodman-Bacon & Pedro H. C. Sant'Anna, 2025. "Difference-in-Differences Designs: A Practitioner's Guide," Papers 2503.13323, arXiv.org, revised Jun 2025.
    31. Kroczek, Martin & Kugler, Philipp, 2022. "Heterogeneous Effects of Monetary and Non-Monetary Job Characteristics on Job Attractiveness in Nursing," VfS Annual Conference 2022 (Basel): Big Data in Economics 264108, Verein für Socialpolitik / German Economic Association.
    32. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    33. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
    34. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    35. Chi Zhang & Xiangdan Piao & Shunsuke Managi, 2023. "Work Hour Mismatch on Life Evaluation: Full Heterogeneity and Individual- and Country-Level Characteristics of the Most and Least Affected Workers," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 170(2), pages 637-674, November.
    36. Deschenes, Olivier & Malloy, Christopher & McDonald, Gavin, 2023. "Causal effects of Renewable Portfolio Standards on renewable investments and generation: The role of heterogeneity and dynamics," Resource and Energy Economics, Elsevier, vol. 75(C).
    37. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
    38. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," Papers 2206.07845, arXiv.org.
    39. Laub, Natalie & Boockmann, Bernhard & Kroczek, Martin, 2023. "Tightening Access to Early Retirement: Who Can Adapt?," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277625, Verein für Socialpolitik / German Economic Association.
    40. Jacob, Daniel & Härdle, Wolfgang Karl & Lessmann, Stefan, 2019. "Group Average Treatment Effects for Observational Studies," IRTG 1792 Discussion Papers 2019-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    41. Pullabhotla, Hemant K. & Souza, Mateus, 2022. "Air pollution from agricultural fires increases hypertension risk," Journal of Environmental Economics and Management, Elsevier, vol. 115(C).
    42. Bilancini, Ennio & Boncinelli, Leonardo & Di Paolo, Roberto & Menicagli, Dario & Pizziol, Veronica & Ricciardi, Emiliano & Serti, Francesco, 2022. "Prosocial behavior in emergencies: Evidence from blood donors recruitment and retention during the COVID-19 pandemic," Social Science & Medicine, Elsevier, vol. 314(C).
    43. Chernozhukov, Victor & Fernández-Val, Iván & Newey, Whitney K., 2019. "Nonseparable multinomial choice models in cross-section and panel data," Journal of Econometrics, Elsevier, vol. 211(1), pages 104-116.
    44. Laura Liu & Alexandre Poirier & Ji-Liang Shiu, 2021. "Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models," Papers 2105.12891, arXiv.org, revised Jul 2024.
    45. Ricardo Masini & Marcelo Medeiros, 2025. "Balancing Flexibility and Interpretability: A Conditional Linear Model Estimation via Random Forest," Papers 2502.13438, arXiv.org.
    46. Bernhard Boockmann & Martin Kroczek & Natalie Laub, 2023. "Tightening access to early retirement: who can adapt?," IAW Discussion Papers 142, Institut für Angewandte Wirtschaftsforschung (IAW).
    47. Xiaohong Chen & Wayne Yuan Gao, 2025. "Semiparametric Learning of Integral Functionals on Submanifolds," Papers 2507.12673, arXiv.org, revised Oct 2025.
    48. Jun Ma & Vadim Marmer & Zhengfei Yu, 2025. "Inference on the Distribution of Individual Treatment Effects in Nonseparable Triangular Models," Papers 2509.15401, arXiv.org.
    49. Aur'elien Sallin, 2021. "Estimating returns to special education: combining machine learning and text analysis to address confounding," Papers 2110.08807, arXiv.org, revised Feb 2022.
    50. Yuri Fonseca & Marcelo Medeiros & Gabriel Vasconcelos & Alvaro Veiga, 2018. "BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions," Papers 1808.03698, arXiv.org, revised Jul 2020.
    51. Kai Feng & Han Hong & Denis Nekipelov, 2024. "Statistical Inference of Optimal Allocations I: Regularities and their Implications," Papers 2403.18248, arXiv.org, revised Feb 2026.

  95. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach," Papers 1501.03430, arXiv.org, revised Aug 2015.

    Cited by:

    1. Gozgor, Giray & Li, Jing & Saleem, Irfan & Shinwari, Riazullah, 2025. "The impact of women's political empowerment on renewable energy demand: Evidence from OECD countries," Energy Economics, Elsevier, vol. 141(C).
    2. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    3. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    4. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    5. Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2022. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," Management Science, INFORMS, vol. 68(11), pages 7918-7939, November.
    6. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    7. Fan, Yanqin & Han, Fang & Li, Wei & Zhou, Xiao-Hua, 2020. "On rank estimators in increasing dimensions," Journal of Econometrics, Elsevier, vol. 214(2), pages 379-412.
    8. Jau-er Chen & Chien-Hsun Huang & Jia-Jyun Tien, 2021. "Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions," Econometrics, MDPI, vol. 9(2), pages 1-18, April.
    9. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2024. "Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 239(2).
    10. Victor Chernozhukov & Whitney Newey & Vira Semenova, 2019. "Welfare Analysis in Dynamic Models," Papers 1908.09173, arXiv.org, revised May 2025.
    11. Stephan Brunow & Stefanie Lösch & Ostap Okhrin, 2022. "Labor market tightness and individual wage growth: evidence from Germany," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 56(1), pages 1-21, December.
    12. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    13. Markus Pelger & Jiacheng Zou, 2022. "Inference for Large Panel Data with Many Covariates," Papers 2301.00292, arXiv.org, revised Mar 2023.
    14. Helmut Wasserbacher & Martin Spindler, 2021. "Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls," Papers 2107.04851, arXiv.org.
    15. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    16. Kaila, Heidi & Azad, Abul, 2023. "The effects of crime and violence on food insecurity and consumption in Nigeria," Food Policy, Elsevier, vol. 115(C).
    17. Victor Chernozhukov & Whitney Newey & Rahul Singh, 2018. "De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers," Papers 1802.08667, arXiv.org, revised Oct 2022.
    18. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    19. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    20. Stépahne Auray & Nicolas Lepage-Saucier & Purevdorj Tuvaandor, 2018. "Doubly Robust GMM Inference and Differentiated Products Demand Models," Working Papers 2018-13, Center for Research in Economics and Statistics.
    21. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2016. "Locally Robust Semiparametric Estimation," Papers 1608.00033, arXiv.org, revised Aug 2020.
    22. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Jul 2025.
    23. Luo, Kaikai & Wang, Fen & Chen, Xuezhen, 2025. "Impact of artificial intelligence on energy efficiency in Chinese enterprises," International Review of Economics & Finance, Elsevier, vol. 103(C).
    24. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    25. Kueck, Jannis & Luo, Ye & Spindler, Martin & Wang, Zigan, 2023. "Estimation and inference of treatment effects with L2-boosting in high-dimensional settings," Journal of Econometrics, Elsevier, vol. 234(2), pages 714-731.
    26. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.
    27. Isaac K. Ofori & Camara K. Obeng & Simplice A. Asongu, 2024. "What Really Drives Economic Growth in Sub-Saharan Africa? Evidence from the Lasso Regularization and Inferential Techniques," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 144-179, March.
    28. Ofori, Isaac K. & Quaidoo, Christopher & Ofori, Pamela E., 2021. "What Drives Financial Sector Development in Africa? Insights from Machine Learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue forthcomi.
    29. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
    30. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    31. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2021. "Economic predictions with big data: the illusion of sparsity," Working Paper Series 2542, European Central Bank.
    32. Rahul Singh, 2021. "Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension," Papers 2102.11076, arXiv.org, revised Jul 2024.
    33. Jannis Kueck & Ye Luo & Martin Spindler & Zigan Wang, 2017. "Estimation and Inference of Treatment Effects with $L_2$-Boosting in High-Dimensional Settings," Papers 1801.00364, arXiv.org, revised Jul 2021.
    34. Connor Lennon & Edward Rubin & Glen Waddell, 2025. "Machine learning the first stage in 2SLS: Practical guidance from bias decomposition and simulation," Papers 2505.13422, arXiv.org.
    35. Tom Boot & Didier Nibbering, 2017. "Inference in high-dimensional linear regression models," Tinbergen Institute Discussion Papers 17-032/III, Tinbergen Institute, revised 05 Jul 2017.
    36. Yanqin Fan & Fang Han & Wei Li & Xiao-Hua Zhou, 2019. "On rank estimators in increasing dimensions," Papers 1908.05255, arXiv.org.
    37. Yoshimasa Uematsu & Takashi Yamagata, 2020. "Inference in Weak Factor Models," ISER Discussion Paper 1080, Institute of Social and Economic Research, The University of Osaka.
    38. T. Tony Cai & Zijian Guo & Yin Xia, 2023. "Statistical inference and large-scale multiple testing for high-dimensional regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(4), pages 1135-1171, December.
    39. Nigmonov, Asror & Shams, Syed & Urbonas, Povilas, 2024. "Estimating probability of default via delinquencies? Evidence from European P2P lending market," Global Finance Journal, Elsevier, vol. 63(C).
    40. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
    41. Yves Staudt & Joël Wagner, 2021. "Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance," Risks, MDPI, vol. 9(3), pages 1-28, March.
    42. Ali Charkhi & Gerda Claeskens, 2018. "Asymptotic post-selection inference for the Akaike information criterion," Biometrika, Biometrika Trust, vol. 105(3), pages 645-664.
    43. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    44. Bach, Philipp & Klaassen, Sven & Kueck, Jannis & Spindler, Martin, 2025. "Estimation and uniform inference in sparse high-dimensional additive models," Journal of Econometrics, Elsevier, vol. 249(PB).
    45. Geonwoo Kim & Suyong Song, 2024. "Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables," Papers 2408.14671, arXiv.org.
    46. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    47. Neng-Chieh Chang, 2018. "Semiparametric Difference-in-Differences with Potentially Many Control Variables," Papers 1812.10846, arXiv.org, revised Jan 2019.
    48. Jean-Marie Dufour & Purevdorj Tuvaandorj, 2025. "Mixed LR-$C(\alpha)$-type tests for irregular hypotheses, general criterion functions and misspecified models," Papers 2510.17070, arXiv.org.
    49. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    50. Qizhao Chen & Vasilis Syrgkanis & Morgane Austern, 2022. "Debiased Machine Learning without Sample-Splitting for Stable Estimators," Papers 2206.01825, arXiv.org, revised Nov 2022.
    51. Uribe, Jorge M., 2025. "Investment in intangible assets and economic complexity," Research Policy, Elsevier, vol. 54(1).
    52. Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.
    53. Stephan Martin, 2022. "Estimation of Conditional Random Coefficient Models using Machine Learning Techniques," Papers 2201.08366, arXiv.org.
    54. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    55. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    56. Drukker, David M. & Liu, Di, 2025. "A cluster plugin method for selecting the GLM lasso tuning parameters in models for unbalanced panel data," Econometrics and Statistics, Elsevier, vol. 34(C), pages 14-31.
    57. Sandro Heiniger, 2024. "Data-driven model selection within the matrix completion method for causal panel data models," Papers 2402.01069, arXiv.org.

  96. Victor Chernozhukov & Wooyoung Kim & Sokbae (Simon) Lee & Adam Rosen, 2014. "Implementing intersection bounds in Stata," CeMMAP working papers 25/14, Institute for Fiscal Studies.

    Cited by:

    1. Ismael Mourifie & Marc Henry & Romuald Meango, 2017. "Sharp bounds and testability of a Roy model of STEM major choices," Papers 1709.09284, arXiv.org, revised Nov 2019.
    2. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    3. Ismael Mourifie & Yuanyuan Wan, 2014. "Testing Local Average Treatment Effect Assumptions," Working Papers tecipa-514, University of Toronto, Department of Economics.
    4. Santiago Acerenza & Otávio Bartalotti & Désiré Kédagni, 2023. "Testing identifying assumptions in bivariate probit models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 407-422, April.
    5. Cl'ement de Chaisemartin & Diego Ciccia & Xavier D'Haultf{oe}uille & Felix Knau, 2024. "Difference-in-Differences Estimators When No Unit Remains Untreated," Papers 2405.04465, arXiv.org, revised Aug 2025.
    6. Giustinelli, Pamela & Manski, Charles F. & Molinari, Francesca, 2022. "Tail and center rounding of probabilistic expectations in the Health and Retirement Study," Journal of Econometrics, Elsevier, vol. 231(1), pages 265-281.
    7. Chalak, Karim & Kim, Daniel, 2020. "Measurement error in multiple equations: Tobin’s q and corporate investment, saving, and debt," Journal of Econometrics, Elsevier, vol. 214(2), pages 413-432.
    8. Andrew E Clark & Rong Zhu, 2023. "Taking Back Control? Quasi-Experimental Evidence on the Impact of Retirement on Locus of Control," PSE Working Papers halshs-04335808, HAL.
    9. Kyunghoon Ban & D'esir'e K'edagni, 2021. "Nonparametric Bounds on Treatment Effects with Imperfect Instruments," Papers 2109.14785, arXiv.org.
    10. Henry, Marc & Méango, Romuald & Mourifié, Ismaël, 2024. "Role models and revealed gender-specific costs of STEM in an extended Roy model of major choice," Journal of Econometrics, Elsevier, vol. 238(2).
    11. Hiroaki Kaido, 2014. "Asymptotically efficient estimation of weighted average derivatives with an interval censored variable," CeMMAP working papers CWP03/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Lixiong Li & Désiré Kédagni & Ismaël Mourifié, 2024. "Discordant relaxations of misspecified models," Quantitative Economics, Econometric Society, vol. 15(2), pages 331-379, May.
    13. Molinari, Francesca, 2020. "Microeconometrics with partial identification," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 355-486, Elsevier.
    14. J. E. Prieger & A. Choi, 2024. "E-cigarettes and Smoking: Correlation, Causation, and Selection Bias," Journal of Consumer Policy, Springer, vol. 47(4), pages 471-498, December.
    15. Dang Hai-Anh H. & Huang Yang & Selod Harris, 2020. "Children Left Behind in China: The Role of School Fees," IZA Journal of Development and Migration, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 11(1), pages 1-29, January.
    16. Zahra Siddique, 2014. "Randomized control trials in an imperfect world," IZA World of Labor, LISER, pages 110-110, December.
    17. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    18. Bergeot, Julien & Tenand, Marianne, 2023. "Does informal care delay nursing home entry? Evidence from Dutch linked survey and administrative data," Journal of Health Economics, Elsevier, vol. 92(C).
    19. Kedagni, Desire, 2018. "Identifying Treatment Effects in the Presence of Confounded Types," ISU General Staff Papers 201809110700001056, Iowa State University, Department of Economics.
    20. Eibich, Peter, 2023. "Instrumental variable estimates of the burden of parental caregiving," The Journal of the Economics of Ageing, Elsevier, vol. 26(C).
    21. Eibich, Peter & Siedler, Thomas, 2020. "Retirement, intergenerational time transfers, and fertility," European Economic Review, Elsevier, vol. 124(C).
    22. Jan Priebe, 2020. "Quasi-experimental evidence for the causal link between fertility and subjective well-being," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(3), pages 839-882, July.
    23. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    24. Romuald Méango, 2016. "What Makes Brain Drain More Likely? Evidence from Sub-Saharan Africa," CESifo Working Paper Series 6209, CESifo.
    25. Wooyoung Kim & Koohyun Kwon & Soonwoo Kwon & Sokbae (Simon) Lee, 2014. "The identification power of smoothness assumptions in models with counterfactual outcomes," CeMMAP working papers CWP17/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    26. Eibich, Peter & Lorenti, Angelo & Mosca, Irene, 2022. "Does retirement affect voluntary work provision? Evidence from Europe and the U.S," Labour Economics, Elsevier, vol. 76(C).
    27. Clément de Chaisemartin & Diego Ciccia & Xavier D’Haultfoeuille & Felix Knau, 2024. "Two-way Fixed Effects and Differences-in-Differences in Heterogeneous Adoption Designs without Stayers," Working Papers 2025-01, Center for Research in Economics and Statistics.
    28. Zhu, Rong & Onur, Ilke, 2023. "Does retirement (really) increase informal caregiving? Quasi-experimental evidence from Australia," Journal of Health Economics, Elsevier, vol. 87(C).

  97. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2014. "Inference in High Dimensional Panel Models with an Application to Gun Control," Papers 1411.6507, arXiv.org.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Achim Ahrens & Sean Lyons, 2021. "Do rising rents lead to longer commutes? A gravity model of commuting flows in Ireland," Urban Studies, Urban Studies Journal Limited, vol. 58(2), pages 264-279, February.
    3. Julián Caballero & Christian Upper, 2023. "What happens to EMEs when US yields go up?," BIS Working Papers 1081, Bank for International Settlements.
    4. Moritz Meister & Annekatrin Niebuhr & Jan Cornelius Peters & Johannes Stiller, 2023. "Local attributes and migration balance – evidence for different age and skill groups from a machine learning approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(4), pages 794-825, May.
    5. Meera Mahadevan, 2024. "The Price of Power: Costs of Political Corruption in Indian Electricity," American Economic Review, American Economic Association, vol. 114(10), pages 3314-3344, October.
    6. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    7. Uckat, Hannah Irmela, 2023. "Leaning in at Home : Women's Promotions and Intra-household Bargaining in Bangladesh," Policy Research Working Paper Series 10370, The World Bank.
    8. Kaicheng Chen, 2025. "Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity," Papers 2504.18772, arXiv.org, revised Dec 2025.
    9. Harrison Fell & Melinda Sandler Morrill, 2024. "The Impact of Wind Energy on Air Pollution and Emergency Department Visits," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(1), pages 287-320, January.
    10. Fluchtmann, Jonas & Glenny, Anita Marie & Harmon, Nikolaj & Maibom, Jonas, 2021. "The Gender Application Gap: Do Men and Women Apply for the Same Jobs?," IZA Discussion Papers 14906, IZA Network @ LISER.
    11. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    12. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    13. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    14. Anders Bredahl Kock & Haihan Tang, 2014. "Inference in High-dimensional Dynamic Panel Data Models," CREATES Research Papers 2014-58, Department of Economics and Business Economics, Aarhus University.
    15. Julián Caballero, 2020. "Corporate dollar debt and depreciations: all's well that ends well?," BIS Working Papers 879, Bank for International Settlements.
    16. Mathilda Eriksson & Alejandro del Valle & Alejandro de la Fuente, 2025. "Droughts worsen air quality and health by shifting power generation," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
    17. Godzinski, Alexandre & Suarez Castillo, Milena, 2021. "Disentangling the effects of air pollutants with many instruments," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    18. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    19. Akash Raja, 2023. "The impact of changes in bank capital requirements," Bank of England working papers 1004, Bank of England.
    20. Paul S. Clarke & Annalivia Polselli, 2023. "Double Machine Learning for Static Panel Models with Fixed Effects," Papers 2312.08174, arXiv.org, revised Dec 2024.
    21. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019. "lassopack: Model selection and prediction with regularized regression in Stata," Papers 1901.05397, arXiv.org.
    22. Damian Kozbur, 2015. "Testing-Based Forward Model Selection," ECON - Working Papers 283, Department of Economics - University of Zurich, revised Apr 2018.
    23. Marta Serra-Garcia & Uri Gneezy, 2023. "Improving Human Deception Detection Using Algorithmic Feedback," CESifo Working Paper Series 10518, CESifo.
    24. Vogt, M. & Walsh, C. & Linton, O., 2022. "CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects," Cambridge Working Papers in Economics 2242, Faculty of Economics, University of Cambridge.
    25. Szabó-Morvai Ágnes & Hubert János Kiss, 2020. "Locus of control and Human Capital Investment Decisions: The Role of Effort, Parental Preferences and Financial Constraints," CERS-IE WORKING PAPERS 2055, Institute of Economics, Centre for Economic and Regional Studies.
    26. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    27. Achim Ahrens & Alessandra Stampi-Bombelli & Selina Kurer & Dominik Hangartner, 2023. "Optimal multi-action treatment allocation: A two-phase field experiment to boost immigrant naturalization," Papers 2305.00545, arXiv.org, revised Feb 2024.
    28. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
    29. Francesca Micocci & Armando Rungi, 2021. "Predicting Exporters with Machine Learning," Papers 2107.02512, arXiv.org, revised Sep 2022.
    30. Borgschulte, Mark & Vogler, Jacob, 2019. "Did the ACA Medicaid Expansion Save Lives?," IZA Discussion Papers 12552, IZA Network @ LISER.
    31. Qiu, Yun & Chen, Xi & Shi, Wei, 2020. "Impacts of Social and Economic Factors on the Transmission of Coronavirus Disease 2019 (COVID-19) in China," GLO Discussion Paper Series 494 [pre.], Global Labor Organization (GLO).
    32. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    33. María Laura Alzua & Natalia Cantet & Ana C. Dammert & Damilola Olajide, 2023. "The Wellbeing Effects of an Old Age Pension: Experimental Evidence for Ekiti State in Nigeria," CEDLAS, Working Papers 0322, CEDLAS, Universidad Nacional de La Plata.
    34. Jonathan Fuhr & Dominik Papies, 2024. "Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions," Papers 2409.01266, arXiv.org.
    35. Mert Hakan Hekimoğlu & Burak Kazaz, 2020. "Analytics for Wine Futures: Realistic Prices," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2096-2120, September.
    36. Achim Ahrens, 2015. "Civil conflicts in Africa: Climate, economic shocks, nighttime lights and spill-over effects," SEEC Discussion Papers 1501, Spatial Economics and Econometrics Centre, Heriot Watt University.
    37. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2020. "Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application," Papers 2008.03600, arXiv.org, revised Nov 2021.
    38. Linton, O. B. & Rücker, M. & Vogt, M. & Walsh, C., 2024. "Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects," Cambridge Working Papers in Economics 2467, Faculty of Economics, University of Cambridge.
    39. Breinlich, Holger & Corradi, Valentina & Rocha, Nadia & Ruta, Michele & Silva, J.M.C. Santos & Zylkin, Tom, 2021. "Machine learning in international trade research - evaluating the impact of trade agreements," LSE Research Online Documents on Economics 114379, London School of Economics and Political Science, LSE Library.
    40. Damian Kozbur, 2020. "Analysis of Testing‐Based Forward Model Selection," Econometrica, Econometric Society, vol. 88(5), pages 2147-2173, September.
    41. Danquah, Michael & Iddrisu, Abdul Malik & Boakye, Ernest Owusu & Owusu, Solomon, 2021. "Do gender wage differences within households influence women's empowerment and welfare? Evidence from Ghana," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 916-932.
    42. WANG, Hongming, 2025. "Quantifying the Mortality Consequences of Climate Change : Evidence from Japan," Discussion paper series HIAS-E-143, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    43. Max Vilgalys, 2023. "A Machine Learning Approach to Measuring Climate Adaptation," Papers 2302.01236, arXiv.org.
    44. Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.
    45. Lamarche, Carlos & Parker, Thomas, 2023. "Wild bootstrap inference for penalized quantile regression for longitudinal data," Journal of Econometrics, Elsevier, vol. 235(2), pages 1799-1826.
    46. Samuel Dodini, 2023. "Insurance Subsidies, the Affordable Care Act, and Financial Stability," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(1), pages 97-136, January.
    47. Collins, Alan & Fan, Jingwen & Mahabir, Aruneema, 2022. "Actual versus ‘natural’ rates of suicide: Evidence from the USA," Economic Modelling, Elsevier, vol. 106(C).
    48. Wei Shi & Lung-fei Lee, 2018. "The effects of gun control on crimes: a spatial interactive fixed effects approach," Empirical Economics, Springer, vol. 55(1), pages 233-263, August.
    49. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2019. "Multiway Cluster Robust Double/Debiased Machine Learning," Papers 1909.03489, arXiv.org, revised Mar 2020.
    50. Duncan Sheppard Gilchrist & Emily Glassberg Sands, 2016. "Something to Talk About: Social Spillovers in Movie Consumption," Journal of Political Economy, University of Chicago Press, vol. 124(5), pages 1339-1382.
    51. Muhammad Ramzan & Hong Li, 2025. "An analytical framework to link factors affecting agricultural trade intensity in the world: pathways to sustainable agricultural development 2030 agenda," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(1), pages 1223-1272, January.
    52. Falco J. Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2023. "Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values," Papers 2306.08165, arXiv.org.
    53. James M. Carson & Cameron M. Ellis & Robert E. Hoyt & Krzysztof Ostaszewski, 2020. "Sunk Costs and Screening: Two‐Part Tariffs in Life Insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(3), pages 689-718, September.
    54. Eriksson,Mathilda & del Valle,Alejandro & De La Fuente,Alejandro, 2024. "Droughts Worsen Air Quality by Shifting Power Generation in Latin America and the Caribbean," Policy Research Working Paper Series 10760, The World Bank.
    55. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
    56. J. Daniel Aromí & M. Paula Bonel & Julián Cristiá & Martín Llada, 2020. "Socio-economic status and mobility during the COVID-19 pandemic: An analysis of large Latin American urban areas," Asociación Argentina de Economía Política: Working Papers 4307, Asociación Argentina de Economía Política.
    57. Carlos Aller & Lorenzo Ductor & Daryna Grechyna, 2020. "Robust Determinants of CO2 Emissions," ThE Papers 20/13, Department of Economic Theory and Economic History of the University of Granada..
    58. Damian Kozbur, 2013. "Inference in additively separable models with a high-dimensional set of conditioning variables," ECON - Working Papers 284, Department of Economics - University of Zurich, revised Apr 2018.
    59. Xi Chen & Ye Luo & Martin Spindler, 2019. "Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data," Papers 1912.12867, arXiv.org, revised Jan 2020.
    60. Harold D. Chiang, 2018. "Many Average Partial Effects: with An Application to Text Regression," Papers 1812.09397, arXiv.org, revised Jan 2022.
    61. Fonseca Morello, Thiago, 2023. "Hospitalization due to fire-induced pollution in the Brazilian Amazon: A causal inference analysis with an assessment of policy trade-offs," World Development, Elsevier, vol. 161(C).
    62. Luv Sharma & Aravind Chandrasekaran & Elliot Bendoly, 2020. "Does the Office of Patient Experience Matter in Improving Delivery of Care?," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 833-855, April.
    63. Joseph S. Shapiro, 2020. "The Environmental Bias of Trade Policy," NBER Working Papers 26845, National Bureau of Economic Research, Inc.
    64. Brian Asquith, 2019. "Do Rent Increases Reduce the Housing Supply Under Rent Control? Evidence from Evictions in San Francisco," Upjohn Working Papers 19-296, W.E. Upjohn Institute for Employment Research.
    65. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    66. Damian Kozbur, 2017. "Sharp convergence rates for forward regression in high-dimensional sparse linear models," ECON - Working Papers 253, Department of Economics - University of Zurich, revised Apr 2018.
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    68. Davide Viviano & Jelena Bradic, 2021. "Dynamic covariate balancing: estimating treatment effects over time with potential local projections," Papers 2103.01280, arXiv.org, revised Feb 2026.
    69. Rossmann, Tobias, 2019. "Does Experience Shape Subjective Expectations?," Rationality and Competition Discussion Paper Series 181, CRC TRR 190 Rationality and Competition.
    70. Drukker, David M. & Liu, Di, 2025. "A cluster plugin method for selecting the GLM lasso tuning parameters in models for unbalanced panel data," Econometrics and Statistics, Elsevier, vol. 34(C), pages 14-31.
    71. Harold D. Chiang & Joel Rodrigue & Yuya Sasaki, 2019. "Post-Selection Inference in Three-Dimensional Panel Data," Papers 1904.00211, arXiv.org, revised Apr 2019.

  98. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2014. "Program evaluation with high-dimensional data," CeMMAP working papers 33/14, Institute for Fiscal Studies.

    Cited by:

    1. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    2. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    3. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2014. "Inference in High Dimensional Panel Models with an Application to Gun Control," Papers 1411.6507, arXiv.org.
    4. Damian Kozbur, 2015. "Testing-Based Forward Model Selection," ECON - Working Papers 283, Department of Economics - University of Zurich, revised Apr 2018.
    5. Matias D. Cattaneo & Michael Jansson, 2014. "Bootstrapping Kernel-Based Semiparametric Estimators," CREATES Research Papers 2014-25, Department of Economics and Business Economics, Aarhus University.
    6. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
    7. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    8. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers 47/16, Institute for Fiscal Studies.
    9. Danquah, Michael & Iddrisu, Abdul Malik & Boakye, Ernest Owusu & Owusu, Solomon, 2021. "Do gender wage differences within households influence women's empowerment and welfare? Evidence from Ghana," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 916-932.
    10. Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
    11. Kasy Maximilian, 2019. "Uniformity and the Delta Method," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-19, January.
    12. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Papers 1312.7186, arXiv.org, revised Jun 2016.
    13. Shao, Shuai & Xu, Le & Yang, Lili & Yu, Dianfan, 2024. "How do energy-saving policies improve environmental quality: Evidence from China’s Top 10,000 energy-consuming enterprises program," World Development, Elsevier, vol. 175(C).
    14. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
    15. Ning Xu & Jian Hong & Timothy C. G. Fisher, 2016. "Finite-sample and asymptotic analysis of generalization ability with an application to penalized regression," Papers 1609.03344, arXiv.org, revised Sep 2016.
    16. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers CWP47/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Braverman, Mark & Chassang, Sylvain, 2022. "Data-driven incentive alignment in capitation schemes," Journal of Public Economics, Elsevier, vol. 207(C).
    18. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers CWP62/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    21. Susan Athey & Guido Imbens, 2016. "The State of Applied Econometrics - Causality and Policy Evaluation," Papers 1607.00699, arXiv.org.
    22. Thai T. Pham & Yuanyuan Shen, 2017. "A Deep Causal Inference Approach to Measuring the Effects of Forming Group Loans in Online Non-profit Microfinance Platform," Papers 1706.02795, arXiv.org.
    23. Guido Imbens, 2019. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," NBER Working Papers 26104, National Bureau of Economic Research, Inc.

  99. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2014. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Papers 1412.8434, arXiv.org, revised Sep 2015.

    Cited by:

    1. Marcel Klatt & Axel Munk & Yoav Zemel, 2022. "Limit laws for empirical optimal solutions in random linear programs," Annals of Operations Research, Springer, vol. 315(1), pages 251-278, August.
    2. Dmitry Arkhangelsky, 2019. "Dealing with a Technological Bias: The Difference-in-Difference Approach," Working Papers wp2019_1903, CEMFI.
    3. Gunsilius, Florian F., 2023. "A condition for the identification of multivariate models with binary instruments," Journal of Econometrics, Elsevier, vol. 235(1), pages 220-238.
    4. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Sciences Po Economics Publications (main) hal-03936221, HAL.
    5. Hamel, Andreas H. & Kostner, Daniel, 2018. "Cone distribution functions and quantiles for multivariate random variables," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 97-113.
    6. Jun Yu & Mingyao Ai & Zhiqiang Ye, 2024. "A review on design inspired subsampling for big data," Statistical Papers, Springer, vol. 65(2), pages 467-510, April.
    7. Marc Hallin & Hongjian Shi & Mathias Drton & Fang Han, 2021. "Center-Outward Sign- and Rank-Based Quadrant, Spearman, and Kendall Tests for Multivariate Independence," Working Papers ECARES 2021-27, ULB -- Universite Libre de Bruxelles.
    8. Manuel Arellano & Stéphane Bonhomme, 2019. "Recovering Latent Variables by Matching," Working Papers wp2019_1914, CEMFI.
    9. María Edo & Walter Sosa Escudero & Marcela Svarc, 2021. "A multidimensional approach to measuring the middle class," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(1), pages 139-162, March.
    10. Lixiong Li & Marc Henry, 2022. "Finite Sample Inference in Incomplete Models," Papers 2204.00473, arXiv.org, revised Oct 2025.
    11. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    12. Florian F Gunsilius, 2025. "A primer on optimal transport for causal inference with observational data," Papers 2503.07811, arXiv.org, revised Mar 2025.
    13. Hudecová, Šárka & Šiman, Miroslav, 2022. "Multivariate ranks based on randomized lift-interdirections," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    14. Yanqin Fan & Marc Henry, 2020. "Vector copulas," Papers 2009.06558, arXiv.org, revised Apr 2021.
    15. Marc Hallin & Davide La Vecchia & Hang Liu, 2020. "Rank-Based Testing for Semiparametric VAR Models: a measure transportation approach," Working Papers ECARES 2020-47, ULB -- Universite Libre de Bruxelles.
    16. Kotík, Lukáš & Hlubinka, Daniel, 2017. "A weighted localization of halfspace depth and its properties," Journal of Multivariate Analysis, Elsevier, vol. 157(C), pages 53-69.
    17. Alberto González-Sanz & Marc Hallin & Bodhisattva Sen, 2023. "Monotone Measure-Preserving Maps in Hilbert Spaces: Existence, Uniqueness, and Stability," Working Papers ECARES 2023-10, ULB -- Universite Libre de Bruxelles.
    18. Olivier Paul Faugeras & Ludger Rüschendorf, 2021. "Functional, randomized and smoothed multivariate quantile regions," Post-Print hal-03352330, HAL.
    19. Marc Hallin & Davide La Vecchia & H Liu, 2019. "Center-Outward R-Estimation for Semiparametric VARMA Models," Working Papers ECARES 2019-25, ULB -- Universite Libre de Bruxelles.
    20. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich towards a “General Generalised Distance”," Working Papers ECARES 2018-12, ULB -- Universite Libre de Bruxelles.
    21. Fan, Yanqin & Henry, Marc, 2023. "Vector copulas," Journal of Econometrics, Elsevier, vol. 234(1), pages 128-150.
    22. Marc Hallin & Daniel Hlubinka & Sarka Hudecova, 2020. "Fully Distribution-free Center-outward Rank Tests for Multiple-output Regression and Manova," Working Papers ECARES 2020-32, ULB -- Universite Libre de Bruxelles.
    23. Jan Bierlant & Sven Buitendag & Eustasio Del Barrio & Marc Hallin, 2019. "Center-Outward Quantiles And The Measurement Of Multivariate Risk," Working Papers ECARES 2019-30, ULB -- Universite Libre de Bruxelles.
    24. Petra Laketa & Stanislav Nagy, 2022. "Halfspace depth for general measures: the ray basis theorem and its consequences," Statistical Papers, Springer, vol. 63(3), pages 849-883, June.
    25. Davy Paindaveine & Germain Van Bever, 2017. "Halfspace Depths for Scatter, Concentration and Shape Matrices," Working Papers ECARES ECARES 2017-19, ULB -- Universite Libre de Bruxelles.
    26. Marc Hallin & Hang Liu, 2022. "Center-outward Rank- and Sign-based VARMA Portmanteau Tests," Working Papers ECARES 2022-27, ULB -- Universite Libre de Bruxelles.
    27. Hudecová, Šárka & Šiman, Miroslav, 2024. "Stochastic hyperplane-based ranks and their use in multivariate portmanteau tests," Journal of Multivariate Analysis, Elsevier, vol. 204(C).
    28. Marc Hallin & Daniel Hlubinka & Sarka Hudecova, 2021. "Efficient Fully Distribution-Free Center-Outward Rank Tests for Multiple-Output Regression and MANOVA," Working Papers ECARES 2021-13, ULB -- Universite Libre de Bruxelles.
    29. Florian Gunsilius, 2018. "Point-identification in multivariate nonseparable triangular models," Papers 1806.09680, arXiv.org.
    30. Marc Hallin, 2018. "From Mahalanobis to Bregman via Monge and Kantorovich," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 135-146, December.
    31. Mendroš, Erik & Nagy, Stanislav, 2025. "Explicit bivariate simplicial depth," Journal of Multivariate Analysis, Elsevier, vol. 205(C).
    32. Yanqin Fan & Marc Henry & Brendan Pass & Jorge A. Rivero, 2022. "Lorenz map, inequality ordering and curves based on multidimensional rearrangements," Papers 2203.09000, arXiv.org, revised Apr 2024.
    33. Faugeras, Olivier P. & Rüschendorf, Ludger, 2021. "Functional, randomized and smoothed multivariate quantile regions," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    34. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    35. Marc Hallin & H Lui & Thomas Verdebout, 2022. "Nonparametric Measure-transportation-based Methods for Directional Data," Working Papers ECARES 2022-18, ULB -- Universite Libre de Bruxelles.
    36. Marc Hallin & Gilles Mordant, 2021. "On the Finite-Sample Performance of Measure Transportation-Based Multivariate Rank Tests," Working Papers ECARES 2021-24, ULB -- Universite Libre de Bruxelles.
    37. Hongjian Shi & Marc Hallin & Mathias Drton & Fang Han, 2020. "Rate-Optimality of Consistent Distribution-Free Tests of Independence Based on Center-Outward Ranks and Signs," Working Papers ECARES 2020-23, ULB -- Universite Libre de Bruxelles.
    38. Eustasio Del Barrio & Juan Cuesta Albertos & Marc Hallin & Carlos Matran, 2018. "Smooth Cyclically Monotone Interpolation and Empirical Center-Outward Distribution Functions," Working Papers ECARES 2018-15, ULB -- Universite Libre de Bruxelles.
    39. Eustasio del Barrio & Alberto González-Sanz & Marc Hallin, 2022. "Nonparametric Multiple-Output Center-Outward Quantile Regression," Working Papers ECARES 2022-10, ULB -- Universite Libre de Bruxelles.
    40. Alfred Galichon & Bernard Salani'e, 2021. "Cupid's Invisible Hand: Social Surplus and Identification in Matching Models," Papers 2106.02371, arXiv.org, revised Jan 2023.
    41. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.
    42. Florian Gunsilius & Susanne M. Schennach, 2019. "Independent nonlinear component analysis," CeMMAP working papers CWP46/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  100. Guillaume Carlier & Victor Chernozhukov & Alfred Galichon, 2014. "Vector quantile regression," CeMMAP working papers 48/14, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Alfred Galichon & Marc Hallin & Marc Henry, 2015. "Monge-Kantorovich Depth, Quantiles, Ranks, and Signs," Working Papers hal-03460056, HAL.
    2. María Edo & Walter Sosa Escudero & Marcela Svarc, 2021. "A multidimensional approach to measuring the middle class," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(1), pages 139-162, March.
    3. Stefan Holst Bache & Christian M. Dahl & Johannes Tang, "undated". "Headlights on tobacco road to low birthweight outcomes - Evidence from a battery of quantile regression estimators and a heterogeneous panelCreation-Date: 20080508," CREATES Research Papers 2008-20, Department of Economics and Business Economics, Aarhus University.

  101. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.

    Cited by:

    1. Chernozhukov, Victor & Fernández-Val, Iván & Hoderlein, Stefan & Holzmann, Hajo & Newey, Whitney, 2015. "Nonparametric identification in panels using quantiles," Journal of Econometrics, Elsevier, vol. 188(2), pages 378-392.
    2. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

  102. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform Post Selection Inference for LAD Regression and Other Z-estimation problems," Papers 1304.0282, arXiv.org, revised Oct 2020.

    Cited by:

    1. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    2. Fan, Yanqin & Han, Fang & Li, Wei & Zhou, Xiao-Hua, 2020. "On rank estimators in increasing dimensions," Journal of Econometrics, Elsevier, vol. 214(2), pages 379-412.
    3. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    4. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    5. Sven Klaassen & Jannis Kuck & Martin Spindler & Victor Chernozhukov, 2018. "Uniform Inference in High-Dimensional Gaussian Graphical Models," Papers 1808.10532, arXiv.org, revised Dec 2018.
    6. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    7. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    9. Victor Chernozhukov & Wolfgang K. Hardle & Chen Huang & Weining Wang, 2018. "LASSO-Driven Inference in Time and Space," Papers 1806.05081, arXiv.org, revised May 2020.
    10. Sven Klaassen & Jannis Kueck & Martin Spindler, 2017. "Transformation Models in High-Dimensions," Papers 1712.07364, arXiv.org.
    11. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Jul 2025.
    12. Le-Yu Chen & Sokbae Lee, 2020. "Sparse Quantile Regression," Papers 2006.11201, arXiv.org, revised Mar 2023.
    13. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Papers 1312.7186, arXiv.org, revised Jun 2016.
    14. Rahul Singh, 2021. "Kernel Ridge Riesz Representers: Generalization, Mis-specification, and the Counterfactual Effective Dimension," Papers 2102.11076, arXiv.org, revised Jul 2024.
    15. Yanqin Fan & Fang Han & Wei Li & Xiao-Hua Zhou, 2019. "On rank estimators in increasing dimensions," Papers 1908.05255, arXiv.org.
    16. Jiaying Gu & Stanislav Volgushev, 2018. "Panel Data Quantile Regression with Grouped Fixed Effects," Papers 1801.05041, arXiv.org, revised Aug 2018.
    17. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    18. Doğan, Osman & Taşpınar, Süleyman & Bera, Anil K., 2021. "A Bayesian robust chi-squared test for testing simple hypotheses," Journal of Econometrics, Elsevier, vol. 222(2), pages 933-958.
    19. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers CWP22/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," American Economic Review, American Economic Association, vol. 105(5), pages 486-490, May.
    21. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "High-Dimensional Metrics in R," Papers 1603.01700, arXiv.org, revised Aug 2016.
    22. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    23. Zhentao Shi & Jingyi Huang, 2019. "Forward-Selected Panel Data Approach for Program Evaluation," Papers 1908.05894, arXiv.org, revised Apr 2021.
    24. Matthew Backus & Sida Peng, 2019. "On Testing Continuity and the Detection of Failures," NBER Working Papers 26016, National Bureau of Economic Research, Inc.
    25. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández‐Val, 2025. "Fisher–Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India," Econometrica, Econometric Society, vol. 93(4), pages 1121-1164, July.

  103. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers 70/13, Institute for Fiscal Studies.

    Cited by:

    1. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, "undated". "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    2. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    3. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    5. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Post-Selection Inference for Generalized Linear Models with Many Controls," Papers 1304.3969, arXiv.org, revised Mar 2016.
    6. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression and other z-estimation problems," CeMMAP working papers 74/13, Institute for Fiscal Studies.
    7. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression models," CeMMAP working papers 24/13, Institute for Fiscal Studies.
    8. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Honest confidence regions for a regression parameter in logistic regression with a large number of controls," CeMMAP working papers 67/13, Institute for Fiscal Studies.
    10. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," American Economic Review, American Economic Association, vol. 105(5), pages 486-490, May.
    11. Rong Jiang & Mengxian Sun, 2022. "Single-index composite quantile regression for ultra-high-dimensional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 443-460, June.

  104. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Inference on causal and structural parameters using many moment inequalities," Papers 1312.7614, arXiv.org, revised Oct 2018.

    Cited by:

    1. Li, Jia & Liao, Zhipeng & Zhou, Wenyu, 2025. "A general test for functional inequalities," Journal of Econometrics, Elsevier, vol. 251(C).
    2. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    3. Daniel Kornbluth & Alexey Kushnir, 2024. "Undergraduate Course Allocation through Competitive Markets," Papers 2412.05691, arXiv.org, revised Dec 2025.
    4. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    5. Gafarov, Bulat, 2025. "Simple subvector inference on sharp identified set in affine models," Journal of Econometrics, Elsevier, vol. 249(PB).
    6. Denni Tommasi & Lina Zhang, 2020. "Bounding Program Benefits When Participation is Misreported," Monash Econometrics and Business Statistics Working Papers 24/20, Monash University, Department of Econometrics and Business Statistics.
    7. D'Haultfoeuille, Xavier & Gaillac, Christophe & Maurel, Arnaud, 2018. "Rationalizing Rational Expectations? Tests and Deviations," IZA Discussion Papers 11989, IZA Network @ LISER.
    8. Lixiong Li & Marc Henry, 2022. "Finite Sample Inference in Incomplete Models," Papers 2204.00473, arXiv.org, revised Oct 2025.
    9. Parker, Thomas, 2019. "Asymptotic inference for the constrained quantile regression process," Journal of Econometrics, Elsevier, vol. 213(1), pages 174-189.
    10. Wei Miao & Jad Beyhum & Jonas Striaukas & Ingrid Van Keilegom, 2025. "High-dimensional censored MIDAS logistic regression for corporate survival forecasting," Papers 2502.09740, arXiv.org, revised Feb 2026.
    11. Ryo Okui, 2021. "A moment inequality approach to statistical inference for rankings," The Japanese Economic Review, Springer, vol. 72(2), pages 169-184, April.
    12. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    13. Chesher, Andrew & Kim, Dongwoo & Rosen, Adam M., 2023. "IV methods for Tobit models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1700-1724.
    14. Molinari, Francesca, 2020. "Microeconometrics with partial identification," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 355-486, Elsevier.
    15. Steven T. Berry & Giovanni Compiani, 2020. "An Instrumental Variable Approach to Dynamic Models," Working Papers 2020-106, Becker Friedman Institute for Research In Economics.
    16. Kutta, Tim & Dette, Holger, 2024. "Validating approximate slope homogeneity in large panels," Journal of Econometrics, Elsevier, vol. 246(1).
    17. , 2023. "Price Competition and Endogenous Product Choice in Networks: Evidence from the US airline Industry," Working Papers 950, Queen Mary University of London, School of Economics and Finance.
    18. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2019. "Improved Central Limit Theorem and bootstrap approximations in high dimensions," Papers 1912.10529, arXiv.org, revised May 2022.
    19. Rami V. Tabri & Christopher D. Walker, 2020. "Inference for Moment Inequalities: A Constrained Moment Selection Procedure," Papers 2008.09021, arXiv.org, revised Aug 2020.
    20. Feng, Long & Lan, Wei & Liu, Binghui & Ma, Yanyuan, 2022. "High-dimensional test for alpha in linear factor pricing models with sparse alternatives," Journal of Econometrics, Elsevier, vol. 229(1), pages 152-175.
    21. Su, Liangjun & Jin, Sainan & Wang, Xia, 2025. "Sieve estimation of state-varying factor models," Journal of Econometrics, Elsevier, vol. 251(C).
    22. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised May 2025.
    23. Yu, Xiufan & Yao, Jiawei & Xue, Lingzhou, 2024. "Power enhancement for testing multi-factor asset pricing models via Fisher’s method," Journal of Econometrics, Elsevier, vol. 239(2).
    24. Timmermann, Allan & Qu, Ritong & Zhu, Yinchu, 2019. "Do Any Economists Have Superior Forecasting Skills?," CEPR Discussion Papers 14112, C.E.P.R. Discussion Papers.
    25. Gregory Fletcher Cox, 2024. "A Simple and Adaptive Confidence Interval when Nuisance Parameters Satisfy an Inequality," Papers 2409.09962, arXiv.org.
    26. Andreas Dzemski & Ryo Okui, 2024. "Confidence set for group membership," Quantitative Economics, Econometric Society, vol. 15(2), pages 245-277, May.
    27. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org, revised Nov 2024.
    28. Beyhum, Jad & Striaukas, Jonas, 2024. "Testing for sparse idiosyncratic components in factor-augmented regression models," Journal of Econometrics, Elsevier, vol. 244(1).
    29. Kirill Ponomarev & Vira Semenova, 2024. "On the Lower Confidence Band for the Optimal Welfare in Policy Learning," Papers 2410.07443, arXiv.org, revised Sep 2025.
    30. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    31. Shosei Sakaguchi, 2024. "Partial identification and inference in duration models with endogenous censoring," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 308-326, March.
    32. Likai Chen & Georg Keilbar & Liangjun Su & Weining Wang, 2023. "Inference on many jumps in nonparametric panel regression models," Papers 2312.01162, arXiv.org, revised Jan 2025.
    33. Evan K. Rose & Yotam Shem-Tov, 2021. "On Recoding Ordered Treatments as Binary Indicators," Papers 2111.12258, arXiv.org, revised Mar 2024.
    34. Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023. "Lasso inference for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
    35. Heiler, Phillip, 2024. "Heterogeneous treatment effect bounds under sample selection with an application to the effects of social media on political polarization," Journal of Econometrics, Elsevier, vol. 244(1).
    36. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
    37. Nick Koning & Paul Bekker, 2019. "Exact Testing of Many Moment Inequalities Against Multiple Violations," Papers 1904.12775, arXiv.org, revised Jun 2020.
    38. Semenova, Vira, 2025. "Generalized Lee bounds," Journal of Econometrics, Elsevier, vol. 251(C).
    39. Chesher, Andrew & Rosen, Adam M., 2020. "Generalized instrumental variable models, methods, and applications," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 1-110, Elsevier.
    40. He, Yi & Jaidee, Sombut & Gao, Jiti, 2023. "Most powerful test against a sequence of high dimensional local alternatives," Journal of Econometrics, Elsevier, vol. 234(1), pages 151-177.
    41. Yi He & Sombut Jaidee & Jiti Gao, 2020. "Most Powerful Test against High Dimensional Free Alternatives," Monash Econometrics and Business Statistics Working Papers 13/20, Monash University, Department of Econometrics and Business Statistics.
    42. Obradović, Filip, 2024. "Measuring diagnostic test performance using imperfect reference tests: A partial identification approach," Journal of Econometrics, Elsevier, vol. 244(1).
    43. Jinyong Hahn & Guido Kuersteiner & Andres Santos & Wavid Willigrod, 2024. "Overidentification in Shift-Share Designs," Papers 2404.17049, arXiv.org.
    44. Chang, Jinyuan & Jiang, Qing & Shao, Xiaofeng, 2023. "Testing the martingale difference hypothesis in high dimension," Journal of Econometrics, Elsevier, vol. 235(2), pages 972-1000.

  105. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Honest confidence regions for a regression parameter in logistic regression with a large number of controls," CeMMAP working papers 67/13, Institute for Fiscal Studies.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    2. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    3. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    4. Fedele, Alessandro & Tonin, Mirco & Wiesen, Daniel, 2025. "Self-Selection into Health Professions," IZA Discussion Papers 17941, IZA Network @ LISER.
    5. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Valid post-selection inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP53/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers CWP28/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2016. "Locally Robust Semiparametric Estimation," Papers 1608.00033, arXiv.org, revised Aug 2020.
    8. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2014. "Program evaluation with high-dimensional data," CeMMAP working papers CWP33/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
    10. Yichen Gao & Yu Zhang & Ximing Wu, 2015. "Penalized exponential series estimation of copula densities with an application to intergenerational dependence of body mass index," Empirical Economics, Springer, vol. 48(1), pages 61-81, February.
    11. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    12. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Stijn Vansteelandt & Oliver Dukes, 2022. "Assumption‐lean inference for generalised linear model parameters," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 657-685, July.
    14. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. van de Geer, Sara, 2016. "Worst possible sub-directions in high-dimensional models," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 248-260.
    16. Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.
    17. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"," Papers 1305.6099, arXiv.org, revised Jun 2013.

  106. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Post-Selection Inference for Generalized Linear Models with Many Controls," Papers 1304.3969, arXiv.org, revised Mar 2016.

    Cited by:

    1. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
    2. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    3. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    4. Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
    5. Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2022. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," Management Science, INFORMS, vol. 68(11), pages 7918-7939, November.
    6. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    7. Christis Katsouris, 2023. "High Dimensional Time Series Regression Models: Applications to Statistical Learning Methods," Papers 2308.16192, arXiv.org.
    8. Nebojša Stojčić & Matija Matić, 2023. "A Journey Toward Global Value Chain Upgrading: Exploring the Transition from Backward to Forward Integration," EFZG Working Papers Series 2308, Faculty of Economics and Business, University of Zagreb.
    9. Woodruff, Christopher & Menzel, Andreas, 2019. "Gender Wage Gaps and Worker Mobility: Evidence from the Garment Sector in Bangladesh," CEPR Discussion Papers 13577, C.E.P.R. Discussion Papers.
    10. Alex Coad & Stjepan Srhoj, 2020. "Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms," Small Business Economics, Springer, vol. 55(3), pages 541-565, October.
    11. Yukun Ma, 2023. "Identification-robust inference for the LATE with high-dimensional covariates," Papers 2302.09756, arXiv.org, revised Nov 2025.
    12. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP69/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    14. Helios Herrera & Massimo Morelli & Salvatore Nunnari, 2020. "A Theory of Power Wars," Working Papers 669, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    15. Robert Brooks & Brandon N. Cline & Pavel Teterin & Yu You, 2022. "The information in global interest rate futures contracts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(6), pages 1135-1166, June.
    16. Ethan X. Fang & Yang Ning & Han Liu, 2017. "Testing and confidence intervals for high dimensional proportional hazards models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1415-1437, November.
    17. Joann Jasiak & Purevdorj Tuvaandorj, 2023. "Penalized Likelihood Inference with Survey Data," Papers 2304.07855, arXiv.org.
    18. Shengfei Tang & Yanmei Shi & Qi Zhang, 2023. "Bias-Corrected Inference of High-Dimensional Generalized Linear Models," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    19. Ofori, Isaac K. & Quaidoo, Christopher & Ofori, Pamela E., 2021. "What Drives Financial Sector Development in Africa? Insights from Machine Learning," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue forthcomi.
    20. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Jing Kong, 2025. "Causal Inference in High-Dimensional Generalized Linear Models with Binary Outcomes," Papers 2510.16669, arXiv.org.
    22. Rocco Macchiavello & Andreas Menzel & Atonu Rabbani & Christopher Woodruff, 2020. "Challenges of Change: An Experiment Promoting Women to Managerial Roles in the Bangladeshi Garment Sector," NBER Working Papers 27606, National Bureau of Economic Research, Inc.
    23. Benito Arruñada & Marco Fabbri & Daniele Nosenzo & Giorgio Zanarone, 2025. "Insider Collusion as a Threat to Property Rights: Experimental Evidence from West Africa," Working Papers 2025-11, FEDEA.
    24. Sung Jae Jun & Sokbae Lee, 2022. "Average Adjusted Association: Efficient Estimation with High Dimensional Confounders," Papers 2205.14048, arXiv.org, revised Apr 2023.
    25. Harold D Chiang & Yukun Ma & Joel Rodrigue & Yuya Sasaki, 2021. "Dyadic double/debiased machine learning for analyzing determinants of free trade agreements," Papers 2110.04365, arXiv.org, revised Dec 2022.
    26. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised May 2025.
    27. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    28. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    29. Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.
    30. Joann Jasiak & Peter MacKenzie & Purevdorj Tuvaandorj, 2023. "Digital Divide: Empirical Study of CIUS 2020," Papers 2301.07855, arXiv.org, revised Oct 2024.
    31. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    32. Nigmonov, Asror & Shams, Syed & Urbonas, Povilas, 2024. "Estimating probability of default via delinquencies? Evidence from European P2P lending market," Global Finance Journal, Elsevier, vol. 63(C).
    33. Menzel, Andreas, 2021. "Knowledge exchange and productivity spill-overs in Bangladeshi garment factories," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 721-746.
    34. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    35. Sophie Brana & Dalila Chenaf-Nicet & Delphine Lahet, 2023. "Drivers of cross-border bank claims: The role of foreign-owned banks in emerging countries," Working Papers 2023.06, International Network for Economic Research - INFER.
    36. Falco J. Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2023. "Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values," Papers 2306.08165, arXiv.org.
    37. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
    38. Backes-Gellner, Uschi & Herz, Holger & Kosfeld, Michael & Oswald, Yvonne, 2021. "Do preferences and biases predict life outcomes? Evidence from education and labor market entry decisions," European Economic Review, Elsevier, vol. 134(C).
    39. Oliver Dukes & Vahe Avagyan & Stijn Vansteelandt, 2020. "Doubly robust tests of exposure effects under high‐dimensional confounding," Biometrics, The International Biometric Society, vol. 76(4), pages 1190-1200, December.
    40. Harold D. Chiang, 2018. "Many Average Partial Effects: with An Application to Text Regression," Papers 1812.09397, arXiv.org, revised Jan 2022.
    41. Xiduo Chen & Xingdong Feng & Antonio F. Galvao & Yeheng Ge, 2025. "Treatment Effects Inference with High-Dimensional Instruments and Control Variables," Papers 2503.20149, arXiv.org, revised Oct 2025.
    42. Jean-Marie Dufour & Purevdorj Tuvaandorj, 2025. "Mixed LR-$C(\alpha)$-type tests for irregular hypotheses, general criterion functions and misspecified models," Papers 2510.17070, arXiv.org.
    43. Semenova, Vira, 2025. "Generalized Lee bounds," Journal of Econometrics, Elsevier, vol. 251(C).
    44. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    45. Barbara Felderer & Jannis Kueck & Martin Spindler, 2021. "Big Data meets Causal Survey Research: Understanding Nonresponse in the Recruitment of a Mixed-mode Online Panel," Papers 2102.08994, arXiv.org.
    46. Menzel, Andreas & Woodruff, Christopher, 2021. "Gender wage gaps and worker mobility: Evidence from the garment sector in Bangladesh," Labour Economics, Elsevier, vol. 71(C).
    47. Kelly Van Lancker & Oliver Dukes & Stijn Vansteelandt, 2023. "Ensuring valid inference for Cox hazard ratios after variable selection," Biometrics, The International Biometric Society, vol. 79(4), pages 3096-3110, December.
    48. Ana Costa-Ramón & Ursina Schaede & Michaela Slotwinski & Anne Ardila Brenoe, 2024. "(Not) Thinking about the Future: Inattention and Maternal Labor Supply," CESifo Working Paper Series 11359, CESifo.
    49. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
    50. Drukker, David M. & Liu, Di, 2025. "A cluster plugin method for selecting the GLM lasso tuning parameters in models for unbalanced panel data," Econometrics and Statistics, Elsevier, vol. 34(C), pages 14-31.

  107. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression models," CeMMAP working papers 24/13, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers CWP61/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    3. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    5. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    6. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Victor Chernozhukov & Whitney Newey & Rahul Singh, 2018. "De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers," Papers 1802.08667, arXiv.org, revised Oct 2022.
    8. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers CWP28/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2017. "Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," Papers 1712.04802, arXiv.org, revised Oct 2023.
    10. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression models," CeMMAP working papers 24/13, Institute for Fiscal Studies.
    13. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers CWP22/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2013. "Honest confidence regions for a regression parameter in logistic regression with a large number of controls," CeMMAP working papers 67/13, Institute for Fiscal Studies.
    16. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," American Economic Review, American Economic Association, vol. 105(5), pages 486-490, May.
    17. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"," Papers 1305.6099, arXiv.org, revised Jun 2013.

  108. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  109. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers 59/13, Institute for Fiscal Studies.

    Cited by:

    1. Andrii Babii & Xi Chen & Eric Ghysels & Rohit Kumar, 2020. "Binary Choice under Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Algorithmic Fairness," Papers 2010.08463, arXiv.org, revised Nov 2025.
    2. Gozgor, Giray & Li, Jing & Saleem, Irfan & Shinwari, Riazullah, 2025. "The impact of women's political empowerment on renewable energy demand: Evidence from OECD countries," Energy Economics, Elsevier, vol. 141(C).
    3. Gonzalez, Felipe & Prem, Mounu & von Dessauer, Cristine, 2023. "Empowerment or Indoctrination? Women Centers Under Dictatorship," SocArXiv 64mf9, Center for Open Science.
    4. Emily Cuddy & Janet Currie, 2020. "Rules vs. Discretion: Treatment of Mental Illness in U.S. Adolescents," Working Papers 2020-10, Princeton University. Economics Department..
    5. Bertoni, Marco & Cavapozzi, Danilo & Pasini, Giacomo & Pavese, Caterina, 2025. "The causal effect of working from home on mental health of 50+ Europeans," The Journal of the Economics of Ageing, Elsevier, vol. 31(C).
    6. Asanov, Anastasiya-Mariya & Asanov, Igor & Buenstorf, Guido, 2024. "A low-cost digital first aid tool to reduce psychological distress in refugees: A multi-country randomized controlled trial of self-help online in the first months after the invasion of Ukraine," Social Science & Medicine, Elsevier, vol. 362(C).
    7. TELLO, Mario D., 2024. "Inversión Pública En Infraestructura Y Crecimiento Regional En Perú, 2005-2020: Un Análisis Basado En Técnicas De Aprendizaje Automático Causal," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 24(2), pages 195-222.
    8. Abhijit Banerjee & Esther Duflo & Amy Finkelstein & Lawrence F. Katz & Benjamin A. Olken & Anja Sautmann, 2020. "In Praise of Moderation: Suggestions for the Scope and Use of Pre-Analysis Plans for RCTs in Economics," NBER Working Papers 26993, National Bureau of Economic Research, Inc.
    9. Alhorr, Layane, 2024. "Virtual Windows Through Glass Walls? Digitalization for Low-Mobility Female Entrepreneurs," Policy Research Working Paper Series 10803, The World Bank.
    10. Giorgia Barboni & Nicol√°s de Roux & Santiago Perez-Cardona, 2024. "Perceived Social Acceptance and Migrants' Financial Inclusion," Documentos CEDE 21278, Universidad de los Andes, Facultad de Economía, CEDE.
    11. Pouliquen, Victor, 2026. "This business is mine! Intra-household effects of property rights in micro-enterprises," Journal of Development Economics, Elsevier, vol. 178(C).
    12. Anil Kumar, 2014. "Do restrictions on home equity extraction contribute to lower mortgage defaults? evidence from a policy discontinuity at the Texas border," Working Papers 1410, Federal Reserve Bank of Dallas.
    13. Uckat, Hannah Irmela, 2023. "Leaning in at Home : Women's Promotions and Intra-household Bargaining in Bangladesh," Policy Research Working Paper Series 10370, The World Bank.
    14. Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
    15. Ingrid Gould Ellen & Keren Mertens Horn & Davin Reed, 2017. "Has Falling Crime Invited Gentrification?," Working Papers 17-27, Center for Economic Studies, U.S. Census Bureau.
    16. Jonathan Hersh & Matthew Harding, 2018. "Big Data in economics," IZA World of Labor, LISER, pages 451-451, September.
    17. Ariadna Jou & Tommy Morgan, 2025. "Do Relief Programs Compensate For Longevity Losses From Reccesions? Evidence From The Great Depression And The New Deal," Working Papers wp562, University of Chile, Department of Economics.
    18. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2019. "Synthetic Difference In Differences," NBER Working Papers 25532, National Bureau of Economic Research, Inc.
    19. Panagopoulos, Orestis P. & Pappu, Vijay & Xanthopoulos, Petros & Pardalos, Panos M., 2016. "Constrained subspace classifier for high dimensional datasets," Omega, Elsevier, vol. 59(PA), pages 40-46.
    20. Elena Esposito, 2018. "Side Effects of Immunity: The Rise of African Slavery in the US South," Cahiers de Recherches Economiques du Département d'économie 18.07, Université de Lausanne, Faculté des HEC, Département d’économie.
    21. Li, Yuexin & Ma, X. & Renneboog, Luc, 2021. "In Art We Trust," Other publications TiSEM b9bb6522-9f8d-4c51-b039-3, Tilburg University, School of Economics and Management.
    22. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    23. Prem, Mounu & Vargas, Juan F. & Mejía, Daniel, 2020. "The Rise and Persistence of Illegal Crops: Evidence from a Naive Policy Announcement," SocArXiv 6gmpd, Center for Open Science.
    24. Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
    25. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    26. Gerald A. Carlino & Albert Saiz, 2019. "Beautiful city: Leisure amenities and urban growth," Journal of Regional Science, Wiley Blackwell, vol. 59(3), pages 369-408, June.
    27. Carneiro, Pedro & Lee, Sokbae & Wilhelm, Daniel, 2016. "Optimal Data Collection for Randomized Control Trials," IZA Discussion Papers 9908, IZA Network @ LISER.
    28. Pierre Koning & Paul Muller & Roger Prudon, 2022. "Why Do Temporary Workers Have Higher Disability Insurance Risks Than Permanent Workers?," Tinbergen Institute Discussion Papers 22-024/V, Tinbergen Institute.
    29. Alison Andrew & Sonya Krutikova & Gabriela Smarrelli & Hemlata Verma, 2022. "Gender norms, violence and adolescent girls' trajectories: evidence from a field experiment in India," Economics Series Working Papers 984, University of Oxford, Department of Economics.
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    Cited by:

    1. Park, Seyoung & Lee, Eun Ryung, 2021. "Hypothesis testing of varying coefficients for regional quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Man, Rebeka & Tan, Kean Ming & Wang, Zian & Zhou, Wen-Xin, 2024. "Retire: Robust expectile regression in high dimensions," Journal of Econometrics, Elsevier, vol. 239(2).
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    4. Chen, Le-Yu & Lee, Sokbae, 2023. "Sparse quantile regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 2195-2217.
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    6. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
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    Cited by:

    1. Victor Chernozhukov & Iván Fernández‐Val & Whitney Newey & Sami Stouli & Francis Vella, 2020. "Semiparametric estimation of structural functions in nonseparable triangular models," Quantitative Economics, Econometric Society, vol. 11(2), pages 503-533, May.
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    3. Fusejima, Koki, 2024. "Identification of multi-valued treatment effects with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
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    5. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    6. Clarke, Damian & Llorca-Jaña, Manuel & Pailañir, Daniel, 2021. "The Use of Quantile Methods in Economic History," IZA Discussion Papers 14659, IZA Network @ LISER.
    7. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    8. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    9. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    10. Bakouan, Pousseni & Sawadogo, Relwendé, 2024. "BioTrade and income inequality: Does frontier technology readiness matter?," Structural Change and Economic Dynamics, Elsevier, vol. 70(C), pages 650-665.
    11. Dilshad Ahmad & Muhammad Afzal, 2022. "Impact of violent conflicts and environmental hazards on pastoral sustainable development in Punjab, Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 11260-11281, September.
    12. Joana Passinhas & Isabel Proença, 2025. "A DSTI limit in an increasing interest rate environment: benefits across the LSTI distribution," Working Papers w02524, Banco de Portugal, Economics and Research Department.
    13. Simone Balestra & Uschi Backes-Gellner, 2013. "Heterogeneous Returns to Education Over Wage Distribution: Who Profits the Most?," Economics of Education Working Paper Series 0091, University of Zurich, Department of Business Administration (IBW), revised Dec 2013.
    14. Pengzhou Wu & Kenji Fukumizu, 2021. "$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap," Papers 2110.05225, arXiv.org.
    15. Christina Christou & Ruthira Naraidoo & Rangan Gupta & Won Joong Kim, 2017. "Monetary Policy Reaction Functions of the TICKs: A Quantile Regression Approach," Working Papers 201738, University of Pretoria, Department of Economics.
    16. Michael Chletsos & Stelios Roupakias, 2020. "Education and wage inequality before and during the fiscal crisis: A quantile regression analysis for Greece 2006–2016," Review of Development Economics, Wiley Blackwell, vol. 24(4), pages 1333-1364, November.
    17. Valizadeh, Pourya & Smith, Travis A., "undated". "How Did the American Recovery and Reinvestment Act (ARRA) Impact the Material Well-being of SNAP Participants? A Distributional Approach," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258496, Agricultural and Applied Economics Association.
    18. Nahid Farnaz, 2023. "Does Financial Development Relieve or Exacerbate Income Inequality? A Quantile Regression Approach," Economics Discussion Paper Series 2311, Economics, The University of Manchester.
    19. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    20. Paul. B. Kenfac Dongmezo & P. N. Mwita & I. R. Kamga Tchwaket, 2018. "Distributive and Quantile Treatment Effects: Imputation Based Estimators Approach," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 7(2), pages 1-3.
    21. Kaspar W thrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
    22. Ghosh, Pallab Kumar, 2014. "The contribution of human capital variables to changes in the wage distribution function," Labour Economics, Elsevier, vol. 28(C), pages 58-69.
    23. Saifullahi Sani Ibrahim & Huseyin Ozdeser & Behiye Cavusoglu, 2020. "Testing the impact of environmental hazards and violent conflicts on sustainable pastoral development: micro-level evidence from Nigeria," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4169-4190, June.
    24. Apergis, Nicholas & Christou, Christina, 2015. "The behaviour of the bank lending channel when interest rates approach the zero lower bound: Evidence from quantile regressions," Economic Modelling, Elsevier, vol. 49(C), pages 296-307.
    25. Mark Stater & Jeffrey B Wenger, 2017. "The Immediate Hardship of Unemployment: Evidence from the US Unemployment Insurance System," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(1), pages 17-36, January.
    26. Pourya Valizadeh & Travis A Smith, 2020. "How Did The American Recovery and Reinvestment Act Affect the Material Well‐Being of SNAP Participants? A Distributional Approach," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(3), pages 455-476, September.
    27. Hiroaki Kaido & Kaspar Wüthrich, 2018. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP72/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    28. Hammoudeh, Shawkat & Nguyen, Duc Khuong & Sousa, Ricardo M., 2014. "Energy prices and CO2 emission allowance prices: A quantile regression approach," Energy Policy, Elsevier, vol. 70(C), pages 201-206.
    29. Bai Huang & Tae-Hwy Lee & Aman Ullah, 2017. "A combined estimator of regression models with measurement errors," Indian Economic Review, Springer, vol. 52(1), pages 73-91, December.
    30. Bola Amoke Awotide & Adebayo Ogunniyi & Kehinde Oluseyi Olagunju & Lateef Olalekan Bello & Amadou Youssouf Coulibaly & Alexander Nimo Wiredu & Bourémo Kone & Aly Ahamadou & Victor Manyong & Tahirou Ab, 2022. "Evaluating the Heterogeneous Impacts of Adoption of Climate-Smart Agricultural Technologies on Rural Households’ Welfare in Mali," Agriculture, MDPI, vol. 12(11), pages 1-16, November.
    31. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    32. David Powell, 2020. "Quantile Treatment Effects in the Presence of Covariates," The Review of Economics and Statistics, MIT Press, vol. 102(5), pages 994-1005, December.
    33. Myoung‐jae Lee & Jin‐young Choi, 2022. "Finding mover–stayer quantile difference due to unobservables using quantile selection corrections," Bulletin of Economic Research, Wiley Blackwell, vol. 74(3), pages 704-721, July.
    34. Saifullahi Sani Ibrahim & Haruna Mohammad Aliero, 2020. "Testing the impact of financial inclusion on income convergence: Empirical evidence from Nigeria," African Development Review, African Development Bank, vol. 32(1), pages 42-54, March.
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    Cited by:

    1. Botosaru, Irene & Muris, Chris & Pendakur, Krishna, 2023. "Identification of time-varying transformation models with fixed effects, with an application to unobserved heterogeneity in resource shares," Journal of Econometrics, Elsevier, vol. 232(2), pages 576-597.
    2. Ghanem, Dalia, 2017. "Testing identifying assumptions in nonseparable panel data models," Journal of Econometrics, Elsevier, vol. 197(2), pages 202-217.
    3. Callaway, Brantly & Li, Tong & Oka, Tatsushi, 2018. "Quantile treatment effects in difference in differences models under dependence restrictions and with only two time periods," Journal of Econometrics, Elsevier, vol. 206(2), pages 395-413.
    4. Graham, Bryan S. & Hahn, Jinyong & Poirier, Alexandre & Powell, James L., 2018. "A quantile correlated random coefficients panel data model," Journal of Econometrics, Elsevier, vol. 206(2), pages 305-335.
    5. Matthew Harding & Carlos Lamarche, 2018. "A Panel Quantile Approach to Attrition Bias in Big Data: Evidence from a Randomized Experiment," Papers 1808.03364, arXiv.org.
    6. Antonio F. Galvao & Jiaying Gu & Stanislav Volgushev, 2018. "On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects," Papers 1807.11863, arXiv.org, revised Feb 2020.
    7. Chernozhukov, Victor & Fernández-Val, Iván & Weidner, Martin, 2024. "Network and panel quantile effects via distribution regression," Journal of Econometrics, Elsevier, vol. 240(2).
    8. Stefan Hoderlein & Hajo Holzmann & Maximilian Kasy & Alexander Meister, 2015. "Erratum regarding “Instrumental variables with unrestricted heterogeneity and continuous treatment”," Boston College Working Papers in Economics 896, Boston College Department of Economics, revised 01 Feb 2016.
    9. Cizek, Pavel & Sadikoglu, Serhan, 2022. "Nonseparable Panel Models with Index Structure and Correlated Random Effects," Discussion Paper 2022-009, Tilburg University, Center for Economic Research.
    10. Bryan S. Graham & Jinyong Hahn & Alexandre Poirier & James L. Powell, 2015. "Quantile Regression with Panel Data," NBER Working Papers 21034, National Bureau of Economic Research, Inc.
    11. Oliver Linton & Ji-Liang Shiu, 2018. "Semiparametric nonlinear panel data models with measurement error," CeMMAP working papers CWP09/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Irene Botosaru & Chris Muris, 2017. "Binarization for panel models with fixed effects," CeMMAP working papers CWP31/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Manuel Arellano & Stéphane Bonhomme, 2015. "Nonlinear panel data estimation via quantile regressions," CeMMAP working papers CWP40/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Demian Pouzo & Zacharias Psaradakis & Martin Sola, 2016. "Maximum Likelihood Estimation in Possibly Misspeci ed Dynamic Models with Time-Inhomogeneous Markov Regimes," Department of Economics Working Papers 2016_04, Universidad Torcuato Di Tella.
    15. Jiaying Gu & Stanislav Volgushev, 2018. "Panel Data Quantile Regression with Grouped Fixed Effects," Papers 1801.05041, arXiv.org, revised Aug 2018.
    16. Čížek, Pavel & Lei, Jinghua, 2018. "Identification and estimation of nonseparable single-index models in panel data with correlated random effects," Journal of Econometrics, Elsevier, vol. 203(1), pages 113-128.
    17. Liang Chen, 2019. "Nonparametric Quantile Regressions for Panel Data Models with Large T," Papers 1911.01824, arXiv.org, revised Sep 2020.
    18. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
    19. Ishihara, Takuya, 2020. "Identification and estimation of time-varying nonseparable panel data models without stayers," Journal of Econometrics, Elsevier, vol. 215(1), pages 184-208.
    20. D’Haultfœuille, Xavier & Hoderlein, Stefan & Sasaki, Yuya, 2023. "Nonparametric difference-in-differences in repeated cross-sections with continuous treatments," Journal of Econometrics, Elsevier, vol. 234(2), pages 664-690.
    21. Takuya Ishihara, 2020. "Panel Data Quantile Regression for Treatment Effect Models," Papers 2001.04324, arXiv.org, revised Nov 2021.
    22. Demian Pouzo & Zacharias Psaradakis & Martin Sola, 2016. "Maximum Likelihood Estimation in Markov Regime-Switching Models with Covariate-Dependent Transition Probabilities," Papers 1612.04932, arXiv.org, revised Dec 2021.
    23. Chernozhukov, Victor & Fernández-Val, Iván & Newey, Whitney K., 2019. "Nonseparable multinomial choice models in cross-section and panel data," Journal of Econometrics, Elsevier, vol. 211(1), pages 104-116.
    24. Yuya Sasaki & Takuya Ura, 2021. "Slow Movers in Panel Data," Papers 2110.12041, arXiv.org.
    25. Laurent Davezies & Xavier D’Haultfoeuille & Louise Laage, 2025. "Identification and Estimation of Average Causal Effects in Fixed Effects Logit Models," Working Papers 2025-02, Center for Research in Economics and Statistics.
    26. Victor Chernozhukov & Ivan Fernandez-Val & Ye Luo, 2015. "The sorted effects method: discovering heterogeneous effects beyond their averages," CeMMAP working papers CWP74/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    27. Irene Botosaru & Chris Muris & Krishna Pendakur, 2020. "Intertemporal Collective Household Models: Identification in Short Panels with Unobserved Heterogeneity in Resource Shares," Department of Economics Working Papers 2020-09, McMaster University.
    28. Gu, Jiaying & Volgushev, Stanislav, 2019. "Panel data quantile regression with grouped fixed effects," Journal of Econometrics, Elsevier, vol. 213(1), pages 68-91.
    29. Irene Botosaru & Chris Muris, 2022. "Identification of time-varying counterfactual parameters in nonlinear panel models," Papers 2212.09193, arXiv.org, revised Nov 2023.

  113. Alexandre Belloni & Victor Chernozhukov & Ivan Fern'andez-Val & Christian Hansen, 2013. "Program Evaluation and Causal Inference with High-Dimensional Data," Papers 1311.2645, arXiv.org, revised Jan 2018.

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    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    3. Undral Byambadalai & Tomu Hirata & Tatsushi Oka & Shota Yasui, 2025. "On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization," Papers 2506.05945, arXiv.org.
    4. Yamin Ahmad & Adam Check & Ming Chien Lo, 2024. "Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2139-2173, June.
    5. Daisuke MIYAKAWA, 2019. "Shocks to Supply Chain Networks and Firm Dynamics: An Application of Double Machine Learning," Discussion papers 19100, Research Institute of Economy, Trade and Industry (RIETI).
    6. Daniel Brunstein & Georges Casamatta & Sauveur Giannoni, 2025. "Using machine learning to estimate the heterogeneous impact of Airbnb on house prices: Evidence from Corsica," Post-Print hal-04934630, HAL.
    7. Mazzocchi, Mario & Capacci, Sara & Biondi, Beatrice, . "Causal inference on the impact of nutrition policies using observational data," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 11(01).
    8. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, IZA Network @ LISER.
    10. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, "undated". "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    11. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    12. Ying-Ying Lee & Chu-An Liu, 2024. "Lee Bounds with a Continuous Treatment in Sample Selection," Papers 2411.04312, arXiv.org, revised Oct 2025.
    13. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    14. Brunstein, Daniel & Casamatta, Georges & Giannoni, Sauveur, 2025. "Using machine learning to estimate the heterogeneous impact of Airbnb on house prices: Evidence from Corsica," Journal of Housing Economics, Elsevier, vol. 67(C).
    15. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    16. Jason Poulos & Shuxi Zeng, 2021. "RNN‐based counterfactual prediction, with an application to homestead policy and public schooling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1124-1139, August.
    17. Galbraith, John W. & Zinde-Walsh, Victoria, 2020. "Simple and reliable estimators of coefficients of interest in a model with high-dimensional confounding effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 609-632.
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    19. Wang, Lulu & Lyu, Jie & Wang, Shanshan & Zhang, Junyan, 2025. "Unveiling the influence of agricultural mechanization on greenhouse gas emission intensity: Insights from China using causal machine learning model," Agricultural Systems, Elsevier, vol. 226(C).
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    22. Zhihao Zhao & Xinyu Zhang & Guohua Zou & Alan T. K. Wan & Geoffrey K. F. Tso, 2024. "Model averaging for estimating treatment effects," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(1), pages 73-92, February.
    23. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    24. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    25. Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
    26. Vira Semenova, 2023. "Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters," Papers 2303.00982, arXiv.org, revised May 2025.
    27. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    28. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    29. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    30. Chen, Juan & Ma, Feng & Qiu, Xuemei & Li, Tao, 2023. "The role of categorical EPU indices in predicting stock-market returns," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 365-378.
    31. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2024. "Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 239(2).
    32. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    33. Michael C. Knaus, 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," Papers 1805.10300, arXiv.org, revised Jan 2019.
    34. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, IZA Network @ LISER.
    35. Yuehao Bai & Jizhou Liu & Azeem M. Shaikh & Max Tabord-Meehan, 2023. "On the Efficiency of Finely Stratified Experiments," Papers 2307.15181, arXiv.org, revised Nov 2025.
    36. Rodney V. Fonseca & Aluísio Pinheiro, 2020. "Wavelet estimation of the dimensionality of curve time series," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(5), pages 1175-1204, October.
    37. Victor Quintas-Martinez & Mohammad Taha Bahadori & Eduardo Santiago & Jeff Mu & Dominik Janzing & David Heckerman, 2024. "Multiply-Robust Causal Change Attribution," Papers 2404.08839, arXiv.org, revised Sep 2024.
    38. Francesca Micocci & Armando Rungi, 2021. "Predicting Exporters with Machine Learning," Papers 2107.02512, arXiv.org, revised Sep 2022.
    39. Entorf, Horst & Hou, Jia, 2018. "Financial education for the disadvantaged? A review," SAFE Working Paper Series 205, Leibniz Institute for Financial Research SAFE.
    40. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    41. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    42. Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2020. "Adversarial Estimation of Riesz Representers," Papers 2101.00009, arXiv.org, revised Apr 2024.
    43. Zemin Zheng & Jie Zhang & Yang Li, 2022. "L 0 -Regularized Learning for High-Dimensional Additive Hazards Regression," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2762-2775, September.
    44. Khashayar Khosravi & Greg Lewis & Vasilis Syrgkanis, 2019. "Non-Parametric Inference Adaptive to Intrinsic Dimension," Papers 1901.03719, arXiv.org, revised Jun 2019.
    45. Sven Klaassen & Jan Rabenseifner & Jannis Kueck & Philipp Bach, 2025. "Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators," Papers 2503.17290, arXiv.org, revised May 2025.
    46. Sven Klaassen & Jannis Kueck & Martin Spindler, 2017. "Transformation Models in High-Dimensions," Papers 1712.07364, arXiv.org.
    47. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    48. Denis Fougère & Nicolas Jacquemet, 2019. "Causal Inference and Impact Evaluation," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 510-511-5, pages 181-200.
    49. Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024. "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers 2407.16037, arXiv.org.
    50. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2016. "Locally Robust Semiparametric Estimation," Papers 1608.00033, arXiv.org, revised Aug 2020.
    51. Zhengyuan Zhou & Susan Athey & Stefan Wager, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Papers 1810.04778, arXiv.org, revised Nov 2018.
    52. Kensuke Sakamoto & Yuya Shimizu, 2025. "Design-Based and Network Sampling-Based Uncertainties in Network Experiments," Papers 2506.22989, arXiv.org, revised Sep 2025.
    53. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.
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    47. Chen, Ya & Tsionas, Mike G. & Zelenyuk, Valentin, 2021. "LASSO+DEA for small and big wide data," Omega, Elsevier, vol. 102(C).
    48. Hagerty, Nick & Zucker, Ariel, 2025. "Price Incentives for Conservation: Experimental Evidence from Groundwater Irrigation," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO 361141, Agricultural and Applied Economics Association.
    49. Simone Maxand & Hend Sallam, 2024. "Local Fiscal Effects of Immigration in Germany," CESifo Working Paper Series 11162, CESifo.
    50. He, Yi & Jaidee, Sombut & Gao, Jiti, 2023. "Most powerful test against a sequence of high dimensional local alternatives," Journal of Econometrics, Elsevier, vol. 234(1), pages 151-177.
    51. McKenzie, David & Sansone, Dario, 2017. "Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria," CEPR Discussion Papers 12523, C.E.P.R. Discussion Papers.
    52. D’Amour, Alexander & Ding, Peng & Feller, Avi & Lei, Lihua & Sekhon, Jasjeet, 2021. "Overlap in observational studies with high-dimensional covariates," Journal of Econometrics, Elsevier, vol. 221(2), pages 644-654.
    53. Do Nascimento Miguel, Jérémy, 2024. "Returns to quality in rural agricultural markets: Evidence from wheat markets in Ethiopia," Journal of Development Economics, Elsevier, vol. 171(C).
    54. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
    55. Sylvain Barde & Rowan Cherodian & Guy Tchuente, 2023. "Moran's I Lasso for models with spatially correlated data," Papers 2310.02773, arXiv.org.
    56. Ahsan Jansson, Cecilia & Patil, Vikram & Vecci, Joe & Chellattan Veettil , Prakashan & Yashodha, Yashodha, 2023. "Locus of Control and Economic Decision-Making: A Field Experiment in Odisha, India," Working Papers in Economics 833, University of Gothenburg, Department of Economics.
    57. Joseph Antonelli & Matthew Cefalu & Nathan Palmer & Denis Agniel, 2018. "Doubly robust matching estimators for high dimensional confounding adjustment," Biometrics, The International Biometric Society, vol. 74(4), pages 1171-1179, December.
    58. Bouguen, Adrien & Dillon, Andrew, 2026. "Beyond poverty reduction: Evidence from a multifaceted program on poverty, nutrition and child development," Journal of Development Economics, Elsevier, vol. 178(C).
    59. Michael Danquah & Solomon Owusu, 2021. "Digital technology and productivity of informal enterprises: Empirical evidence from Nigeria," WIDER Working Paper Series wp-2021-114, World Institute for Development Economic Research (UNU-WIDER).

  115. Victor Chernozhukov & Emre Kocatulum & Konrad Menzel, 2012. "Inference on Sets in Finance," Papers 1211.4282, arXiv.org.

    Cited by:

    1. Hiroaki Kaido, 2014. "Asymptotically efficient estimation of weighted average derivatives with an interval censored variable," CeMMAP working papers 03/14, Institute for Fiscal Studies.
    2. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    3. Otsu, Taisuke & Matsushita, Yukitoshi & Xu, Ke-Li, 2014. "Empirical likelihood for regression discontinuity design," LSE Research Online Documents on Economics 58065, London School of Economics and Political Science, LSE Library.
    4. Bontemps, Christian & Kumar, Rohit, 2018. "A Geometric Approach to Inference in Set-Identified Entry Games," TSE Working Papers 18-943, Toulouse School of Economics (TSE), revised Mar 2019.
    5. Molinari, Francesca, 2020. "Microeconometrics with partial identification," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 355-486, Elsevier.
    6. Yuan Liao & Anna Simoni, 2019. "Bayesian inference for partially identified smooth convex models," Post-Print hal-03089881, HAL.
    7. Gospodinov, Nikolay & Kan, Raymond & Robotti, Cesare, 2016. "On the properties of the constrained Hansen–Jagannathan distance," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 121-150.
    8. Karun Adusumilli & Taisuke Otsu, 2014. "Empirical Likelihood for Random Sets," STICERD - Econometrics Paper Series 574, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    9. Hiroaki Kaido & Jiaxuan Li & Marc Rysman, 2018. "Moment Inequalities in the Context of Simulated and Predicted Variables," Papers 1804.03674, arXiv.org.
    10. Chi Zhang & Xiangdan Piao & Shunsuke Managi, 2023. "Work Hour Mismatch on Life Evaluation: Full Heterogeneity and Individual- and Country-Level Characteristics of the Most and Least Affected Workers," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 170(2), pages 637-674, November.
    11. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Yuan Liao & Anna Simoni, 2016. "Bayesian Inference for Partially Identified Convex Models: Is it Valid for Frequentist Inference?," Departmental Working Papers 201607, Rutgers University, Department of Economics.

  116. Arun Chandrasekhar & Victor Chernozhukov & Francesca Molinari & Paul Schrimpf, 2012. "Inference for best linear approximations to set identified functions," CeMMAP working papers 43/12, Institute for Fiscal Studies.

    Cited by:

    1. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    2. Vira Semenova, 2023. "Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters," Papers 2303.00982, arXiv.org, revised May 2025.
    3. Vira Semenova, 2017. "Debiased Machine Learning of Set-Identified Linear Models," Papers 1712.10024, arXiv.org, revised Dec 2022.
    4. Otsu, Taisuke & Matsushita, Yukitoshi & Xu, Ke-Li, 2014. "Empirical likelihood for regression discontinuity design," LSE Research Online Documents on Economics 58065, London School of Economics and Political Science, LSE Library.
    5. Ben Sand & Chris Bidner, 2016. "Job Prospects and Pay Gaps: Theory and Evidence on the Gender Gap from U.S. Cities," Discussion Papers dp16-14, Department of Economics, Simon Fraser University.
    6. Hiroaki Kaido, 2014. "Asymptotically efficient estimation of weighted average derivatives with an interval censored variable," CeMMAP working papers CWP03/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Yuan Liao & Anna Simoni, 2019. "Bayesian inference for partially identified smooth convex models," Post-Print hal-03089881, HAL.
    8. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers 55/13, Institute for Fiscal Studies.
    9. Lee, Y-Y. & Bhattacharya, D., 2018. "Applied Welfare Analysis for Discrete Choice with Interval-data on Income," Cambridge Working Papers in Economics 1882, Faculty of Economics, University of Cambridge.
    10. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised May 2025.
    11. Schennach, Susanne M., 2020. "Mismeasured and unobserved variables," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 487-565, Elsevier.
    12. Karun Adusumilli & Taisuke Otsu, 2014. "Empirical Likelihood for Random Sets," STICERD - Econometrics Paper Series 574, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    13. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    14. Christian Bontemps & Thierry Magnac, 2017. "Set identification, moment restrictions, and inference," Post-Print hal-01575813, HAL.
    15. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org, revised Nov 2024.
    16. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    18. Semenova, Vira, 2025. "Generalized Lee bounds," Journal of Econometrics, Elsevier, vol. 251(C).
    19. Hoshino, Tadao, 2013. "Partial identification in binary response models with nonignorable nonresponses," Economics Letters, Elsevier, vol. 121(1), pages 74-78.
    20. Maasoumi, Esfandiar & Wang, Le, 2017. "What can we learn about the racial gap in the presence of sample selection?," Journal of Econometrics, Elsevier, vol. 199(2), pages 117-130.
    21. Yuan Liao & Anna Simoni, 2016. "Bayesian Inference for Partially Identified Convex Models: Is it Valid for Frequentist Inference?," Departmental Working Papers 201607, Rutgers University, Department of Economics.

  117. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results," Papers 1212.0442, arXiv.org, revised Jun 2015.

    Cited by:

    1. Dong, Chaohua & Linton, Oliver, 2018. "Additive nonparametric models with time variable and both stationary and nonstationary regressors," Journal of Econometrics, Elsevier, vol. 207(1), pages 212-236.
    2. Mona Azadkia & Leihao Chen & Fang Han, 2025. "Bias correction for Chatterjee's graph-based correlation coefficient," Papers 2508.09040, arXiv.org, revised Jan 2026.
    3. Christoph Breunig, 2018. "Varying Random Coefficient Models," Papers 1804.03110, arXiv.org, revised Aug 2020.
    4. Michael Jansson & Demian Pouzo, 2019. "Towards a general large sample theory for regularized estimators," CeMMAP working papers CWP63/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Zheng Fang & Juwon Seo, 2021. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Econometrica, Econometric Society, vol. 89(5), pages 2439-2458, September.
    6. Drew Fudenberg & Whitney K. Newey & Philipp Strack & Tomasz Strzalecki, 2019. "Testing the Drift-Diffusion Model," Papers 1908.05824, arXiv.org.
    7. Cai, Zongwu & Juhl, Ted, 2023. "The distribution of rolling regression estimators," Journal of Econometrics, Elsevier, vol. 235(2), pages 1447-1463.
    8. Adam Lee & Emil A. Stoltenberg & Per A. Mykland, 2025. "Semiparametrics via parametrics and contiguity," Papers 2501.09483, arXiv.org, revised Sep 2025.
    9. Li, Jia & Liao, Zhipeng & Zhou, Wenyu, 2025. "A general test for functional inequalities," Journal of Econometrics, Elsevier, vol. 251(C).
    10. Tadao Hoshino, 2024. "Functional Spatial Autoregressive Models," Papers 2402.14763, arXiv.org, revised Oct 2024.
    11. Ignacio Martinez & Jaume Vives-i-Bastida, 2022. "Bayesian and Frequentist Inference for Synthetic Controls," Papers 2206.01779, arXiv.org, revised Jul 2024.
    12. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    13. Byunghoon Kang, 2017. "Inference in Nonparametric Series Estimation with Data-Dependent Undersmoothing," Working Papers 170712442, Lancaster University Management School, Economics Department.
    14. Tao, Jing, 2020. "Trinity tests of functions for conditional moment models," Journal of Multivariate Analysis, Elsevier, vol. 178(C).
    15. Man, Rebeka & Tan, Kean Ming & Wang, Zian & Zhou, Wen-Xin, 2024. "Retire: Robust expectile regression in high dimensions," Journal of Econometrics, Elsevier, vol. 239(2).
    16. Marina Bassi & Lelys Dinarte-Diaz & Maria Marta Ferreyra & Sergio Urzua, 2023. "What Makes a Program Good? Evidence from Short-Cycle Higher Education Programs in Five Developing Countries," CESifo Working Paper Series 10255, CESifo.
    17. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP04/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Sendhil Mullainathan & Ashesh Rambachan, 2024. "From Predictive Algorithms to Automatic Generation of Anomalies," Papers 2404.10111, arXiv.org, revised Sep 2025.
    19. Breunig, Christoph & Hoderlein, Stefan, 2018. "Specification Testing in Random Coefficient Models," Rationality and Competition Discussion Paper Series 77, CRC TRR 190 Rationality and Competition.
    20. Nan Liu & Yanbo Liu & Yuya Sasaki & Yuanyuan Wan, 2025. "Nonparametric Uniform Inference in Binary Classification and Policy Values," Working Papers tecipa-811, University of Toronto, Department of Economics.
    21. Dong, Chaohua & Chen, Rong & Xiao, Zhijie & Liu, Weiyi, 2024. "Functional quantile autoregression," Journal of Econometrics, Elsevier, vol. 244(2).
    22. Lin, Yingqian & Tu, Yundong, 2020. "Sieve extremum estimation of a semiparametric transformation model," Economics Letters, Elsevier, vol. 189(C).
    23. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    24. Hidehiko Ichimura & Whitney K. Newey, 2017. "The influence function of semiparametric estimators," CeMMAP working papers 06/17, Institute for Fiscal Studies.
    25. Jiang, Liang & Li, Liyao & Miao, Ke & Zhang, Yichong, 2025. "Adjustments with many regressors under covariate-adaptive randomizations," Journal of Econometrics, Elsevier, vol. 249(PB).
    26. Byunghoon Kang, 2018. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Working Papers 240829404, Lancaster University Management School, Economics Department.
    27. Li, Jia & Liao, Zhipeng, 2020. "Uniform nonparametric inference for time series," Journal of Econometrics, Elsevier, vol. 219(1), pages 38-51.
    28. Matias D. Cattaneo & Richard K. Crump & Max H. Farrell & Yingjie Feng, 2019. "On Binscatter," Papers 1902.09608, arXiv.org, revised Apr 2024.
    29. Andrii Babii, 2016. "Honest Confidence Sets in Nonparametric IV Regression and Other Ill-Posed Models," Papers 1611.03015, arXiv.org, revised Dec 2020.
    30. Holland, Ashley D., 2017. "Penalized spline estimation in the partially linear model," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 211-235.
    31. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers 59/15, Institute for Fiscal Studies.
    32. Zhao, Weihua & Lian, Heng, 2016. "Local asymptotics for nonparametric quantile regression with regression splines," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 209-215.
    33. Lin, Yingqian & Tu, Yundong, 2024. "Functional coefficient cointegration models with Box–Cox transformation," Economics Letters, Elsevier, vol. 234(C).
    34. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2018. "Nonparametric estimation in case of endogenous selection," Journal of Econometrics, Elsevier, vol. 202(2), pages 268-285.
    35. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
    36. Buchinsky, Moshe & Li, Fanghua & Liao, Zhipeng, 2022. "Estimation and inference of semiparametric models using data from several sources," Journal of Econometrics, Elsevier, vol. 226(1), pages 80-103.
    37. Chaohua Dong & Jiti Gao & Bin Peng, 2018. "Series estimation for single-index models under constraints," Monash Econometrics and Business Statistics Working Papers 5/18, Monash University, Department of Econometrics and Business Statistics.
    38. S. Centorrino & J. S. Racine, 2016. "Semiparametric Varying Coefficient Models with Endogenous Covariates," Department of Economics Working Papers 2016-02, McMaster University.
    39. Parente, Paulo M.D.C. & Smith, Richard J., 2017. "Tests of additional conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 200(1), pages 1-16.
    40. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    41. Toru Kitagawa & Sokbae Lee & Chen Qiu, 2025. "Leave No One Undermined: Policy Targeting with Regret Aversion," Papers 2506.16430, arXiv.org.
    42. Ahnaf Rafi, 2023. "Efficient Semiparametric Estimation of Average Treatment Effects Under Covariate Adaptive Randomization," Papers 2305.08340, arXiv.org.
    43. Tadao Hoshino & Takahide Yanagi, 2018. "Treatment Effect Models with Strategic Interaction in Treatment Decisions," Papers 1810.08350, arXiv.org, revised Feb 2023.
    44. Breunig, Christoph & Haan, Peter, 2021. "Nonparametric regression with selectively missing covariates," Journal of Econometrics, Elsevier, vol. 223(1), pages 28-52.
    45. Shi, Chengchun & Luo, Shikai & Zhu, Hongtu & Song, Rui, 2021. "An online sequential test for qualitative treatment effects," LSE Research Online Documents on Economics 112521, London School of Economics and Political Science, LSE Library.
    46. Joachim Freyberger & Andreas Neuhierl & Michael Weber & Michael Weber, 2018. "Dissecting Characteristics Nonparametrically," CESifo Working Paper Series 7187, CESifo.
    47. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers 47/16, Institute for Fiscal Studies.
    48. Matias D. Cattaneo & Yingjie Feng & Boris Shigida, 2024. "Uniform Estimation and Inference for Nonparametric Partitioning-Based M-Estimators," Papers 2409.05715, arXiv.org, revised Aug 2025.
    49. Zhou, Weilun & Gao, Jiti & Harris, David & Kew, Hsein, 2024. "Semi-parametric single-index predictive regression models with cointegrated regressors," Journal of Econometrics, Elsevier, vol. 238(1).
    50. Matias Cattaneo & Michael Jansson & Whitney K. Newey, 2015. "Alternative asymptotics and the partially linear model with many regressors," CeMMAP working papers 36/15, Institute for Fiscal Studies.
    51. Matias D. Cattaneo & Richard K. Crump & Max H. Farrell & Yingjie Feng, 2024. "Nonlinear Binscatter Methods," Papers 2407.15276, arXiv.org.
    52. Yukitoshi Matsushita & Taisuke Otsu & Keisuke Takahata, 2022. "Estimating density ratio of marginals to joint: Applications to causal inference," STICERD - Econometrics Paper Series 619, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    53. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    54. Max H. Farrell, 2013. "Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations," Papers 1309.4686, arXiv.org, revised Feb 2018.
    55. Breunig, Christoph, 2017. "Testing missing at random using instrumental variables," SFB 649 Discussion Papers 2017-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    56. Nan Liu & Yanbo Liu & Peter C.B. Phillips & Yajie Zhang, 2025. "Robust Inference for Time Varying Predictability: A Sieve-IVX Approach," Cowles Foundation Discussion Papers 2431, Cowles Foundation for Research in Economics, Yale University.
    57. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
    58. Hu, Pingbo & Peng, Xiuyuan & Hu, Xinglin, 2024. "Some new asymptotic results for series estimation under clustered dependence," Statistics & Probability Letters, Elsevier, vol. 213(C).
    59. Matias D. Cattaneo & Ricardo P. Masini & William G. Underwood, 2022. "Yurinskii's Coupling for Martingales," Papers 2210.00362, arXiv.org, revised Aug 2025.
    60. Breunig, Christoph, 2021. "Varying random coefficient models," Journal of Econometrics, Elsevier, vol. 221(2), pages 381-408.
    61. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 10/22, Monash University, Department of Econometrics and Business Statistics.
    62. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers CWP41/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    63. Freyberger, Joachim & Rai, Yoshiyasu, 2018. "Uniform confidence bands: Characterization and optimality," Journal of Econometrics, Elsevier, vol. 204(1), pages 119-130.
    64. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    65. Lee, Sungwon, 2023. "Efficient estimation of a triangular system of equations for quantile regression," Economics Letters, Elsevier, vol. 226(C).
    66. Qihui Chen & Nikolai Roussanov & Xiaoliang Wang, 2021. "Semiparametric Conditional Factor Models in Asset Pricing," Papers 2112.07121, arXiv.org, revised Apr 2025.
    67. Xiaohong Chen & Wayne Yuan Gao & Likang Wen, 2025. "ReLU-Based and DNN-Based Generalized Maximum Score Estimators," Cowles Foundation Discussion Papers 2476, Cowles Foundation for Research in Economics, Yale University.
    68. Cattaneo, Matias D & Jansson, Michael & Ma, Xinwei, 2019. "Two-Step Estimation and Inference with Possibly Many Included Covariates," University of California at San Diego, Economics Working Paper Series qt86c7x315, Department of Economics, UC San Diego.
    69. Matias Cattaneo & Michael Jansson & Whitney K. Newey, 2017. "Inference in linear regression models with many covariates and heteroskedasticity," CeMMAP working papers CWP03/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    70. Dinarte Diaz,Lelys Ileana & Ferreyra,Maria Marta & Urzua,Sergio & Bassi,Marina, 2021. "What Makes a Program Good ? Evidence from Short-Cycle Higher Education Programs in LatinAmerica and the Caribbean," Policy Research Working Paper Series 9722, The World Bank.
    71. Li, Mengbing & Shi, Chengchun & Wu, Zhenke & Fryzlewicz, Piotr, 2025. "Testing stationarity and change point detection in reinforcement learning," LSE Research Online Documents on Economics 127507, London School of Economics and Political Science, LSE Library.
    72. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    73. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    74. Tadao Hoshino, 2021. "Estimating a Continuous Treatment Model with Spillovers: A Control Function Approach," Papers 2112.15114, arXiv.org, revised Jan 2023.
    75. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers CWP47/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    76. Breunig, Christoph, 2017. "Testing Missing At Random Using Instrumental Variables," Rationality and Competition Discussion Paper Series 59, CRC TRR 190 Rationality and Competition.
    77. Qiu, Chen & Otsu, Taisuke, 2022. "Information theoretic approach to high dimensional multiplicative models: stochastic discount factor and treatment effect," LSE Research Online Documents on Economics 110494, London School of Economics and Political Science, LSE Library.
    78. Cattaneo, Matias D. & Jansson, Michael & Ma, Xinwei, 2024. "Local regression distribution estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    79. Cheng, Tingting & Dong, Chaohua & Gao, Jiti & Linton, Oliver, 2024. "GMM estimation for high-dimensional panel data models," Journal of Econometrics, Elsevier, vol. 244(1).
    80. Kenichi Nagasawa, 2018. "Treatment Effect Estimation with Noisy Conditioning Variables," Papers 1811.00667, arXiv.org, revised Sep 2022.
    81. Samuele Centorrino & Aman Ullah & Jing Xue, 2019. "Semiparametric Estimation of Correlated Random Coefficient Models without Instrumental Variables," Papers 1911.06857, arXiv.org.
    82. Christoph Breunig & Stefan Hoderlein, 2016. "Nonparametric Specification Testing in Random Parameter Models," Boston College Working Papers in Economics 897, Boston College Department of Economics.
    83. Zongwu Cai & Ted Juhl, 2020. "The Distribution Of Rolling Regression Estimators," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202218, University of Kansas, Department of Economics, revised Dec 2022.
    84. Yanqin Fan & Yuan Qi & Gaoqian Xu, 2025. "Policy Learning with $\alpha$-Expected Welfare," Papers 2505.00256, arXiv.org.
    85. Damian Kozbur, 2013. "Inference in additively separable models with a high-dimensional set of conditioning variables," ECON - Working Papers 284, Department of Economics - University of Zurich, revised Apr 2018.
    86. Tomohiro Ando & Tadao Hoshino, 2025. "Functional Network Autoregressive Models for Panel Data," Papers 2502.13431, arXiv.org, revised Feb 2026.
    87. Sun, Yiguo & Malikov, Emir, 2017. "Estimation and Inference in Functional-Coefficient Spatial Autoregressive Panel Data Models with Fixed Effects," MPRA Paper 83671, University Library of Munich, Germany.
    88. Xiaohong Chen & Wayne Yuan Gao & Likang Wen, 2025. "ReLU-Based and DNN-Based Generalized Maximum Score Estimators," Papers 2511.19121, arXiv.org.
    89. Karun Adusumilli & Taisuke Otsu & Chen Qiu, 2020. "Reweighted nonparametric likelihood inference for linear functionals," STICERD - Econometrics Paper Series 614, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    90. Byunghoon Kang, 2019. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Papers 1909.12162, arXiv.org, revised Feb 2020.
    91. Sid Kankanala, 2023. "On Gaussian Process Priors in Conditional Moment Restriction Models," Papers 2311.00662, arXiv.org, revised Nov 2023.
    92. Liu, Lin & Mukherjee, Rajarshi & Robins, James M., 2024. "Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    93. Adusumilli, Karun & Otsu, Taisuke & Qiu, Chen, 2023. "Reweighted nonparametric likelihood inference for linear functionals," LSE Research Online Documents on Economics 120198, London School of Economics and Political Science, LSE Library.
    94. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    95. Abdul-Nasah Soale & Emmanuel Selorm Tsyawo, 2023. "Clustered Covariate Regression," Papers 2302.09255, arXiv.org, revised Jul 2025.
    96. Yukun Ma & Pedro H. C. Sant'Anna & Yuya Sasaki & Takuya Ura, 2023. "Doubly Robust Estimators with Weak Overlap," Papers 2304.08974, arXiv.org, revised Apr 2023.
    97. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    98. Abhinandan Dalal & Eric J. Tchetgen Tchetgen, 2025. "Partial Identification of Causal Effects for Endogenous Continuous Treatments," Papers 2508.13946, arXiv.org.
    99. Adam Lee, 2024. "Locally Regular and Efficient Tests in Non-Regular Semiparametric Models," Papers 2403.05999, arXiv.org, revised Dec 2024.
    100. Plassier, Vincent & Portier, François & Segers, Johan, 2020. "Risk bounds when learning infinitely many response functions by ordinary linear regression," LIDAM Discussion Papers ISBA 2020019, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    101. Christoph Breunig & Peter Haan, 2018. "Nonparametric Regression with Selectively Missing Covariates," Papers 1810.00411, arXiv.org, revised Oct 2020.
    102. Cui, Wenhao & Hu, Jie & Wang, Jiandong, 2024. "Nonparametric estimation for high-frequency data incorporating trading information," Journal of Econometrics, Elsevier, vol. 240(1).
    103. Adam Baybutt & Manu Navjeevan, 2023. "Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls," Papers 2301.06283, arXiv.org.
    104. Chen Qiu & Taisuke Otsu, 2022. "Information theoretic approach to high‐dimensional multiplicative models: Stochastic discount factor and treatment effect," Quantitative Economics, Econometric Society, vol. 13(1), pages 63-94, January.
    105. Peter Horvath & Jia Li & Zhipeng Liao & Andrew J. Patton, 2022. "A consistent specification test for dynamic quantile models," Quantitative Economics, Econometric Society, vol. 13(1), pages 125-151, January.
    106. Shi, Chengchun & Lu, Wenbin & Song, Rui, 2019. "A sparse random projection-based test for overall qualitative treatment effects," LSE Research Online Documents on Economics 102107, London School of Economics and Political Science, LSE Library.
    107. Feng, Guohua & Peng, Bin & Su, Liangjun & Yang, Thomas Tao, 2019. "Semi-parametric single-index panel data models with interactive fixed effects: Theory and practice," Journal of Econometrics, Elsevier, vol. 212(2), pages 607-622.
    108. Lin Liu & Chang Li, 2023. "New $\sqrt{n}$-consistent, numerically stable higher-order influence function estimators," Papers 2302.08097, arXiv.org.
    109. Denis Chetverikov, 2024. "Tuning parameter selection in econometrics," Papers 2405.03021, arXiv.org.
    110. Matias D. Cattaneo & Max H. Farrell & Yingjie Feng, 2018. "Large Sample Properties of Partitioning-Based Series Estimators," Papers 1804.04916, arXiv.org, revised Jun 2019.
    111. Harold D. Chiang & Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs," Papers 2102.06586, arXiv.org.
    112. Scott Kostyshak, 2025. "Flatness-Robust Critical Bandwidth," Papers 2504.03594, arXiv.org.
    113. Dong, Chaohua & Linton, Oliver & Peng, Bin, 2021. "A weighted sieve estimator for nonparametric time series models with nonstationary variables," Journal of Econometrics, Elsevier, vol. 222(2), pages 909-932.
    114. Hongjun Ha & Daniel Bauer, 2022. "A least-squares Monte Carlo approach to the estimation of enterprise risk," Finance and Stochastics, Springer, vol. 26(3), pages 417-459, July.
    115. Matias D. Cattaneo & Fang Han & Zhexiao Lin, 2023. "On Rosenbaum's Rank-based Matching Estimator," Papers 2312.07683, arXiv.org, revised Jan 2024.
    116. Raffaello Seri & Samuele Centorrino & Michele Bernasconi, 2019. "Nonparametric Estimation and Inference in Economic and Psychological Experiments," Papers 1904.11156, arXiv.org, revised Dec 2019.
    117. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers 41/17, Institute for Fiscal Studies.
    118. Giovanni Ballarin, 2023. "Impulse Response Analysis of Structural Nonlinear Time Series Models," Papers 2305.19089, arXiv.org, revised Jun 2025.
    119. Gupta, Abhimanyu, 2018. "Nonparametric specification testing via the trinity of tests," Journal of Econometrics, Elsevier, vol. 203(1), pages 169-185.

  118. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors," Papers 1212.6906, arXiv.org, revised Jan 2018.

    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    3. Thilo Reinschlussel & Martin C. Arnold, 2024. "Information-Enriched Selection of Stationary and Non-Stationary Autoregressions using the Adaptive Lasso," Papers 2402.16580, arXiv.org, revised Jul 2024.
    4. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
    5. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2014. "Central limit theorems and bootstrap in high dimensions," CeMMAP working papers 49/14, Institute for Fiscal Studies.
    6. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, "undated". "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    7. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," Journal of Econometrics, Elsevier, vol. 196(2), pages 259-274.
    8. Magne Mogstad & Joseph P. Romano & Daniel Wilhelm & Azeem M. Shaikh, 2020. "Inference for ranks with applications to mobility across neighborhoods and academic achievement across countries," CeMMAP working papers CWP10/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Xinrui Ruan & Xinwei Ma & Yingfei Wang & Waverly Wei & Jingshen Wang, 2025. "Can language models boost the power of randomized experiments without statistical bias?," Papers 2510.05545, arXiv.org, revised Dec 2025.
    10. Li, Jia & Liao, Zhipeng & Zhou, Wenyu, 2025. "A general test for functional inequalities," Journal of Econometrics, Elsevier, vol. 251(C).
    11. Peng Chen & Ivan Nourdin & Lihu Xu & Xiaochuan Yang & Rui Zhang, 2022. "Non-integrable Stable Approximation by Stein’s Method," Journal of Theoretical Probability, Springer, vol. 35(2), pages 1137-1186, June.
    12. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    13. Xingcai Zhou & Zhaoyang Jing & Chao Huang, 2024. "Distributed Bootstrap Simultaneous Inference for High-Dimensional Quantile Regression," Mathematics, MDPI, vol. 12(5), pages 1-53, February.
    14. Eric Gautier & Christiern Rose, 2022. "Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments," Papers 2211.02249, arXiv.org, revised Nov 2022.
    15. Zhu, Ying, 2018. "Concentration Based Inference in High Dimensional Generalized Regression Models (I: Statistical Guarantees)," MPRA Paper 88502, University Library of Munich, Germany.
    16. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    17. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. Jianqing Fan & Ricardo Masini & Marcelo C. Medeiros, 2021. "Bridging factor and sparse models," Papers 2102.11341, arXiv.org, revised Sep 2022.
    19. Denis Chetverikov & Jesper R.-V. Sørensen, 2021. "Analytic and Bootstrap-after-Cross-Validation Methods for Selecting Penalty Parameters of High-Dimensional M-Estimators," Discussion Papers 21-04, University of Copenhagen. Department of Economics.
    20. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    21. Javier Hidalgo & Marcia M Schafgans, 2015. "Inference and Testing Breaks in Large Dynamic Panels with Strong Cross Sectional Dependence," STICERD - Econometrics Paper Series /2015/583, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    22. Mauricio Olivares & Tomasz Olma & Daniel Wilhelm, 2025. "A Powerful Bootstrap Test of Independence in High Dimensions," Papers 2503.21715, arXiv.org, revised Feb 2026.
    23. Shi, Chengchun & Lu, Wenbin & Song, Rui, 2018. "A massive data framework for M-estimators with cubic-rate," LSE Research Online Documents on Economics 102111, London School of Economics and Political Science, LSE Library.
    24. Dongwoo Kim & Daniel Wilhelm, 2017. "Powerful t-Tests in the presence of nonclassical measurement error," CeMMAP working papers CWP57/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    25. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    26. Qi Zheng & Yunwei Cui & Rongning Wu, 2024. "On estimation of nonparametric regression models with autoregressive and moving average errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(2), pages 235-262, April.
    27. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019. "lassopack: Model selection and prediction with regularized regression in Stata," Papers 1901.05397, arXiv.org.
    28. Kutta, Tim & Jach, Agnieszka & Haddad, Michel Ferreira Cardia & Kokoszka, Piotr & Wang, Haonan, 2025. "Detection and localization of changes in a panel of densities," Journal of Multivariate Analysis, Elsevier, vol. 205(C).
    29. Denis Kojevnikov, 2021. "The Bootstrap for Network Dependent Processes," Papers 2101.12312, arXiv.org.
    30. Sokbae (Simon) Lee & Ryo Okui & Yoon-Jae Whang, 2016. "Doubly robust uniform confidence band for the conditional average treatment effect function," CeMMAP working papers 03/16, Institute for Fiscal Studies.
    31. Kock, Anders Bredahl & Preinerstorfer, David, 2024. "A remark on moment-dependent phase transitions in high-dimensional Gaussian approximations," Statistics & Probability Letters, Elsevier, vol. 211(C).
    32. Manu Navjeevan, 2023. "An Identification and Dimensionality Robust Test for Instrumental Variables Models," Papers 2311.14892, arXiv.org, revised Dec 2024.
    33. Yukun Ma, 2023. "Identification-robust inference for the LATE with high-dimensional covariates," Papers 2302.09756, arXiv.org, revised Nov 2025.
    34. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP46/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    35. Byunghoon Kang, 2018. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Working Papers 240829404, Lancaster University Management School, Economics Department.
    36. Pasha Andreyanov & Grigory Franguridi, 2021. "Nonparametric inference on counterfactuals in first-price auctions," Papers 2106.13856, arXiv.org, revised Oct 2024.
    37. Li, Jia & Liao, Zhipeng, 2020. "Uniform nonparametric inference for time series," Journal of Econometrics, Elsevier, vol. 219(1), pages 38-51.
    38. Mayer, Alexander, 2020. "(Consistently) testing strict exogeneity against the alternative of predeterminedness in linear time-series models," Economics Letters, Elsevier, vol. 193(C).
    39. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    40. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    41. Jinyuan Chang & Wen Zhou & Wen-Xin Zhou & Lan Wang, 2017. "Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering," Biometrics, The International Biometric Society, vol. 73(1), pages 31-41, March.
    42. Marie-Christine Düker & Seok-Oh Jeong & Taewook Lee & Changryong Baek, 2024. "Detection of multiple change-points in high-dimensional panel data with cross-sectional and temporal dependence," Statistical Papers, Springer, vol. 65(4), pages 2327-2359, June.
    43. Andrei Iakovlev & Annie Liang, 2024. "The Value of Context: Human versus Black Box Evaluators," Papers 2402.11157, arXiv.org, revised Jun 2024.
    44. Shengchun Kong & Zhuqing Yu & Xianyang Zhang & Guang Cheng, 2021. "High‐dimensional robust inference for Cox regression models using desparsified Lasso," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(3), pages 1068-1095, September.
    45. Stefan Seifert & Marica Valente, 2018. "An Offer that you Can't Refuse? Agrimafias and Migrant Labor on Vineyards in Southern Italy," Discussion Papers of DIW Berlin 1735, DIW Berlin, German Institute for Economic Research.
    46. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Uniform post selection inference for LAD regression and other Z-estimation problems," CeMMAP working papers 51/14, Institute for Fiscal Studies.
    47. Victor Chernozhukov & Whitney Newey & Rahul Singh, 2018. "De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers," Papers 1802.08667, arXiv.org, revised Oct 2022.
    48. Xu, Kai & Cheng, Qing, 2024. "Test of conditional independence in factor models via Hilbert–Schmidt independence criterion," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    49. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Jul 2025.
    50. Matias D. Cattaneo & Yingjie Feng & Rocio Titiunik, 2019. "Prediction Intervals for Synthetic Control Methods," Papers 1912.07120, arXiv.org, revised Sep 2021.
    51. Gonzalo, Jesús & Olmo, José, 2016. "Long-term optimal portfolio allocation under dynamic horizon-specific risk aversion," UC3M Working papers. Economics 23599, Universidad Carlos III de Madrid. Departamento de Economía.
    52. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings," CeMMAP working papers CWP38/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    53. Kutta, Tim & Dette, Holger, 2024. "Validating approximate slope homogeneity in large panels," Journal of Econometrics, Elsevier, vol. 246(1).
    54. Philip Dörr & Johannes Heiny, 2025. "Joint Extremes of Inversions and Descents of Random Permutations," Journal of Theoretical Probability, Springer, vol. 38(2), pages 1-37, June.
    55. Anders Bredahl Kock & David Preinerstorfer, 2024. "Enhanced power enhancements for testing many moment equalities: Beyond the $2$- and $\infty$-norm," Papers 2407.17888, arXiv.org, revised Oct 2024.
    56. He, Yong & Zhang, Mingjuan & Zhang, Xinsheng & Zhou, Wang, 2020. "High-dimensional two-sample mean vectors test and support recovery with factor adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    57. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2019. "Improved Central Limit Theorem and bootstrap approximations in high dimensions," Papers 1912.10529, arXiv.org, revised May 2022.
    58. Kai Xu & Nan An, 2024. "A tuning-free efficient test for marginal linear effects in high-dimensional quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(1), pages 93-110, February.
    59. Shi, Chengchun & Li, Lexin, 2022. "Testing mediation effects using logic of Boolean matrices," LSE Research Online Documents on Economics 108881, London School of Economics and Political Science, LSE Library.
    60. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
    61. Katarzyna Reluga & María‐José Lombardía & Stefan Sperlich, 2023. "Simultaneous inference for linear mixed model parameters with an application to small area estimation," International Statistical Review, International Statistical Institute, vol. 91(2), pages 193-217, August.
    62. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers 47/16, Institute for Fiscal Studies.
    63. Helmut Wasserbacher & Martin Spindler, 2024. "Credit Ratings: Heterogeneous Effect on Capital Structure," Papers 2406.18936, arXiv.org.
    64. Baris Ata & Alexandre Belloni & Ozan Candogan, 2018. "Latent Agents in Networks: Estimation and Targeting," Papers 1808.04878, arXiv.org, revised Jan 2022.
    65. Fan, Yanqin & Han, Fang & Park, Hyeonseok, 2023. "Estimation and inference in a high-dimensional semiparametric Gaussian copula vector autoregressive model," Journal of Econometrics, Elsevier, vol. 237(1).
    66. Brice Ozenne & Esben Budtz-Jørgensen & Sebastian Elgaard Ebert, 2023. "Controlling the familywise error rate when performing multiple comparisons in a linear latent variable model," Computational Statistics, Springer, vol. 38(1), pages 1-23, March.
    67. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," Journal of Econometrics, Elsevier, vol. 206(1), pages 57-82.
    68. Jinyuan Chang & Chao Zheng & Wen‐Xin Zhou & Wen Zhou, 2017. "Simulation‐based hypothesis testing of high dimensional means under covariance heterogeneity," Biometrics, The International Biometric Society, vol. 73(4), pages 1300-1310, December.
    69. Armstrong, Timothy B. & Chan, Hock Peng, 2016. "Multiscale adaptive inference on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 194(1), pages 24-43.
    70. Matias D. Cattaneo & Ricardo P. Masini & William G. Underwood, 2022. "Yurinskii's Coupling for Martingales," Papers 2210.00362, arXiv.org, revised Aug 2025.
    71. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," LSE Research Online Documents on Economics 87513, London School of Economics and Political Science, LSE Library.
    72. Wang, Steven Shuye & Xu, Kuan & Zhang, Hao, 2019. "A microstructure study of circuit breakers in the Chinese stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    73. Jad Beyhum & Jonas Striaukas, 2023. "Testing for sparse idiosyncratic components in factor-augmented regression models," Papers 2307.13364, arXiv.org, revised Jul 2024.
    74. Ruben Dezeure & Peter Bühlmann & Cun-Hui Zhang, 2017. "High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 685-719, December.
    75. Timmermann, Allan & Qu, Ritong & Zhu, Yinchu, 2019. "Do Any Economists Have Superior Forecasting Skills?," CEPR Discussion Papers 14112, C.E.P.R. Discussion Papers.
    76. Matthias Löffler & Richard Nickl, 2017. "Comments on: High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 731-733, December.
    77. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    78. Jinyuan Chang & Qing Jiang & Xiaofeng Shao, 2022. "Testing the martingale difference hypothesis in high dimension," Papers 2209.04770, arXiv.org, revised Sep 2022.
    79. Li, Shuangbo & Zhang, Li-Xin, 2019. "Identifying the number of factors using a white noise test," Statistics & Probability Letters, Elsevier, vol. 152(C), pages 92-99.
    80. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2022. "High-dimensional Data Bootstrap," Papers 2205.09691, arXiv.org.
    81. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "hdm: High-Dimensional Metrics," Papers 1608.00354, arXiv.org.
    82. Hill, Jonathan B., 2025. "Mixingale and physical dependence equality with applications," Statistics & Probability Letters, Elsevier, vol. 221(C).
    83. Li, Qiang & An, Lian & Zhang, Ren, 2023. "Corruption drives brain drain: Cross-country evidence from machine learning," Economic Modelling, Elsevier, vol. 126(C).
    84. Robert Adamek & Stephan Smeekes & Ines Wilms, 2023. "Sparse High-Dimensional Vector Autoregressive Bootstrap," Papers 2302.01233, arXiv.org, revised May 2025.
    85. Cheng, Guanghui & Liu, Zhi & Peng, Liuhua, 2022. "Gaussian approximations for high-dimensional non-degenerate U-statistics via exchangeable pairs," Statistics & Probability Letters, Elsevier, vol. 182(C).
    86. Philipp Bach & Sven Klaassen & Jannis Kueck & Martin Spindler, 2020. "Estimation and Uniform Inference in Sparse High-Dimensional Additive Models," Papers 2004.01623, arXiv.org, revised Apr 2024.
    87. Wenlong Ji & Lihua Lei & Asher Spector, 2023. "Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects," Papers 2310.08115, arXiv.org, revised Nov 2024.
    88. Matteo Barigozzi, 2022. "Principal Component Analysis for High-Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification," Papers 2211.01921, arXiv.org, revised Feb 2026.
    89. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2018. "Inference on causal and structural parameters using many moment inequalities," CeMMAP working papers CWP60/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    90. Yuta Koike, 2023. "High-Dimensional Central Limit Theorems for Homogeneous Sums," Journal of Theoretical Probability, Springer, vol. 36(1), pages 1-45, March.
    91. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    92. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers CWP47/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    93. Jana Janková & Rajen D. Shah & Peter Bühlmann & Richard J. Samworth, 2020. "Goodness‐of‐fit testing in high dimensional generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 773-795, July.
    94. Martin C. Arnold & Thilo Reinschlussel, 2024. "Bootstrap Adaptive Lasso Solution Path Unit Root Tests," Papers 2409.07859, arXiv.org.
    95. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    96. Naumov, A. & Spokoiny, V. & Ulyanovk, V., 2018. "Bootstrap Confidence Sets for Spectral Projectors of Sample Covariance," IRTG 1792 Discussion Papers 2018-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    97. Kojevnikov, Denis & Song, Kyungchul, 2022. "A Berry–Esseen bound for vector-valued martingales," Statistics & Probability Letters, Elsevier, vol. 186(C).
    98. Zhengbang Li & Fuxiang Liu & Luanjie Zeng & Guoxin Zuo, 2021. "A stationary bootstrap test about two mean vectors comparison with somewhat dense differences and fewer sample size than dimension," Computational Statistics, Springer, vol. 36(2), pages 941-960, June.
    99. Lixiu Wu & Jiang Hu, 2024. "Multi-sample hypothesis testing of high-dimensional mean vectors under covariance heterogeneity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(4), pages 579-615, August.
    100. Wu Wang & Xuming He & Zhongyi Zhu, 2020. "Statistical inference for multiple change‐point models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1149-1170, December.
    101. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2019. "Valid simultaneous inference in high-dimensional settings (with the HDM package for R)," CeMMAP working papers CWP30/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    102. Denis Chetverikov & Daniel Wilhelm & Dongwoo Kim, 2019. "An adaptive test of stochastic monotonicity," CeMMAP working papers CWP49/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    103. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    104. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul, 2017. "Confidence bands for coefficients in high dimensional linear models with error-in-variables," CeMMAP working papers CWP22/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    105. Ouyang, Yanyan & Liu, Jiamin & Tong, Tiejun & Xu, Wangli, 2022. "A rank-based high-dimensional test for equality of mean vectors," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    106. Joel L. Horowitz, 2018. "Bootstrap Methods in Econometrics," Papers 1809.04016, arXiv.org.
    107. Jin, Lei & Cai, Li & Wang, Suojin, 2025. "Testing the constancy of the variance for time series with a trend," Computational Statistics & Data Analysis, Elsevier, vol. 208(C).
    108. Bach, Philipp & Klaassen, Sven & Kueck, Jannis & Spindler, Martin, 2025. "Estimation and uniform inference in sparse high-dimensional additive models," Journal of Econometrics, Elsevier, vol. 249(PB).
    109. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," American Economic Review, American Economic Association, vol. 105(5), pages 486-490, May.
    110. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "High-Dimensional Metrics in R," Papers 1603.01700, arXiv.org, revised Aug 2016.
    111. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," LSE Research Online Documents on Economics 68839, London School of Economics and Political Science, LSE Library.
    112. Rahul Singh & Liyang Sun, 2019. "Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics," Papers 1909.05244, arXiv.org, revised Dec 2022.
    113. Alexander Giessing & Jianqing Fan, 2020. "Bootstrapping $\ell_p$-Statistics in High Dimensions," Papers 2006.13099, arXiv.org, revised Aug 2020.
    114. Harold D. Chiang, 2018. "Many Average Partial Effects: with An Application to Text Regression," Papers 1812.09397, arXiv.org, revised Jan 2022.
    115. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    116. Spokoiny, Vladimir & Zhilova, Mayya, 2014. "Bootstrap confidence sets under model misspecification," SFB 649 Discussion Papers 2014-067, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    117. Alexandre Belloni & Mathieu Rosenbaum & Alexandre B. Tsybakov, 2017. "Linear and conic programming estimators in high dimensional errors-in-variables models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 939-956, June.
    118. Demian Pouzo, 2014. "Bootstrap Consistency for Quadratic Forms of Sample Averages with Increasing Dimension," Papers 1411.2701, arXiv.org, revised Aug 2015.
    119. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    120. Keith Battocchi & Eleanor Dillon & Maggie Hei & Greg Lewis & Miruna Oprescu & Vasilis Syrgkanis, 2021. "Estimating the Long-Term Effects of Novel Treatments," Papers 2103.08390, arXiv.org, revised Feb 2022.
    121. Matias D. Cattaneo & Richard K. Crump & Weining Wang, 2023. "Beta-Sorted Portfolios," Staff Reports 1068, Federal Reserve Bank of New York.
    122. Yang, Weichao & Guo, Xu & Zhu, Lixing, 2024. "Tests for high-dimensional generalized linear models under general covariance structure," Computational Statistics & Data Analysis, Elsevier, vol. 199(C).
    123. Denis Chetverikov & Jesper Riis-Vestergaard S{o}rensen, 2021. "Selecting Penalty Parameters of High-Dimensional M-Estimators using Bootstrapping after Cross-Validation," Papers 2104.04716, arXiv.org, revised Nov 2024.
    124. Timothy B Armstrong & Michal Kolesár, 2018. "A Simple Adjustment for Bandwidth Snooping," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(2), pages 732-765.
    125. Luo, Shikai & Yang, Ying & Shi, Chengchun & Yao, Fang & Ye, Jieping & Zhu, Hongtu, 2024. "Policy evaluation for temporal and/or spatial dependent experiments," LSE Research Online Documents on Economics 122741, London School of Economics and Political Science, LSE Library.
    126. Mengjia Yu & Xiaohui Chen, 2021. "Finite sample change point inference and identification for high‐dimensional mean vectors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 247-270, April.
    127. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Comparison and anti-concentration bounds for maxima of Gaussian random vectors," CeMMAP working papers 40/16, Institute for Fiscal Studies.
    128. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers 46/17, Institute for Fiscal Studies.
    129. Yang, Lin & Feng, Zhenghui & Jiang, Qing, 2025. "Test for the mean of high-dimensional functional time series," Computational Statistics & Data Analysis, Elsevier, vol. 201(C).
    130. Victor Chernozhukov & Christian Hansen & Yuan Liao & Yinchu Zhu, 2021. "Inference for Low-Rank Models," Papers 2107.02602, arXiv.org, revised Jan 2023.
    131. Joel L. Horowitz, 2018. "Non-Asymptotic Inference in Instrumental Variables Estimation," Papers 1809.03600, arXiv.org.
    132. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    133. Horowitz, Joel L., 2021. "Bounding the difference between true and nominal rejection probabilities in tests of hypotheses about instrumental variables models," Journal of Econometrics, Elsevier, vol. 222(2), pages 1057-1082.
    134. Sven Klaassen & Jannis Kück & Martin Spindler & Victor Chernozhukov, 2019. "Uniform inference in high-dimensional Gaussian graphical models," CeMMAP working papers CWP29/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    135. Peter Horvath & Jia Li & Zhipeng Liao & Andrew J. Patton, 2022. "A consistent specification test for dynamic quantile models," Quantitative Economics, Econometric Society, vol. 13(1), pages 125-151, January.
    136. Denis Chetverikov, 2024. "Tuning parameter selection in econometrics," Papers 2405.03021, arXiv.org.
    137. Wei, Waverly & Zhou, Yuqing & Zheng, Zeyu & Wang, Jingshen, 2024. "Inference on the best policies with many covariates," Journal of Econometrics, Elsevier, vol. 239(2).
    138. Chang, Jinyuan & Hu, Qiao & Kolaczyk, Eric D. & Yao, Qiwei & Yi, Fengting, 2024. "Edge differentially private estimation in the β-model via jittering and method of moments," LSE Research Online Documents on Economics 122099, London School of Economics and Political Science, LSE Library.
    139. Kean Ming Tan & Junwei Lu & Tong Zhang & Han Liu, 2021. "Estimating and inferring the maximum degree of stimulus‐locked time‐varying brain connectivity networks," Biometrics, The International Biometric Society, vol. 77(2), pages 379-390, June.
    140. Ye Luo & Hai Wang, 2017. "Core Determining Class and Inequality Selection," American Economic Review, American Economic Association, vol. 107(5), pages 274-277, May.
    141. Thomas M. Russell, 2020. "Policy Transforms and Learning Optimal Policies," Papers 2012.11046, arXiv.org.
    142. Joel L. Horowitz, 2018. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP52/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    143. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2023. "Distribution regression with sample selection and UK wage decomposition," CeMMAP working papers 09/23, Institute for Fiscal Studies.
    144. Mingjuan Zhang & Libin Jin, 2024. "High-Dimensional U-Statistics Type Hypothesis Testing via Jackknife Pseudo-Values with Multiplier Bootstrap," Mathematics, MDPI, vol. 12(23), pages 1-20, December.
    145. Cyrus Samii & Ye Wang & Jonathan Sullivan & P. M. Aronow, 2023. "Inference in Spatial Experiments with Interference using the SpatialEffect Package," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 138-156, March.
    146. Chang, Jinyuan & Yao, Qiwei & Zhou, Wen, 2017. "Testing for high-dimensional white noise using maximum cross-correlations," LSE Research Online Documents on Economics 68531, London School of Economics and Political Science, LSE Library.
    147. Jin, Lei & Wang, Suojin, 2025. "A new bootstrap assisted test for checking second order stationarity," Econometrics and Statistics, Elsevier, vol. 35(C), pages 101-119.
    148. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2015. "IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade," NBER Working Papers 21033, National Bureau of Economic Research, Inc.
    149. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
    150. Zhilova, Mayya, 2015. "Simultaneous likelihood-based bootstrap confidence sets for a large number of models," SFB 649 Discussion Papers 2015-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    151. Joel L. Horowitz, 2018. "Bootstrap methods in econometrics," CeMMAP working papers CWP53/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    152. Baek, Changryong & Gates, Katheleen M. & Leinwand, Benjamin & Pipiras, Vladas, 2021. "Two sample tests for high-dimensional autocovariances," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).

  119. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Central limit theorems and multiplier bootstrap when p is much larger than n," CeMMAP working papers 45/12, Institute for Fiscal Studies.

    Cited by:

    1. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Comparison and anti-concentration bounds for maxima of Gaussian random vectors," CeMMAP working papers 71/13, Institute for Fiscal Studies.

  120. Alexandre Belloni & Victor Chernozhukov, 2011. "High Dimensional Sparse Econometric Models: An Introduction," Papers 1106.5242, arXiv.org, revised Sep 2011.

    Cited by:

    1. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    2. Bottmer, Lea & Croux, Christophe & Wilms, Ines, 2022. "Sparse regression for large data sets with outliers," European Journal of Operational Research, Elsevier, vol. 297(2), pages 782-794.
    3. Alquier, Pierre & Hebiri, Mohamed, 2011. "Generalization of ℓ1 constraints for high dimensional regression problems," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1760-1765.
    4. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    5. Mehmet Caner & Anders Bredahl Kock, 2014. "Asymptotically Honest Confidence Regions for High Dimensional Parameters by the Desparsified Conservative Lasso," CREATES Research Papers 2014-36, Department of Economics and Business Economics, Aarhus University.
    6. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019. "lassopack: Model selection and prediction with regularized regression in Stata," Papers 1901.05397, arXiv.org.
    7. Federico A. Bugni & Mehmet Caner & Anders Bredahl Kock & Soumendra Lahiri, 2016. "Inference in partially identified models with many moment inequalities using Lasso," CREATES Research Papers 2016-12, Department of Economics and Business Economics, Aarhus University.
    8. Mehmet Caner & Anders Bredahl Kock, 2013. "Oracle Inequalities for Convex Loss Functions with Non-Linear Targets," CREATES Research Papers 2013-51, Department of Economics and Business Economics, Aarhus University.
    9. André Nunes Maranhão, 2024. "Brazilian Business Cycle Analysis in a High-Dimensional and Time-Irregular Span Context," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(1), pages 1-58, August.
    10. Simon Freyaldenhoven, 2020. "Identification Through Sparsity in Factor Models," Working Papers 20-25, Federal Reserve Bank of Philadelphia.
    11. Malene Kallestrup-Lamb & Anders Bredahl Kock & Johannes Tang Kristensen, 2013. "Lassoing the Determinants of Retirement," CREATES Research Papers 2013-21, Department of Economics and Business Economics, Aarhus University.
    12. André Nunes Maranhão & Nicole Rennó Castro, 2023. "Dissecting Brazilian agriculture business cycles in high-dimensional and time-irregular span contexts," Empirical Economics, Springer, vol. 65(4), pages 1543-1578, October.
    13. Jorge Balat & Camila Casas, 2018. "Firm Productivity and Cities: The Case of Colombia," Borradores de Economia 1032, Banco de la Republica de Colombia.
    14. Laurent Callot & Johannes Tang Kristensen, 2014. "Vector Autoregressions with Parsimoniously Time Varying Parameters and an Application to Monetary Policy," CREATES Research Papers 2014-41, Department of Economics and Business Economics, Aarhus University.
    15. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers 47/16, Institute for Fiscal Studies.
    16. Ulrike Schneider, 2016. "Confidence Sets Based on Thresholding Estimators in High-Dimensional Gaussian Regression Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1412-1455, December.
    17. Xu, Ning & Hong, Jian & Fisher, Timothy, 2016. "Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso," MPRA Paper 71670, University Library of Munich, Germany.
    18. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2019. "High-Dimensional Granger Causality Tests with an Application to VIX and News," Papers 1912.06307, arXiv.org, revised Feb 2021.
    19. Yamada, Hiroshi & Yoon, Gawon, 2014. "When Grilli and Yang meet Prebisch and Singer: Piecewise linear trends in primary commodity prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 193-207.
    20. Achim Ahrens & Arnab Bhattacharjee, 2015. "Two-Step Lasso Estimation of the Spatial Weights Matrix," Econometrics, MDPI, vol. 3(1), pages 1-28, March.
    21. Newhouse,David Locke & Merfeld,Joshua David & Ramakrishnan,Anusha Pudugramam & Swartz,Tom & Lahiri,Partha, 2022. "Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning," Policy Research Working Paper Series 10175, The World Bank.
    22. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
    23. Denis Chetverikov & . ., 2016. "On cross-validated Lasso," CeMMAP working papers CWP47/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    24. Kock, Anders Bredahl, 2016. "Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models," Journal of Econometrics, Elsevier, vol. 195(1), pages 71-85.
    25. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    26. Michael Zimmert, 2018. "The Finite Sample Performance of Treatment Effects Estimators based on the Lasso," Papers 1805.05067, arXiv.org.
    27. Alberto Abadie & Maximilian Kasy, 2019. "Choosing Among Regularized Estimators in Empirical Economics: The Risk of Machine Learning," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 743-762, December.
    28. De La Maza, Cristóbal & Davis, Alex & Azevedo, Inês, 2021. "Welfare analysis of the ecological impacts of electricity production in Chile using the sparse multinomial logit model," Ecological Economics, Elsevier, vol. 184(C).
    29. Kalouptsidi, Myrto, 2017. "Detection and Impact of Industrial Subsidies: The Case of Chinese Shipbuilding," CEPR Discussion Papers 12080, C.E.P.R. Discussion Papers.
    30. Imran Rasul & Pedro Souza & Aureo de Paula, 2023. "Identifying Network Ties from Panel Data: Theory and an application to tax competition," POID Working Papers 081, Centre for Economic Performance, LSE.
    31. Damian Kozbur, 2013. "Inference in additively separable models with a high-dimensional set of conditioning variables," ECON - Working Papers 284, Department of Economics - University of Zurich, revised Apr 2018.
    32. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
    33. Riccardo Di Francesco, 2023. "Ordered Correlation Forest," Papers 2309.08755, arXiv.org.
    34. Roberto Casarin & Daniel Felix Ahelegbey & Monica Billio, 2014. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Working Papers 2014:29, Department of Economics, University of Venice "Ca' Foscari".
    35. Zhu, Ying, 2018. "Sparse linear models and l1-regularized 2SLS with high-dimensional endogenous regressors and instruments," Journal of Econometrics, Elsevier, vol. 202(2), pages 196-213.
    36. Myrto Kalouptsidi, 2014. "Detection and Impact of Industrial Subsidies: The Case of World Shipbuilding," NBER Working Papers 20119, National Bureau of Economic Research, Inc.
    37. Kock, Anders Bredahl & Callot, Laurent, 2015. "Oracle inequalities for high dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 186(2), pages 325-344.
    38. Michael P. Leung & Pantelis Loupos, 2022. "Graph Neural Networks for Causal Inference Under Network Confounding," Papers 2211.07823, arXiv.org, revised Dec 2025.
    39. Dai, Wei & Tsang, Ka Wai, 2023. "A resampling approach for confidence intervals in linear time-series models after model selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

  121. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls," Papers 1201.0224, arXiv.org, revised May 2012.

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    1. Manda, Constantine & Sango, Danford & Hoffmann, Vivian & de Brauw, Alan & Zakaria, Zakayo & Temba, George & Brown, Elizabeth & Richards, Dorothy & Rashid, Said, 2025. "Overcoming budget constraints to healthy diets: Evidence from urban Tanzania," IFPRI discussion papers 2372, International Food Policy Research Institute (IFPRI).
    2. Rao, Manaswini & Shenoy, Ashish, 2023. "Got (clean) milk? Organization, incentives, and management in Indian dairy cooperatives," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 708-722.
    3. Toru Kitagawa & Chris Muris, 2015. "Model averaging in semiparametric estimation of treatment effects," CeMMAP working papers CWP46/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
    5. Imbens, Guido W., 2014. "Matching Methods in Practice: Three Examples," IZA Discussion Papers 8049, IZA Network @ LISER.
    6. Shukla, Pallavi & Pullabhotla, Hemant K. & Baylis, Kathy, 2022. "Trouble with zero: The limits of subsidizing technology adoption," Journal of Development Economics, Elsevier, vol. 158(C).
    7. Mehmet Caner & Anders Bredahl Kock, 2014. "Asymptotically Honest Confidence Regions for High Dimensional Parameters by the Desparsified Conservative Lasso," CREATES Research Papers 2014-36, Department of Economics and Business Economics, Aarhus University.
    8. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, IZA Network @ LISER.
    9. Yash Raj Shrestha & Vivianna Fang He & Phanish Puranam & Georg von Krogh, 2021. "Algorithm Supported Induction for Building Theory: How Can We Use Prediction Models to Theorize?," Organization Science, INFORMS, vol. 32(3), pages 856-880, May.
    10. Alexander Krei{ss} & Christoph Rothe, 2021. "Inference in Regression Discontinuity Designs with High-Dimensional Covariates," Papers 2110.13725, arXiv.org, revised May 2022.
    11. Teck-Hua Ho & Noah Lim & Sadat Reza & Xiaoyu Xia, 2017. "OM Forum—Causal Inference Models in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 509-525, October.
    12. Wunsch, Conny & Strittmatter, Anthony, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," CEPR Discussion Papers 15840, C.E.P.R. Discussion Papers.
    13. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    14. Clarke, Damian, 2023. "The Economics of Abortion Policy," IZA Discussion Papers 16395, IZA Network @ LISER.
    15. Alex Armand & Britta Augsburg & Antonella Bancalari, 2021. "Coordination and the poor maintenance trap: an experiment on public infrastructure in India," IFS Working Papers W21/16, Institute for Fiscal Studies.
    16. John List & Ian Muir & Gregory Sun, 2022. "Using Machine Learning for Efficient Flexible Regression Adjustment in Economic Experiments," Natural Field Experiments 00763, The Field Experiments Website.
    17. Stefano Cabras & J. D. Tena, 2023. "Implicit institutional incentives and individual decisions: Causal inference with deep learning models," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(6), pages 3739-3754, September.
    18. Alexander Kreiss & Christoph Rothe, 2023. "Inference in regression discontinuity designs with high-dimensional covariates," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 105-123.
    19. Kaila, Heidi & Azad, Abul, 2023. "The effects of crime and violence on food insecurity and consumption in Nigeria," Food Policy, Elsevier, vol. 115(C).
    20. Haroon, Maryiam & Said, Farah & Zafar, Mahniya, 2022. "Fostering non-cognitive skills and academic performance: Experimental evidence from women’s-only colleges in Pakistan," Journal of Asian Economics, Elsevier, vol. 81(C).
    21. Hensel, Lukas & Tekleselassie, Tsegay & Witte, Marc J., 2021. "Formalized Employee Search and Labor Demand," IZA Discussion Papers 14839, IZA Network @ LISER.
    22. Miric, Milan & Boudreau, Kevin J. & Jeppesen, Lars Bo, 2019. "Protecting their digital assets: The use of formal & informal appropriability strategies by App developers," Research Policy, Elsevier, vol. 48(8), pages 1-1.
    23. Ji, Chengyuan & Jiang, Junyan & Zhang, Yujin, 2024. "Political trust and government performance in the time of COVID-19," World Development, Elsevier, vol. 176(C).
    24. Chauhan, Tarana, 2025. "Accounting for Empowerment? Examining Women's Financial Inclusion in India," GLO Discussion Paper Series 1689, Global Labor Organization (GLO).
    25. Toru Kitagawa & Chris Muris, 2013. "Covariate selection and model averaging in semiparametric estimation of treatment effects," CeMMAP working papers 61/13, Institute for Fiscal Studies.
    26. Zhu, Ying, 2013. "Sparse Linear Models and Two-Stage Estimation in High-Dimensional Settings with Possibly Many Endogenous Regressors," MPRA Paper 49846, University Library of Munich, Germany.
    27. Jin, Wei & van der Ploeg, Frederick & Zhang, Lin, 2024. "How clean capital slows down disinvestment of carbon-intensive capital in the low-carbon transition," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    28. Bernhard C. Dannemann & Erkan Goeren, 2025. "Where Does the Money Go? Spatial Patterns in the Distribution of World Bank Foreign Aid Projects," Working Papers V-451-25, University of Oldenburg, Department of Economics, revised Mar 2025.
    29. Helmut Wasserbacher & Martin Spindler, 2024. "Credit Ratings: Heterogeneous Effect on Capital Structure," Papers 2406.18936, arXiv.org.
    30. Kovacs, Roxanne J. & Lagarde, Mylene & Cairns, John, 2022. "Can patients improve the quality of care they receive? Experimental evidence from Senegal," World Development, Elsevier, vol. 150(C).
    31. Chakravorty, Bhaskar & Arulampalam, Wiji & Bhatiya, Apurav Yash & Imbert, Clement & Rathelot, Roland, 2021. "Can information about jobs improve the effectiveness of vocational training? Experimental evidence from India," The Warwick Economics Research Paper Series (TWERPS) 1361, University of Warwick, Department of Economics.
    32. Alex Armand & Frederica Mendonca & Wayne Aaron Sandholtz & Pedro C. Vicente, 2025. "On the political economy of urbanization: experimental evidence from Mozambique," Nova SBE Working Paper Series wp670, Universidade Nova de Lisboa, Nova School of Business and Economics.
    33. Loh, Wen Wei & Ren, Dongning, 2021. "Data-driven Covariate Selection for Confounding Adjustment by Focusing on the Stability of the Effect Estimator," OSF Preprints yve6u, Center for Open Science.
    34. Leight, Jessica & Hirvonen, Kalle & Karachiwalla, Naureen & Rakshit, Deboleena, 2025. "Displacement and development: Evidence from a graduation program for Somalia’s ultra-poor," IFPRI discussion papers 2356, International Food Policy Research Institute (IFPRI).
    35. Matthias Breuer & Harm H. Schütt, 2023. "Accounting for uncertainty: an application of Bayesian methods to accruals models," Review of Accounting Studies, Springer, vol. 28(2), pages 726-768, June.
    36. Alex Armand & Britta Augsburg & Antonella Bancalari & Maitreesh Ghatak, 2023. "Public service delivery, exclusion and externalities: theory and experimental evidence from India," NOVAFRICA Working Paper Series wp2303, Universidade Nova de Lisboa, Nova School of Business and Economics, NOVAFRICA.
    37. Byol Kim & Song Liu & Mladen Kolar, 2021. "Two‐sample inference for high‐dimensional Markov networks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 939-962, November.
    38. Gallegos, Sebastian, 2025. "Replication: "Physical Disability and Labor Market Discrimination: Evidence from a Video Résumé Field Experiment"," I4R Discussion Paper Series 256, The Institute for Replication (I4R).
    39. Ya Chen & Mike Tsionas & Valentin Zelenyuk, 2020. "LASSO DEA for small and big data," CEPA Working Papers Series WP092020, School of Economics, University of Queensland, Australia.
    40. Travis J. Lybbert & Ashish Shenoy & Tomoé Bourdier & Caitlin Kieran, 2024. "Striving to revive pulses in India with extension, input subsidies, and output price supports†," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(3), pages 1167-1192, May.
    41. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2016. "Inference in High-Dimensional Panel Models With an Application to Gun Control," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 590-605, October.
    42. Alex Armand & Britta Augsburg & Antonella Bancalari & Kalyan Kumar Kameshwara, 2023. "Social proximity and misinformation: Experimental evidence from a mobile phone-based campaign in India," IFS Working Papers W23/39, Institute for Fiscal Studies.
    43. Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
    44. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    45. Ian W. McKeague & Min Qian, 2015. "An Adaptive Resampling Test for Detecting the Presence of Significant Predictors," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1422-1433, December.
    46. Martin C. Arnold & Thilo Reinschlussel, 2024. "Bootstrap Adaptive Lasso Solution Path Unit Root Tests," Papers 2409.07859, arXiv.org.
    47. Renée, Laëtitia, 2022. "The long-term effects of financial aid and career education: Evidence from a randomized experiment," CLEF Working Paper Series 46, Canadian Labour Economics Forum (CLEF), University of Waterloo.
    48. Alex Armand & Frederica Mendonca & Wayne Aaron Sandholtz & Pedro C. Vicente, 2024. "On the political economy of urbanization: experimental evidence from Mozambique," Nova SBE Working Paper Series wp2403, Universidade Nova de Lisboa, Nova School of Business and Economics.
    49. Alexandre Belloni & Victor Chernozhukov, 2015. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1449-1451, December.
    50. Chakravorty, Bhaskar & Bhatiya, Apurav Yash & Imbert, Clément & Lohnert, Maximilian & Panda, Poonam & Rathelot, Roland, 2023. "Impact of the COVID-19 crisis on India’s rural youth: Evidence from a panel survey and an experiment," World Development, Elsevier, vol. 168(C).
    51. David Cheng & Abhishek Chakrabortty & Ashwin N. Ananthakrishnan & Tianxi Cai, 2020. "Estimating average treatment effects with a double‐index propensity score," Biometrics, The International Biometric Society, vol. 76(3), pages 767-777, September.
    52. Pollack, Adam B. & Kaufmann, Robert K., 2022. "Increasing storm risk, structural defense, and house prices in the Florida Keys," Ecological Economics, Elsevier, vol. 194(C).
    53. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    54. Linton, O. & Seo, M. & Whang, Y-J., 2020. "Testing Stochastic Dominance with Many Conditioning Variables," Cambridge Working Papers in Economics 2004, Faculty of Economics, University of Cambridge.
    55. Armand, Alex & Augsburg, Britta & Bancalari, Antonella & Kameshwara, Kalyan Kumar, 2024. "Religious proximity and misinformation: Experimental evidence from a mobile phone-based campaign in India," Journal of Health Economics, Elsevier, vol. 96(C).
    56. Bütikofer, Aline & Ginja, Rita & Landaud, Fanny & Løken, Katrine V., 2020. "School Selectivity, Peers, and Mental Health," Working Papers in Economics 5/20, University of Bergen, Department of Economics.
    57. Felipe González & Pablo Muñoz & Mounu Prem, 2019. "Lost in Transition? The Persistance of Dictatorship Mayors," Documentos de Trabajo 525, Instituto de Economia. Pontificia Universidad Católica de Chile..
    58. Joshua Angrist & Miikka Rokkanen, 2012. "Wanna Get Away? RD Identification Away from the Cutoff," NBER Working Papers 18662, National Bureau of Economic Research, Inc.
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    60. Ben Gillen & Erik Snowberg & Leeat Yariv, 2015. "Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study," NBER Working Papers 21517, National Bureau of Economic Research, Inc.
    61. Hagerty, Nick & Zucker, Ariel, 2025. "Price Incentives for Conservation: Experimental Evidence from Groundwater Irrigation," 2025 AAEA & WAEA Joint Annual Meeting, July 27-29, 2025, Denver, CO 361141, Agricultural and Applied Economics Association.
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    70. Ahsan Jansson, Cecilia & Patil, Vikram & Vecci, Joe & Chellattan Veettil , Prakashan & Yashodha, Yashodha, 2023. "Locus of Control and Economic Decision-Making: A Field Experiment in Odisha, India," Working Papers in Economics 833, University of Gothenburg, Department of Economics.
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  122. Xiaohong Chen & Victor Chernozhukov & Sokbae (Simon) Lee & Whitney K. Newey, 2011. "Local identification of nonparametric and semiparametric models," CeMMAP working papers 17/11, Institute for Fiscal Studies.

    Cited by:

    1. Zhongren Chen & Siyu Chen & Zhengling Qi & Xiaohong Chen & Zhuoran Yang, 2025. "Quantile-Optimal Policy Learning under Unmeasured Confounding," Papers 2506.07140, arXiv.org.
    2. Yu Zhu, 2020. "Inference in nonparametric/semiparametric moment equality models with shape restrictions," Quantitative Economics, Econometric Society, vol. 11(2), pages 609-636, May.
    3. Dalderop, Jeroen, 2023. "Semiparametric estimation of latent variable asset pricing models," Journal of Econometrics, Elsevier, vol. 236(1).
    4. Chen, Qihui, 2021. "Robust and optimal estimation for partially linear instrumental variables models with partial identification," Journal of Econometrics, Elsevier, vol. 221(2), pages 368-380.
    5. Xiaohong Chen & Demian Pouzo, 2014. "Sieve Wald and QLR Inferences on Semi/nonparametric Conditional Moment Models," Papers 1411.1144, arXiv.org, revised Mar 2015.
    6. Yingyao Hu & Ji‐Liang Shiu, 2018. "Identification and estimation of semi‐parametric censored dynamic panel data models of short time periods," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 55-85, February.
    7. Matzkin, Rosa L., 2016. "On independence conditions in nonseparable models: Observable and unobservable instruments," Journal of Econometrics, Elsevier, vol. 191(2), pages 302-311.
    8. Xiaohong Chen & Demian Pouzo, 2014. "Sieve Wald and QLR Inferences on Semi/nonparametric Conditional Moment Models," CeMMAP working papers 38/14, Institute for Fiscal Studies.
    9. Timothy M. Christensen, 2014. "Nonparametric identification of positive eigenfunctions," CeMMAP working papers 37/14, Institute for Fiscal Studies.
    10. Hidehiko Ichimura & Whitney K. Newey, 2017. "The influence function of semiparametric estimators," CeMMAP working papers 06/17, Institute for Fiscal Studies.
    11. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2016. "Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action," Quantitative Marketing and Economics (QME), Springer, vol. 14(4), pages 271-323, December.
    12. Jason R. Blevins & Wei Shi & Donald R. Haurin & Stephanie Moulton, 2020. "A Dynamic Discrete Choice Model Of Reverse Mortgage Borrower Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1437-1477, November.
    13. Chen, Xiaohong & Liao, Yuan & Wang, Weichen, 2025. "Inference on time series nonparametric conditional moment restrictions using nonlinear sieves," Journal of Econometrics, Elsevier, vol. 249(PA).
    14. Marcin Penconek & Paweł Strawiński, 2022. "Generalisation of the gross flows model," Working Papers 2022-17, Faculty of Economic Sciences, University of Warsaw.
    15. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers 59/15, Institute for Fiscal Studies.
    16. Victor H. Aguiar & Nail Kashaev, 2018. "Stochastic Revealed Preferences with Measurement Error," Papers 1810.05287, arXiv.org, revised Sep 2020.
    17. Chen, Xiaohong & Pouzo, Demian & Powell, James L., 2019. "Penalized sieve GEL for weighted average derivatives of nonparametric quantile IV regressions," Journal of Econometrics, Elsevier, vol. 213(1), pages 30-53.
    18. Enache, Andreea & Florens, Jean-Pierre, 2024. "Quantile analysis of “hazard-rate” game models," Journal of Econometrics, Elsevier, vol. 238(2).
    19. Zhongren Chen & Siyu Chen & Zhengling Qi & Xiaohong Chen & Zhuoran Yang, 2025. "Quantile-Optimal Policy Learning under Unmeasured Confounding," Cowles Foundation Discussion Papers 2469, Cowles Foundation for Research in Economics, Yale University.
    20. Loh, Isaac, 2023. "Genericity of the completeness condition with constrained instruments," Economics Letters, Elsevier, vol. 224(C).
    21. Doug J. Chung & Byungyeon Kim & Byoung G. Park, 2021. "The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training," Management Science, INFORMS, vol. 67(11), pages 7046-7074, November.
    22. Andrew Chesher & Adam Rosen, 2014. "Generalized instrumental variable models," CeMMAP working papers 04/14, Institute for Fiscal Studies.
    23. Dunker , Fabian & Florens, Jean-Pierre & Hohage, Thorsten & Johannes, Jan & Mammen, Enno, 2013. "Iterative estimation of solutions to noisy nonlinear operator equations in nonparametric instrumental regression," LIDAM Discussion Papers ISBA 2013041, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    24. Christoph Breunig, 2019. "Specification Testing in Nonparametric Instrumental Quantile Regression," Papers 1909.10129, arXiv.org.
    25. Feng, Junlong, 2024. "Matching points: Supplementing instruments with covariates in triangular models," Journal of Econometrics, Elsevier, vol. 238(1).
    26. Ben Deaner, 2018. "Proxy Controls and Panel Data," Papers 1810.00283, arXiv.org, revised Nov 2023.
    27. de Castro, Luciano & Galvao, Antonio F. & Kaplan, David M. & Liu, Xin, 2019. "Smoothed GMM for quantile models," Journal of Econometrics, Elsevier, vol. 213(1), pages 121-144.
    28. Jean-Jacques Forneron, 2019. "A Sieve-SMM Estimator for Dynamic Models," Papers 1902.01456, arXiv.org, revised Jan 2023.
    29. Fabian Dunker, 2015. "Adaptive estimation for some nonparametric instrumental variable models," Papers 1511.03977, arXiv.org, revised Aug 2021.
    30. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    31. Cazals, Catherine & Fève, Frédérique & Florens, Jean-Pierre & Simar, Léopold, 2016. "Nonparametric instrumental variables estimation for efficiency frontier," Journal of Econometrics, Elsevier, vol. 190(2), pages 349-359.
    32. Xiaohong Chen & Andres Santos, 2015. "Overidentification in Regular Models," Cowles Foundation Discussion Papers 1999, Cowles Foundation for Research in Economics, Yale University.
    33. Timothy M. Christensen, 2014. "Nonparametric identification of positive eigenfunctions," CeMMAP working papers CWP37/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    34. Gilles Crommen & Jean-Pierre Florens & Ingrid Van Keilegom, 2025. "Tests of exogeneity in duration models with censored data," Papers 2510.26613, arXiv.org, revised Dec 2025.
    35. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2013. "A Dynamic Model of Subprime Mortgage Default: Estimation and Policy Implications," NBER Working Papers 18850, National Bureau of Economic Research, Inc.
    36. Jaroslav Borovicka & Lars Peter Hansen & Jose A. Scheinkman, 2015. "Misspecified Recovery," Working Papers 063_2014, Princeton University, Department of Economics, Econometric Research Program..
    37. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    38. Senay Sokullu & Irene Botosaru & Chris Muris, 2022. "Time-Varying Linear Transformation Models with Fixed Effects and Endogeneity for Short Panels," Bristol Economics Discussion Papers 22/756, School of Economics, University of Bristol, UK.
    39. Breunig, Christoph, 2016. "Specification testing in nonparametric instrumental quantile regression," SFB 649 Discussion Papers 2016-032, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    40. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness," Papers 2302.05404, arXiv.org.
    41. Chiappori, Pierre-André & Komunjer, Ivana & Kristensen, Dennis, 2015. "Nonparametric identification and estimation of transformation models," Journal of Econometrics, Elsevier, vol. 188(1), pages 22-39.
    42. Arthur Lewbel, 2018. "The Identification Zoo - Meanings of Identification in Econometrics," Boston College Working Papers in Economics 957, Boston College Department of Economics, revised 14 Dec 2019.
    43. Centorrino, Samuele & Fève, Frédérique & Florens, Jean-Pierre, 2025. "Iterative estimation of nonparametric regressions with continuous endogenous variables and discrete instruments," Journal of Econometrics, Elsevier, vol. 247(C).
    44. Victor Chernozhukov & Christian Hansen, 2013. "Quantile Models with Endogeneity," Papers 1303.7050, arXiv.org.
    45. Hiroaki Kaido & Kaspar Wüthrich, 2021. "Decentralization estimators for instrumental variable quantile regression models," Quantitative Economics, Econometric Society, vol. 12(2), pages 443-475, May.
    46. de Castro, Luciano & Cundy, Lance D. & Galvao, Antonio F. & Westenberger, Rafael, 2023. "A dynamic quantile model for distinguishing intertemporal substitution from risk aversion," European Economic Review, Elsevier, vol. 159(C).
    47. de Castro, Luciano & Galvao, Antonio F. & Montes-Rojas, Gabriel, 2020. "Quantile selection in non-linear GMM quantile models," Economics Letters, Elsevier, vol. 195(C).

  123. Victor Chernozhukov & Ivan Fernandez-Val & Amanda Kowalski, 2011. "Quantile Regression with Censoring and Endogeneity," Papers 1104.4580, arXiv.org, revised Mar 2014.

    Cited by:

    1. Chen, Songnian, 2018. "Sequential estimation of censored quantile regression models," Journal of Econometrics, Elsevier, vol. 207(1), pages 30-52.
    2. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Alina Ozhegova & Evgeniy M. Ozhegov, 2018. "Heterogeneity in demand for performances and seats in the theatre," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(3), pages 131-145, June.
    4. Olga Miroshnichenko & Elena Iakovleva & Natalia Voronova, 2022. "Banking Sector Profitability: Does Household Income Matter?," Sustainability, MDPI, vol. 14(6), pages 1-19, March.
    5. Yangyang Chen & Rui Ge & Henock Louis & Leon Zolotoy, 2019. "Stock liquidity and corporate tax avoidance," Review of Accounting Studies, Springer, vol. 24(1), pages 309-340, March.
    6. Erik Figueiredo & Luiz Renato Lima & Gianluca Orefice, 2016. "Migration and Regional Trade Agreements: A (New) Gravity Estimation," Review of International Economics, Wiley Blackwell, vol. 24(1), pages 99-125, February.
    7. Javier Alejo & Antonio F. Galvao & Julian Martinez-Iriarte & Gabriel Montes-Rojas, 2023. "Unconditional Quantile Partial Effects via Conditional Quantile Regression," Papers 2301.07241, arXiv.org, revised Dec 2023.
    8. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    9. Young-Joo Kim & Vincent Daly, 2019. "The Education Gradient in Health: The Case of Obesity in the UK and US," Economics Discussion Papers 2019-4, School of Economics, Kingston University London.
    10. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    11. Jaeun Choi & A. James O'Malley, 2017. "Estimating the causal effect of treatment in observational studies with survival time end points and unmeasured confounding," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(1), pages 159-185, January.
    12. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2012. "Inference on counterfactual distributions," CeMMAP working papers 05/12, Institute for Fiscal Studies.
    13. L. Benfratello & A. Bottasso & C. Piccardo, 2022. "R&D and export performance: exploring heterogeneity along the export intensity distribution," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(2), pages 189-232, June.
    14. Wang, Qian, 2025. "Identification of quantile regression models with endogeneity," Economics Letters, Elsevier, vol. 254(C).
    15. Männasoo, Kadri, 2022. "Working hours and gender wage differentials: Evidence from the American Working Conditions Survey," Labour Economics, Elsevier, vol. 76(C).
    16. Konstantinos Konstantaras & Vasilios Sogiakas, 2019. "Is stock liquidity transferred and upgraded in acquisitions? Evidence from liquidity synergies in US freeze-outs," Annals of Operations Research, Springer, vol. 282(1), pages 179-216, November.
    17. Amanda E. Kowalski, 2016. "Doing More When You're Running LATE: Applying Marginal Treatment Effect Methods to Examine Treatment Effect Heterogeneity in Experiments," NBER Working Papers 22363, National Bureau of Economic Research, Inc.
    18. Bo Wei & Limin Peng & Mei‐Jie Zhang & Jason P. Fine, 2021. "Estimation of causal quantile effects with a binary instrumental variable and censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 559-578, July.
    19. Mukasa Adamon N. & Anthony M. Simpasa & Adeleke Oluwole Salami, 2017. "Working Paper 247 - Credit constraints and farm productivity: Micro-level evidence from smallholder farmers in Ethiopia," Working Paper Series 2356, African Development Bank.
    20. Marinho Bertanha & Andrew H. McCallum & Nathan Seegert, 2021. "Better Bunching, Nicer Notching," Finance and Economics Discussion Series 2021-002, Board of Governors of the Federal Reserve System (U.S.).
    21. Leon Zolotoy & Don O’Sullivan & Geoffrey P. Martin & Robert M. Wiseman, 2021. "Stakeholder Agency Relationships: CEO Stock Options and Corporate Tax Avoidance," Journal of Management Studies, Wiley Blackwell, vol. 58(3), pages 782-814, May.
    22. Whitney Newey & Sami Stouli, 2018. "Control Variables, Discrete Instruments, and Identification of Structural Functions," Bristol Economics Discussion Papers 18/702, School of Economics, University of Bristol, UK.
    23. Jad Beyhum & Jean-Pierre FLorens & Ingrid Van Keilegom, 2020. "Nonparametric instrumental regression with right censored duration outcomes," Papers 2011.10423, arXiv.org.
    24. Jad Beyhum, 2021. "Two-stage least squares with a randomly right censored outcome," Papers 2110.05107, arXiv.org.
    25. Chen, Songnian, 2025. "Quantile regression with group-level treatments," Journal of Econometrics, Elsevier, vol. 251(C).
    26. Mendolia, Silvia & Paloyo, Alfredo R. & Walker, Ian, 2016. "Heterogeneous effects of high school peers on educational outcomes," Ruhr Economic Papers 612, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    27. Jad Beyhum & Lorenzo Tedesco & Ingrid Van Keilegom, 2022. "Instrumental variable quantile regression under random right censoring," Papers 2209.01429, arXiv.org, revised Feb 2023.
    28. Jad Beyhum & Jean-Pierre Florens & Ingrid Van Keilegom, 2021. "A nonparametric instrumental approach to endogeneity in competing risks models," Papers 2105.00946, arXiv.org.
    29. Aldieri, Luigi & Vinci, Concetto Paolo, 2017. "Quantile regression for Panel data: An empirical approach for knowledge spillovers endogeneity," MPRA Paper 76405, University Library of Munich, Germany.
    30. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    31. Evgeniy M. Ozhegov & Alina Ozhegova, 2020. "Regression tree model for prediction of demand with heterogeneity and censorship," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 489-500, April.
    32. Chen, Songnian, 2019. "Quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 209(1), pages 1-17.
    33. Yinchu Zhu, 2018. "Learning non-smooth models: instrumental variable quantile regressions and related problems," Papers 1805.06855, arXiv.org, revised Sep 2019.
    34. Justin Falk, 2023. "The Effects of Work Requirements on the Employment and Income of TANF Participants: Working Paper 2023-03," Working Papers 58867, Congressional Budget Office.
    35. Victor Chernozhukov & Iv'n Fern'ndez-Val & Sukjin Han & Amanda E. Kowalski, 2018. "Censored Quantile Instrumental Variable Estimation with Stata," Cowles Foundation Discussion Papers 2120, Cowles Foundation for Research in Economics, Yale University.
    36. Manuel Arellano & Stéphane Bonhomme, 2017. "Sample Selection in Quantile Regression: A Survey," Working Papers wp2017_1702, CEMFI.
    37. James E. Payne & James W. Saunoris, 2020. "Corruption and Firm Tax Evasion in Transition Economies: Results from Censored Quantile Instrumental Variables Estimation," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 48(2), pages 195-206, June.
    38. Evgeniy M. Ozhegov & Daria Teterina, 2018. "The Ensemble Method For Censored Demand Prediction," HSE Working papers WP BRP 200/EC/2018, National Research University Higher School of Economics.
    39. Pourya Valizadeh & Shu Wen Ng, 2021. "Would A National Sugar‐Sweetened Beverage Tax in the United States Be Well Targeted?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 961-986, May.
    40. Alzuabi, Raslan & Brown, Sarah & Gray, Daniel & Harris, Mark N. & Spencer, Christopher, 2019. "Household Saving, Health, and Healthcare Utilisation in Japan," CEI Working Paper Series 2018-17, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    41. Brown, Sarah & Taylor, Karl, 2015. "Charitable Behaviour and the Big Five Personality Traits: Evidence from UK Panel Data," IZA Discussion Papers 9318, IZA Network @ LISER.
    42. Schmillen, Achim & Umkehrer, Matthias, 2013. "The scars of youth : effects of early-career unemployment on future unemployment experience," IAB-Discussion Paper 201306, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    43. Fernández-Val, Iván & van Vuuren, Aico & Vella, Francis, 2018. "Nonseparable Sample Selection Models with Censored Selection Rules: An Application to Wage Decompositions," IZA Discussion Papers 11294, IZA Network @ LISER.
    44. Abi Adams & Richard Blundell & Martin Browning & Ian Crawford, 2015. "Prices versus preferences: taste change and revealed preference," IFS Working Papers W15/11, Institute for Fiscal Studies.
    45. Ivan Fernandez-Val & Aico van Vuuren & Francis Vella, 2018. "Nonseparable sample selection models with censored selection rules," CeMMAP working papers CWP10/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    46. Kushawaha, Deepak, 2025. "Understanding the role of greenfield and mergers & acquisitions foreign direct investments in renewable energy expansion in developing countries," Energy Economics, Elsevier, vol. 145(C).
    47. Roger, Muriel & Savignac, Frédérique & d’Addio, Anna, 2020. "Pensions and household savings: cross-country heterogeneity in Europe," Working Paper Series 2372, European Central Bank.
    48. Basha, Shabeen Afsar & Bennasr, Hamdi & Goaied, Mohamed, 2023. "Financial literacy, financial development, and leverage of small firms," International Review of Financial Analysis, Elsevier, vol. 86(C).
    49. Jayeeta Bhattacharya, 2020. "Quantile regression with generated dependent variable and covariates," Papers 2012.13614, arXiv.org.
    50. Chen, Songnian & Wang, Qian, 2020. "Semiparametric estimation of a censored regression model with endogeneity," Journal of Econometrics, Elsevier, vol. 215(1), pages 239-256.
    51. Lee, Sungwon, 2023. "Efficient estimation of a triangular system of equations for quantile regression," Economics Letters, Elsevier, vol. 226(C).
    52. Evgeniy M. Ozhegov & Alina Ozhegova, 2017. "Regression Tree Model for Analysis of Demand with Heterogeneity and Censorship," HSE Working papers WP BRP 174/EC/2017, National Research University Higher School of Economics.
    53. David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.
    54. Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023. "Penalized Model Averaging for High Dimensional Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202302, University of Kansas, Department of Economics.
    55. Jau-er Chen & Masanori Kashiwagi, 2017. "The Japanese Taylor rule estimated using censored quantile regressions," Empirical Economics, Springer, vol. 52(1), pages 357-371, February.
    56. Li, Tong & Oka, Tatsushi, 2015. "Set identification of the censored quantile regression model for short panels with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 363-377.
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    58. Sanna Nivakoski, 2020. "Wealth and the effect of subjective survival probability," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(2), pages 633-670, April.
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    60. Gilles Crommen & Jean-Pierre Florens & Ingrid Van Keilegom, 2025. "Tests of exogeneity in duration models with censored data," Papers 2510.26613, arXiv.org, revised Dec 2025.
    61. Amanda E. Kowalski, 2018. "Extrapolation using Selection and Moral Hazard Heterogeneity from within the Oregon Health Insurance Experiment," Cowles Foundation Discussion Papers 2135, Cowles Foundation for Research in Economics, Yale University.
    62. Victor Champonnois & Olivier Chanel & Costin Protopopescu, 2022. "Quantile Regression Analysis of Censored Data with Selection An Application to Willingness-to-Pay Data," AMSE Working Papers 2214, Aix-Marseille School of Economics, France.
    63. Blanco, German & Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2018. "Bounds on Average and Quantile Treatment Effects on Duration Outcomes under Censoring, Selection, and Noncompliance," GLO Discussion Paper Series 288, Global Labor Organization (GLO).
    64. Tatsushi Oka & Ken Yamada, 2019. "Heterogeneous Impact of the Minimum Wage: Implications for Changes in Between- and Within-group Inequality," Papers 1903.03925, arXiv.org, revised Jul 2019.
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    80. Viola, Alessandra Pasqualina & Klotzle, Marcelo Cabus & Pinto, Antonio Carlos Figueiredo & da Silveira Barbedo, Claudio Henrique, 2019. "Foreign exchange interventions in Brazil and their impact on volatility: A quantile regression approach," Research in International Business and Finance, Elsevier, vol. 47(C), pages 251-263.
    81. Kiran Krishnamurthy, Chandra & Kriström, Bengt, 2013. "Determinants of the price-premium for Green Energy: Evidence from an OECD cross-section," CERE Working Papers 2013:7, CERE - the Center for Environmental and Resource Economics, revised 30 Jun 2014.
    82. J Doran & G Ryan, 2016. "The effectiveness of R&D and external interaction for innovation: Insights from quantile regression," Economic Issues Journal Articles, Economic Issues, vol. 21(1), pages 47-65, March.
    83. Vial, Virginie & Hanoteau, Julien, 2015. "Returns to Micro-Entrepreneurship in an Emerging Economy: A Quantile Study of Entrepreneurial Indonesian Households’ Welfare," World Development, Elsevier, vol. 74(C), pages 142-157.
    84. Liqiong Chen & Antonio F. Galvao & Suyong Song, 2021. "Quantile Regression with Generated Regressors," Econometrics, MDPI, vol. 9(2), pages 1-35, April.
    85. Holland, Sara B. & Sarkissian, Sergei & Schill, Michael J. & Warnock, Francis E., 2024. "Nonlinearities and a pecking order in cross-border investment," Journal of Banking & Finance, Elsevier, vol. 166(C).
    86. Guo, Jing & Wang, Lei & Zhang, Zhengyu, 2022. "Identification and estimation of a heteroskedastic censored regression model with random coefficient dummy endogenous regressors," Economic Modelling, Elsevier, vol. 110(C).

  124. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference for High-Dimensional Sparse Econometric Models," Papers 1201.0220, arXiv.org.

    Cited by:

    1. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    2. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    3. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    4. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, "undated". "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    5. Bottmer, Lea & Croux, Christophe & Wilms, Ines, 2022. "Sparse regression for large data sets with outliers," European Journal of Operational Research, Elsevier, vol. 297(2), pages 782-794.
    6. Mueller, Hannes & Rauh, Christopher, 2016. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," CEPR Discussion Papers 11516, C.E.P.R. Discussion Papers.
    7. Matteo Barigozzi & Christian Brownlees, 2015. "Nets: Network Estimation for Time Series," Working Papers 723, Barcelona School of Economics.
    8. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    9. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2020. "Measurement of Factor Strenght: Theory and Practice," CESifo Working Paper Series 8146, CESifo.
    11. Daniels, David P. & Zlatev, Julian J., 2019. "Choice architects reveal a bias toward positivity and certainty," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 132-149.
    12. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    13. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    14. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    15. Gambacorta, Leonardo & Huang, Yiping & Qiu, Han & Wang, Jingyi, 2019. "How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm," CEPR Discussion Papers 14259, C.E.P.R. Discussion Papers.
    16. Bonan, Jacopo & Burlacu, Sergiu & Galliera, Arianna, 2023. "Prosociality in variants of the dictator game: Evidence from children in El Salvador," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 107(C).
    17. Federico A. Bugni & Mehmet Caner & Anders Bredahl Kock & Soumendra Lahiri, 2016. "Inference in partially identified models with many moment inequalities using Lasso," CREATES Research Papers 2016-12, Department of Economics and Business Economics, Aarhus University.
    18. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
    19. Song Song & Peter J. Bickel, 2011. "Large Vector Auto Regressions," Papers 1106.3915, arXiv.org.
    20. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    21. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers CWP28/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    22. André Nunes Maranhão, 2024. "Brazilian Business Cycle Analysis in a High-Dimensional and Time-Irregular Span Context," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 20(1), pages 1-58, August.
    23. Carlos A. Manzanares & Ying Jiang & Patrick Bajari, 2015. "Improving Policy Functions in High-Dimensional Dynamic Games," NBER Working Papers 21124, National Bureau of Economic Research, Inc.
    24. André Nunes Maranhão & Nicole Rennó Castro, 2023. "Dissecting Brazilian agriculture business cycles in high-dimensional and time-irregular span contexts," Empirical Economics, Springer, vol. 65(4), pages 1543-1578, October.
    25. Myrto Kalouptsidi, 2014. "Time to Build and Fluctuations in Bulk Shipping," American Economic Review, American Economic Association, vol. 104(2), pages 564-608, February.
    26. Alec Smith & B. Douglas Bernheim & Colin Camerer & Antonio Rangel, 2013. "Neural Activity Reveals Preferences Without Choices," NBER Working Papers 19270, National Bureau of Economic Research, Inc.
    27. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019. "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers 1902.10991, arXiv.org, revised Dec 2020.
    28. Xu, Ning & Hong, Jian & Fisher, Timothy, 2016. "Model selection consistency from the perspective of generalization ability and VC theory with an application to Lasso," MPRA Paper 71670, University Library of Munich, Germany.
    29. Gold, David & Lederer, Johannes & Tao, Jing, 2020. "Inference for high-dimensional instrumental variables regression," Journal of Econometrics, Elsevier, vol. 217(1), pages 79-111.
    30. Lim, Dennis & Wang, Wenjie & Zhang, Yichong, 2024. "A conditional linear combination test with many weak instruments," Journal of Econometrics, Elsevier, vol. 238(2).
    31. Yichen Gao & Yu Zhang & Ximing Wu, 2015. "Penalized exponential series estimation of copula densities with an application to intergenerational dependence of body mass index," Empirical Economics, Springer, vol. 48(1), pages 61-81, February.
    32. Scoles, Brooke & Nicodemo, Catia, 2022. "Doctors’ attitudes toward specific medical conditions," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 182-199.
    33. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    34. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
    35. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
    36. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    37. Kock, Anders Bredahl, 2016. "Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models," Journal of Econometrics, Elsevier, vol. 195(1), pages 71-85.
    38. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
    39. Demian Pouzo, 2015. "On the Non-Asymptotic Properties of Regularized M-estimators," Papers 1512.06290, arXiv.org, revised Oct 2016.
    40. De La Maza, Cristóbal & Davis, Alex & Azevedo, Inês, 2021. "Welfare analysis of the ecological impacts of electricity production in Chile using the sparse multinomial logit model," Ecological Economics, Elsevier, vol. 184(C).
    41. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    42. Imran Rasul & Pedro Souza & Aureo de Paula, 2023. "Identifying Network Ties from Panel Data: Theory and an application to tax competition," POID Working Papers 081, Centre for Economic Performance, LSE.
    43. Damian Kozbur, 2013. "Inference in additively separable models with a high-dimensional set of conditioning variables," ECON - Working Papers 284, Department of Economics - University of Zurich, revised Apr 2018.
    44. Ng, Serena, 2013. "Variable Selection in Predictive Regressions," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 752-789, Elsevier.
    45. Huseynov, Samir & Palma, Marco A., "undated". "Does California’s LCFS Reduce CO2 Emissions?," 2018 Annual Meeting, August 5-7, Washington, D.C. 274200, Agricultural and Applied Economics Association.
    46. Victor Chernozhukov & Chris Hansen & Martin Spindler, 2016. "High-Dimensional Metrics in R," Papers 1603.01700, arXiv.org, revised Aug 2016.
    47. Riccardo Di Francesco, 2023. "Ordered Correlation Forest," Papers 2309.08755, arXiv.org.
    48. Roberto Casarin & Daniel Felix Ahelegbey & Monica Billio, 2014. "Sparse Graphical Vector Autoregression: A Bayesian Approach," Working Papers 2014:29, Department of Economics, University of Venice "Ca' Foscari".
    49. Zhu, Ying, 2018. "Sparse linear models and l1-regularized 2SLS with high-dimensional endogenous regressors and instruments," Journal of Econometrics, Elsevier, vol. 202(2), pages 196-213.
    50. Cowgill, Bo & Perkowski, Patryk, 2024. "Delegation in Hiring: Evidence from a Two-Sided Audit," IZA Discussion Papers 17004, IZA Network @ LISER.
    51. Florens, Jean-Pierre & Van Bellegem, Sébastien, 2015. "Instrumental variable estimation in functional linear models," Journal of Econometrics, Elsevier, vol. 186(2), pages 465-476.
    52. Chatterjee, A. & Gupta, S. & Lahiri, S.N., 2015. "On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property," Journal of Econometrics, Elsevier, vol. 186(2), pages 317-324.
    53. Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    54. Li, Zhaoyuan & Yao, Jianfeng, 2019. "Testing for heteroscedasticity in high-dimensional regressions," Econometrics and Statistics, Elsevier, vol. 9(C), pages 122-139.
    55. Kock, Anders Bredahl & Callot, Laurent, 2015. "Oracle inequalities for high dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 186(2), pages 325-344.
    56. Matthew Gentzkow & Bryan T. Kelly & Matt Taddy, 2017. "Text as Data," NBER Working Papers 23276, National Bureau of Economic Research, Inc.
    57. Dai, Wei & Tsang, Ka Wai, 2023. "A resampling approach for confidence intervals in linear time-series models after model selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

  125. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Estimation of treatment effects with high-dimensional controls," CeMMAP working papers 42/11, Institute for Fiscal Studies.

    Cited by:

    1. Chen, Daniel L., 2016. "Priming Ideology: Why Presidential Elections Affect U.S. Judges," IAST Working Papers 16-39, Institute for Advanced Study in Toulouse (IAST), revised Aug 2016.
    2. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.
    3. Chen, Daniel L. & Michaeli, Moti & Spiro, Daniel, 2016. "Ideological Perfectionism," TSE Working Papers 16-694, Toulouse School of Economics (TSE).

  126. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Iv'an Fern'andez-Val, 2011. "Conditional Quantile Processes based on Series or Many Regressors," Papers 1105.6154, arXiv.org, revised Aug 2018.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.
    2. Dimitrios Panagiotou, 2021. "Re-examining the leverage effect and gold’s safe haven properties with the utilization of the implied volatility of gold: a non-parametric quantile regression approach," SN Business & Economics, Springer, vol. 1(7), pages 1-18, July.
    3. Xiaorong Yang & Jia Chen & Degui Li & Runze Li, 2023. "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure," Papers 2303.13218, arXiv.org.
    4. Zheng Fang & Juwon Seo, 2021. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Econometrica, Econometric Society, vol. 89(5), pages 2439-2458, September.
    5. Charlier, Isabelle & Paindaveine, Davy & Saracco, Jérôme, 2015. "Conditional quantile estimation based on optimal quantization: From theory to practice," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 20-39.
    6. Victor Chernozhukov & Ivan Fernandez-Val & Stefan Hoderlein & Hajo Holzmann & Whitney K. Newey, 2013. "Nonparametric identification in panels using quantiles," CeMMAP working papers 66/13, Institute for Fiscal Studies.
    7. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results," Papers 1212.0442, arXiv.org, revised Jun 2015.
    8. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers CWP75/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Rangan Gupta & Syed Jawad Hussain Shahzad & Xin Sheng & Sowmya Subramaniam, 2023. "The role of oil and risk shocks in the high‐frequency movements of the term structure of interest rates: Evidence from the U.S. Treasury market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1845-1857, April.
    10. Alexandre Belloni & Victor Chernozhukov, 2009. "L1-Penalized Quantile Regression in High-Dimensional Sparse Models," Papers 0904.2931, arXiv.org, revised Sep 2019.
    11. Massimiliano Caporin & Petre Caraiani & Oguzhan Cepni & Rangan Gupta, 2024. "Predicting the Conditional Distribution of US Stock Market Systemic Stress: The Role of Climate Risks," Working Papers 202407, University of Pretoria, Department of Economics.
    12. Dong, Chaohua & Chen, Rong & Xiao, Zhijie & Liu, Weiyi, 2024. "Functional quantile autoregression," Journal of Econometrics, Elsevier, vol. 244(2).
    13. Onur Polat & Rangan Gupta & Elie Bouri & Mariem Brahim, 2025. "Climate Risks and Predictability of the Conditional Distributions of Rare Earth Stock Returns and Volatility," Working Papers 202517, University of Pretoria, Department of Economics.
    14. Hao, Meiling & Lin, Yuanyuan & Shen, Guohao & Su, Wen, 2023. "Nonparametric inference on smoothed quantile regression process," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    15. Arun Chandrasekhar & Victor Chernozhukov & Francesca Molinari & Paul Schrimpf, 2012. "Inference for best linear approximations to set identified functions," CeMMAP working papers CWP43/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    16. Gimenes, Nathalie & Guerre, Emmanuel, 2022. "Quantile regression methods for first-price auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 224-247.
    17. Lin, Fangzheng & Tang, Yanlin & Zhu, Zhongyi, 2020. "Weighted quantile regression in varying-coefficient model with longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    18. Bonaccolto, G. & Caporin, M. & Gupta, R., 2018. "The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 446-469.
    19. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    20. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2021. "Bootstrap inference for panel data quantile regression," Papers 2111.03626, arXiv.org.
    21. Rangan Gupta & Syed Jawad Hussain Shahzad & Xin Sheng & Sowmya Subramaniam, 2020. "The Role of Oil and Risk Shocks in the High-Frequency Movements of the Term Structure of Interest Rates of the United States," Working Papers 202063, University of Pretoria, Department of Economics.
    22. Zhongjun Qu & Jungmo Yoon, 2011. "Nonparametric Estimation and Inference on Conditional Quantile Processes," Boston University - Department of Economics - Working Papers Series WP2011-059, Boston University - Department of Economics.
    23. Matias D. Cattaneo & Yingjie Feng & Rocio Titiunik, 2019. "Prediction Intervals for Synthetic Control Methods," Papers 1912.07120, arXiv.org, revised Sep 2021.
    24. Yuefeng Han & Likai Chen & Wei Biao Wu, 2025. "Estimation of High-dimensional Nonlinear Vector Autoregressive Models," Papers 2511.18641, arXiv.org.
    25. Zequn Jin & Gaoqian Xu & Xi Zheng & Yahong Zhou, 2025. "Policy Learning under Unobserved Confounding: A Robust and Efficient Approach," Papers 2507.20550, arXiv.org.
    26. Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2015. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Working Papers wp2015_1506, CEMFI.
    27. Zheng Fang & Andres Santos & Azeem M. Shaikh & Alexander Torgovitsky, 2020. "Inference for Large-Scale Linear Systems with Known Coefficients," Papers 2009.08568, arXiv.org, revised Sep 2021.
    28. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2017. "Quantile graphical models: prediction and conditional independence with applications to systemic risk," CeMMAP working papers CWP54/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    29. Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2016. "Factorisable multi-task quantile regression," SFB 649 Discussion Papers 2016-057, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    30. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    31. Elie Bouri & Rangan Gupta & Clement Kweku Kyei & Sowmya Subramaniam, 2020. "High-Frequency Movements of the Term Structure of Interest Rates of the United States: The Role of Oil Market Uncertainty," Working Papers 202085, University of Pretoria, Department of Economics.
    32. Vira Semenova, 2020. "Generalized Lee Bounds," Papers 2008.12720, arXiv.org, revised May 2025.
    33. Matias D. Cattaneo & Ricardo P. Masini & William G. Underwood, 2022. "Yurinskii's Coupling for Martingales," Papers 2210.00362, arXiv.org, revised Aug 2025.
    34. Shujie Ma & Oliver Linton & Jiti Gao, 2018. "Estimation in semiparametric quantile factor models," CeMMAP working papers CWP07/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    35. Matteo Bonato & Riza Demirer & Rangan Gupta & Abeeb Olaniran, 2025. "Does Mining Activity Drive Crash Risks in Cryptocurrency Markets? An Application to Bitcoin," Working Papers 202530, University of Pretoria, Department of Economics.
    36. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Papers 1312.7186, arXiv.org, revised Jun 2016.
    37. Chen, Xirong & Li, Degui & Li, Qi & Li, Zheng, 2019. "Nonparametric estimation of conditional quantile functions in the presence of irrelevant covariates," Journal of Econometrics, Elsevier, vol. 212(2), pages 433-450.
    38. Richard Spady & Sami Stouli, 2016. "Dual regression," CeMMAP working papers 04/16, Institute for Fiscal Studies.
    39. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression and other z-estimation problems," CeMMAP working papers 74/13, Institute for Fiscal Studies.
    40. Matias D. Cattaneo & Richard K. Crump & Max H. Farrell & Yingjie Feng, 2019. "On binscatter," Staff Reports 881, Federal Reserve Bank of New York.
    41. Zhipeng Liao & Xiaoxia Shi, 2020. "A nondegenerate Vuong test and post selection confidence intervals for semi/nonparametric models," Quantitative Economics, Econometric Society, vol. 11(3), pages 983-1017, July.
    42. Torossian, Léonard & Picheny, Victor & Faivre, Robert & Garivier, Aurélien, 2020. "A review on quantile regression for stochastic computer experiments," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    43. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Fernández-Val, Iván, 2019. "Conditional quantile processes based on series or many regressors," Journal of Econometrics, Elsevier, vol. 213(1), pages 4-29.
    44. Manuel Arellano & Stéphane Bonhomme, 2015. "Nonlinear panel data estimation via quantile regressions," CeMMAP working papers CWP40/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    45. Arun Chandrasekhar & Victor Chernozhukov & Francesca Molinari & Paul Schrimpf, 2019. "Best linear approximations to set identified functions: with an application to the gender wage gap," CeMMAP working papers 09/19, Institute for Fiscal Studies.
    46. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    47. Rosen, Adam M., 2012. "Set identification via quantile restrictions in short panels," Journal of Econometrics, Elsevier, vol. 166(1), pages 127-137.
    48. Victor Chernozhukov & Iv'an Fern'andez-Val & Blaise Melly & Kaspar Wuthrich, 2016. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Papers 1608.05142, arXiv.org, revised Aug 2018.
    49. Xi Chen & Victor Chernozhukov & Iv'an Fern'andez-Val & Scott Kostyshak & Ye Luo, 2018. "Shape-Enforcing Operators for Point and Interval Estimators," Papers 1809.01038, arXiv.org, revised Feb 2021.
    50. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Uniform post selection inference for LAD regression models," CeMMAP working papers 24/13, Institute for Fiscal Studies.
    51. Feng, Xingdong & Liu, Qiaochu & Wang, Caixing, 2023. "A lack-of-fit test for quantile regression process models," Statistics & Probability Letters, Elsevier, vol. 192(C).
    52. Cattaneo, Matias D. & Jansson, Michael & Ma, Xinwei, 2024. "Local regression distribution estimators," Journal of Econometrics, Elsevier, vol. 240(2).
    53. Anqi Li & Shiko Maruyama, 2024. "Who suffered most in the pandemic? A distribution regression analysis of happiness in Japan," The Japanese Economic Review, Springer, vol. 75(4), pages 637-690, December.
    54. Mehmet Balcilar & Kenny Kutu & Sonali Das & Rangan Gupta, 2025. "Predicting the Conditional Distributions of Inflation and Inflation Uncertainty in South Africa: The Role of Climate Risks," Working Papers 202529, University of Pretoria, Department of Economics.
    55. Tengyuan Liang, 2022. "Universal prediction band via semi‐definite programming," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1558-1580, September.
    56. Rangan Gupta & Damien Moodley, 2023. "Housing Search Activity and Quantiles-Based Predictability of Housing Price Movements in the United States," Working Papers 202335, University of Pretoria, Department of Economics.
    57. Mehmet Balcilar & Seyi Saint Akadiri & Rangan Gupta & Stephen M. Miller, 2019. "Partisan Conflict and Income Inequality in the United States: A Nonparametric Causality-in-Quantiles Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 65-82, February.
    58. Byunghoon Kang, 2019. "Inference in Nonparametric Series Estimation with Specification Searches for the Number of Series Terms," Papers 1909.12162, arXiv.org, revised Feb 2020.
    59. Petre Caraiani & Onur Polat & Rangan Gupta & Elie Bouri, 2025. "Climate Risks and Predictability of Financial Risks in the US Banking Sector," Working Papers 202507, University of Pretoria, Department of Economics.
    60. Shujie Ma & Oliver Linton & Jiti Gao, 2017. "Estimation and inference in semiparametric quantile factor models," Monash Econometrics and Business Statistics Working Papers 8/17, Monash University, Department of Econometrics and Business Statistics.
    61. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    62. Efang Kong & Oliver Linton & Yingcun Xia, 2011. "Global Bahadur representation for nonparametric censored regression quantiles and its applications," CeMMAP working papers CWP33/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    63. Semenova, Vira, 2025. "Generalized Lee bounds," Journal of Econometrics, Elsevier, vol. 251(C).
    64. Victor Chernozhukov & Vira Semenova, 2018. "Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions," CeMMAP working papers CWP40/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    65. Qihui Chen, 2022. "Robust Estimation of Conditional Factor Models," Papers 2204.00801, arXiv.org, revised Apr 2022.
    66. Gupta, Rangan & Nel, Jacobus & Nielsen, Joshua & Pierdzioch, Christian, 2025. "Stock market volatility and multi-scale positive and negative bubbles," The North American Journal of Economics and Finance, Elsevier, vol. 75(PA).
    67. Ilias Chronopoulos & Aristeidis Raftapostolos & George Kapetanios, 2024. "Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 636-669.
    68. Manuel Arellano & Stéphane Bonhomme, 2017. "Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 471-496, September.
    69. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
    70. Karen X. Yan & Qi Li, 2018. "Nonparametric Estimation of a Conditional Quantile Function in a Fixed Effects Panel Data Model," JRFM, MDPI, vol. 11(3), pages 1-10, August.
    71. Mehmet Balcilar & Seyi Saint Akadiri & Rangan Gupta & Stephen M. Miller, 2017. "Partisan Conflict and Income Distribution in the United States: A Nonparametric Causality-in-Quantiles Approach," Working papers 2017-11, University of Connecticut, Department of Economics.
    72. Ferreira, Francisco H. G. & Firpo, Sergio & Galvao, Antonio F., 2017. "Estimation and Inference for Actual and Counterfactual Growth Incidence Curves," IZA Discussion Papers 10473, IZA Network @ LISER.
    73. Holly Brannelly & Andrea Macrina & Gareth W. Peters, 2021. "Stochastic measure distortions induced by quantile processes for risk quantification and valuation," Papers 2201.02045, arXiv.org.
    74. Erik Figueiredo & Luiz Lima & Georg Schaur, 2016. "The effect of the Euro on the bilateral trade distribution," Empirical Economics, Springer, vol. 50(1), pages 17-29, February.
    75. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Christian Pierdzioch, 2021. "El Niño, La Niña, and the Forecastability of the Realized Variance of Heating Oil Price Movements," Sustainability, MDPI, vol. 13(14), pages 1-23, July.

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    Cited by:

    1. Nepp, Alexander & Okhrin, Ostap & Egorova, Julia & Dzhuraeva, Zarnigor & Zykov, Alexander, 2022. "What threatens stock markets more - The coronavirus or the hype around it?," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 519-539.
    2. Mehmet Caner & Esfandiar Maasoumi & Juan Andrés Riquelme, 2016. "Moment and IV Selection Approaches: A Comparative Simulation Study," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1562-1581, December.
    3. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2020. "Ill-posed estimation in high-dimensional models with instrumental variables," Journal of Econometrics, Elsevier, vol. 219(1), pages 171-200.
    4. Andrii Babii, 2020. "High-dimensional mixed-frequency IV regression," Papers 2003.13478, arXiv.org.
    5. Adel Javanmard & Jason D. Lee, 2020. "A flexible framework for hypothesis testing in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 685-718, July.
    6. Felipe González & Magdalena Larreboure, 2021. "The Impact of the Women’s March on the U.S. House Election," Documentos de Trabajo 560, Instituto de Economia. Pontificia Universidad Católica de Chile..
    7. Ellora Derenoncourt, 2022. "Can You Move to Opportunity? Evidence from the Great Migration," American Economic Review, American Economic Association, vol. 112(2), pages 369-408, February.
    8. Alena Skolkova, 2023. "Instrumental Variable Estimation with Many Instruments Using Elastic-Net IV," CERGE-EI Working Papers wp759, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    9. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.

  128. Alexandre Belloni & Daniel Chen & Victor Chernozhukov & Christian Hansen, 2010. "Sparse Models and Methods for Optimal Instruments with an Application to Eminent Domain," Papers 1010.4345, arXiv.org, revised Apr 2015.

    Cited by:

    1. Gozgor, Giray & Li, Jing & Saleem, Irfan & Shinwari, Riazullah, 2025. "The impact of women's political empowerment on renewable energy demand: Evidence from OECD countries," Energy Economics, Elsevier, vol. 141(C).
    2. Emily Cuddy & Janet Currie, 2020. "Rules vs. Discretion: Treatment of Mental Illness in U.S. Adolescents," Working Papers 2020-10, Princeton University. Economics Department..
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    4. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    5. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    6. Dennis Lim & Wenjie Wang & Yichong Zhang, 2022. "A Conditional Linear Combination Test with Many Weak Instruments," Papers 2207.11137, arXiv.org, revised Apr 2023.
    7. Julián Caballero & Christian Upper, 2023. "What happens to EMEs when US yields go up?," BIS Working Papers 1081, Bank for International Settlements.
    8. Zhong, Wei & Gao, Yang & Zhou, Wei & Fan, Qingliang, 2021. "Endogenous treatment effect estimation using high-dimensional instruments and double selection," Statistics & Probability Letters, Elsevier, vol. 169(C).
    9. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, "undated". "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    10. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    11. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    12. Linton, Oliver & Seo, Myung Hwan & Whang, Yoon-Jae, 2023. "Testing stochastic dominance with many conditioning variables," Journal of Econometrics, Elsevier, vol. 235(2), pages 507-527.
    13. Carlana, Michela & La Ferrara, Eliana, 2021. "Apart but Connected: Online Tutoring and Student Outcomes during the COVID-19 Pandemic," CEPR Discussion Papers 15761, C.E.P.R. Discussion Papers.
    14. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    15. Gyuhyeong Goh & Jisang Yu, 2022. "Causal inference with some invalid instrumental variables: A quasi‐Bayesian approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1432-1451, December.
    16. Chen, Daniel L. & Lind, Jo Thori, 2016. "The Political Economy of Beliefs: Why Fiscal and Social Conservatives/Liberals (Sometimes) Come Hand-in-Hand," TSE Working Papers 16-722, Toulouse School of Economics (TSE).
    17. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
    18. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    19. Anil Kumar, 2014. "Do restrictions on home equity extraction contribute to lower mortgage defaults? evidence from a policy discontinuity at the Texas border," Working Papers 1410, Federal Reserve Bank of Dallas.
    20. Badawi, Adam B. & Chen, Daniel L., 2016. "The Shareholder Wealth Effects of Delaware Litigation," TSE Working Papers 16-683, Toulouse School of Economics (TSE).
    21. Rowena Gray & Greg C. Wright, 2022. "A Rising Tide? The Local Incidence of the Second Wave of Globalization," CESifo Working Paper Series 9725, CESifo.
    22. Gregor Steiner & Mark Steel, 2025. "Bayesian Model Averaging in Causal Instrumental Variable Models," Papers 2504.13520, arXiv.org, revised Feb 2026.
    23. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    24. Franz Huber & Alan Ponce & Francesco Rentocchini & Thomas Wainwright, 2020. "The Wealth of (Open Data) Nations? Examining the interplay of open government data and country-level institutions for entrepreneurial activity at the country-level," SEEDS Working Papers 1120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Nov 2020.
    25. Hinnosaar, Marit & Liu, Elaine, 2020. "Persistence in alcohol consumption: evidence from migrants," CEPR Discussion Papers 15196, C.E.P.R. Discussion Papers.
    26. Bellmann, Lutz & Hübler, Olaf, 2019. "Personal Attitudes, Job Characteristics and Health," IZA Discussion Papers 12597, IZA Network @ LISER.
    27. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    28. Santos, José Eusébio & Andini, Corrado, 2024. "Net privatisation prices in Brazil," Emerging Markets Review, Elsevier, vol. 60(C).
    29. Ariadna Jou & Tommy Morgan, 2025. "Do Relief Programs Compensate For Longevity Losses From Reccesions? Evidence From The Great Depression And The New Deal," Working Papers wp562, University of Chile, Department of Economics.
    30. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    31. Eric Gautier & Christiern Rose, 2022. "Fast, Robust Inference for Linear Instrumental Variables Models using Self-Normalized Moments," Papers 2211.02249, arXiv.org, revised Nov 2022.
    32. Zhu, Ying, 2015. "Sparse Linear Models and l1−Regularized 2SLS with High-Dimensional Endogenous Regressors and Instruments," MPRA Paper 81217, University Library of Munich, Germany.
    33. Ivan A. Canay & Magne Mogstad & Jack Mountjoy, 2020. "On the Use of Outcome Tests for Detecting Bias in Decision Making," Working Papers 2020-125, Becker Friedman Institute for Research In Economics.
    34. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    35. Kaicheng Chen, 2025. "Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity," Papers 2504.18772, arXiv.org, revised Dec 2025.
    36. Abhinandan Dalal & Patrick Blobaum & Shiva Kasiviswanathan & Aaditya Ramdas, 2024. "Anytime-Valid Inference for Double/Debiased Machine Learning of Causal Parameters," Papers 2408.09598, arXiv.org, revised Sep 2024.
    37. Guangsu Zhou & Keyi Guo, 2023. "The impact of guardian's usage of the Internet on children's cognitive skills," International Studies of Economics, John Wiley & Sons, vol. 18(2), pages 193-210, June.
    38. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    39. Adam D. Nowak & Bradley S. Price & Patrick S. Smith, 2021. "Real Estate Dictionaries Across Space and Time," The Journal of Real Estate Finance and Economics, Springer, vol. 62(1), pages 139-163, January.
    40. Milosh, Maria & Painter, Marcus & Sonin, Konstantin & Van Dijcke, David & Wright, Austin L., 2021. "Unmasking partisanship: Polarization undermines public response to collective risk," Journal of Public Economics, Elsevier, vol. 204(C).
    41. Carrasco, Marine & Tchuente, Guy, 2015. "Regularized LIML for many instruments," Journal of Econometrics, Elsevier, vol. 186(2), pages 427-442.
    42. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2012. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers CWP10/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    43. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    44. Ye Luo & Martin Spindler & Jannis Kuck, 2016. "High-Dimensional $L_2$Boosting: Rate of Convergence," Papers 1602.08927, arXiv.org, revised Jul 2022.
    45. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    46. Krüger, Jens J. & Rhiel, Mathias, 2016. "Determinants of ICT infrastructure: A cross-country statistical analysis," Darmstadt Discussion Papers in Economics 228, Darmstadt University of Technology, Department of Law and Economics.
    47. Anders Bredahl Kock & Haihan Tang, 2014. "Inference in High-dimensional Dynamic Panel Data Models," CREATES Research Papers 2014-58, Department of Economics and Business Economics, Aarhus University.
    48. Julián Caballero, 2020. "Corporate dollar debt and depreciations: all's well that ends well?," BIS Working Papers 879, Bank for International Settlements.
    49. Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E & Wiemann, Thomas, 2023. "ddml: Double/Debiased Machine Learning in Stata," IZA Discussion Papers 15963, IZA Network @ LISER.
    50. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    51. Jun Li & Serguei Netessine & Sergei Koulayev, 2018. "Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space," Management Science, INFORMS, vol. 64(9), pages 4118-4136, September.
    52. Hao Hao & Bai Huang & Tae-Hwy Lee, 2022. "Model Averaging Estimation of Panel Data Models with Many Instruments and Boosting," Working Papers 202212, University of California at Riverside, Department of Economics.
    53. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    54. Yicheng Wei & Junzo Watada & Zijin Wang, 2025. "Topology Unveiled: A New Horizon for Economic and Financial Modeling," Mathematics, MDPI, vol. 13(2), pages 1-34, January.
    55. Godzinski, Alexandre & Suarez Castillo, Milena, 2021. "Disentangling the effects of air pollutants with many instruments," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).
    56. Christian Hansen & Yuan Liao, 2016. "The Factor-Lasso and K-Step Bootstrap Approach for Inference in High-Dimensional Economic Applications," Papers 1611.09420, arXiv.org, revised Dec 2016.
    57. Nicolas Apfel, 2019. "Relaxing the Exclusion Restriction in Shift-Share Instrumental Variable Estimation," Papers 1907.00222, arXiv.org, revised Jul 2022.
    58. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    59. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    60. Anthony Bald & Eric Chyn & Justine S. Hastings & Margarita Machelett, 2019. "The Causal Impact of Removing Children from Abusive and Neglectful Homes," NBER Working Papers 25419, National Bureau of Economic Research, Inc.
    61. Nebojša Stojčić & Matija Matić, 2023. "A Journey Toward Global Value Chain Upgrading: Exploring the Transition from Backward to Forward Integration," EFZG Working Papers Series 2308, Faculty of Economics and Business, University of Zagreb.
    62. Benjamin G. Hyman, 2022. "Can Displaced Labor Be Retrained? Evidence from Quasi-Random Assignment to Trade Adjustment Assistance," Working Papers 22-05, Center for Economic Studies, U.S. Census Bureau.
    63. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019. "lassopack: Model selection and prediction with regularized regression in Stata," Papers 1901.05397, arXiv.org.
    64. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2014. "Inference in High Dimensional Panel Models with an Application to Gun Control," Papers 1411.6507, arXiv.org.
    65. Alexandre Belloni & Victor Chernozhukov, 2011. "High Dimensional Sparse Econometric Models: An Introduction," Papers 1106.5242, arXiv.org, revised Sep 2011.
    66. Brian Quistorff & Gentry Johnson, 2020. "Machine Learning for Experimental Design: Methods for Improved Blocking," Papers 2010.15966, arXiv.org.
    67. Hansen, Christian & Kozbur, Damian, 2014. "Instrumental variables estimation with many weak instruments using regularized JIVE," Journal of Econometrics, Elsevier, vol. 182(2), pages 290-308.
    68. Liu, Chu-An & Tao, Jing, 2016. "Model selection and model averaging in nonparametric instrumental variables models," MPRA Paper 69492, University Library of Munich, Germany.
    69. Kanaya, Shin & Taylor, Luke, 2020. "Type I and Type II Error Probabilities in the Courtroom," MPRA Paper 100217, University Library of Munich, Germany.
    70. Damian Kozbur, 2015. "Testing-Based Forward Model Selection," ECON - Working Papers 283, Department of Economics - University of Zurich, revised Apr 2018.
    71. Taisuke Otsu & Myung Hwan Seo, 2014. "Asymptotics for maximum score method under general conditions," STICERD - Econometrics Paper Series 571, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    72. Alex Coad & Stjepan Srhoj, 2020. "Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms," Small Business Economics, Springer, vol. 55(3), pages 541-565, October.
    73. Bom, Judith & Bakx, Pieter & Schut, Frederik & van Doorslaer, Eddy, 2019. "Health effects of caring for and about parents and spouses," The Journal of the Economics of Ageing, Elsevier, vol. 14(C).
    74. Chi, Yeguang & El-Jahel, Lina & Vu, Thanh, 2025. "Media emotion intensity and commodity futures pricing," Journal of Commodity Markets, Elsevier, vol. 37(C).
    75. Härdle, Wolfgang Karl & Chen, Shi & Liang, Chong & Schienle, Melanie, 2018. "Time-varying Limit Order Book Networks," IRTG 1792 Discussion Papers 2018-016, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    76. Manu Navjeevan, 2023. "An Identification and Dimensionality Robust Test for Instrumental Variables Models," Papers 2311.14892, arXiv.org, revised Dec 2024.
    77. Khai Xiang Chiong & Matthew Shum, 2019. "Random Projection Estimation of Discrete-Choice Models with Large Choice Sets," Management Science, INFORMS, vol. 65(1), pages 256-271, January.
    78. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2014. "Valid post-selection inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP53/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    79. Fišera, Boris & Horváth, Roman, 2022. "Are exchange rates less important for trade in a more globalized world? Evidence for the new EU members," Economic Systems, Elsevier, vol. 46(1).
    80. Michael Zimmert, 2018. "Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding," Papers 1809.01643, arXiv.org, revised Aug 2020.
    81. Sauvenier, Mathieu & Van Bellegem, Sébastien, 2023. "Direction Identification and Minimax Estimation by Generalized Eigenvalue Problem in High Dimensional Sparse Regression," LIDAM Discussion Papers CORE 2023005, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    82. Alessandro V. M. Oliveira & Bruno F. Oliveira & Moises D. Vassallo, 2024. "Airport service quality perception and flight delays: examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys," Papers 2401.02139, arXiv.org.
    83. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    84. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.
    85. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    86. Meijer, Erik & Spierdijk, Laura & Wansbeek, Tom, 2017. "Consistent estimation of linear panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 200(2), pages 169-180.
    87. Barrera-Osorio, Felipe & Gertler,Paul J. & Nakajima,Nozomi & Patrinos,Harry Anthony, 2020. "Promoting Parental Involvement in Schools : Evidence from Two Randomized Experiments," Policy Research Working Paper Series 9462, The World Bank.
    88. Anthony Strittmatter & Conny Wunsch, 2021. "The Gender Pay Gap Revisited with Big Data: Do Methodological Choices Matter?," Papers 2102.09207, arXiv.org, revised Feb 2021.
    89. Szabó-Morvai Ágnes & Hubert János Kiss, 2020. "Locus of control and Human Capital Investment Decisions: The Role of Effort, Parental Preferences and Financial Constraints," CERS-IE WORKING PAPERS 2055, Institute of Economics, Centre for Economic and Regional Studies.
    90. Hao Hao & Tae-Hwy Lee, 2025. "Boosting GMM with Many Instruments When Some Are Invalid and/or Irrelevant," Working Papers 202504, University of California at Riverside, Department of Economics.
    91. Sylvain Barde & Rowan Cherodian & Guy Tchuente, 2024. "Moran's I 2-Stage Lasso: for Models with Spatial Correlation and Endogenous Variables," Papers 2404.02584, arXiv.org.
    92. Colin F. Camerer & Gideon Nave & Alec Smith, 2019. "Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning," Management Science, INFORMS, vol. 65(4), pages 1867-1890, April.
    93. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    94. Santos, Luca J. & Oliveira, Alessandro V.M. & Aldrighi, Dante Mendes, 2021. "Testing the differentiated impact of the COVID-19 pandemic on air travel demand considering social inclusion," Journal of Air Transport Management, Elsevier, vol. 94(C).
    95. Chaohua Dong & Jiti Gao & Oliver Linton, 2018. "High dimensional semiparametric moment restriction models," CeMMAP working papers CWP69/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    96. Zhaonan Qu & Yongchan Kwon, 2024. "Distributionally Robust Instrumental Variables Estimation," Papers 2410.15634, arXiv.org, revised Dec 2024.
    97. Corinne Emmenegger & Peter Bühlmann, 2023. "Plug‐in machine learning for partially linear mixed‐effects models with repeated measurements," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(4), pages 1553-1567, December.
    98. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    99. Qu Feng & Sombut Jaidee & Wenjie Wang, 2025. "Robust Inference with High-Dimensional Instruments," Papers 2506.23834, arXiv.org.
    100. Qingliang Fan & Yaqian Wu, 2020. "Endogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments," Papers 2006.14998, arXiv.org.
    101. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
    102. Ash, Elliott & Chen, Daniel L. & Lu, Wei, 2018. "Motivated Reasoning in the Field: Partisanship in Precedent, Prose, Vote, and Retirement in U.S. Circuit Courts, 1800-2013," TSE Working Papers 18-976, Toulouse School of Economics (TSE).
    103. Guy Tchuente, 2021. "A Note on the Topology of the First Stage of 2SLS with Many Instruments," Papers 2106.15003, arXiv.org.
    104. Lucas Z. Zhang, 2025. "Approximate Sparsity Class and Minimax Estimation," Papers 2508.09278, arXiv.org.
    105. Alastair R. Hall, 2015. "Econometricians Have Their Moments: GMM at 32," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 1-24, June.
    106. Everding, Jakob & Marcus, Jan, 2019. "The effect of unemployment on the smoking behavior of couples," hche Research Papers 17, University of Hamburg, Hamburg Center for Health Economics (hche).
    107. Mehmet Caner & Anders Bredahl Kock, 2013. "Oracle Inequalities for Convex Loss Functions with Non-Linear Targets," CREATES Research Papers 2013-51, Department of Economics and Business Economics, Aarhus University.
    108. Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
    109. Michela Carlana & Eliana La Ferrara, 2025. "Apart but Connected: Online Tutoring, Cognitive Outcomes, and Soft Skills," American Economic Review, American Economic Association, vol. 115(10), pages 3487-3513, October.
    110. Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting the Hodrick-Prescott Filter," Cowles Foundation Discussion Papers 2192, Cowles Foundation for Research in Economics, Yale University.
    111. Kolesár, Michal, 2018. "Minimum distance approach to inference with many instruments," Journal of Econometrics, Elsevier, vol. 204(1), pages 86-100.
    112. Marcelo C. Medeiros & Eduardo F. Mendes, 2012. "Estimating High-Dimensional Time Series Models," CREATES Research Papers 2012-37, Department of Economics and Business Economics, Aarhus University.
    113. Chen, Shi & Härdle, Wolfgang & Schienle, Melanie, 2021. "High-dimensional statistical learning techniques for time-varying limit order book networks," IRTG 1792 Discussion Papers 2021-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    114. Robert Brooks & Brandon N. Cline & Pavel Teterin & Yu You, 2022. "The information in global interest rate futures contracts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(6), pages 1135-1166, June.
    115. Helmut Wasserbacher & Martin Spindler, 2021. "Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls," Papers 2107.04851, arXiv.org.
    116. Stefan Seifert & Marica Valente, 2018. "An Offer that you Can't Refuse? Agrimafias and Migrant Labor on Vineyards in Southern Italy," Discussion Papers of DIW Berlin 1735, DIW Berlin, German Institute for Economic Research.
    117. Lonjezo Sithole, 2024. "A Locally Robust Semiparametric Approach to Examiner IV Designs," Papers 2404.19144, arXiv.org.
    118. Michael Danquah & Abdul Malik Iddrisu & Ernest Owusu Boakye & Solomon Owusu, 2021. "Do gender wage differences within households influence women's empowerment and welfare?: Evidence from Ghana," WIDER Working Paper Series wp-2021-40, World Institute for Development Economic Research (UNU-WIDER).
    119. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
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    1. Adam Nowak & Patrick Smith, 2015. "Textual Analysis in Real Estate," Working Papers 15-34, Department of Economics, West Virginia University.
    2. Heng Lian, 2012. "Variable selection in high-dimensional partly linear additive models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 825-839, December.

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    2. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.
    3. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    5. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    6. Ismael Mourifie & Yuanyuan Wan, 2014. "Testing Local Average Treatment Effect Assumptions," Working Papers tecipa-514, University of Toronto, Department of Economics.
    7. Xinrui Ruan & Xinwei Ma & Yingfei Wang & Waverly Wei & Jingshen Wang, 2025. "Can language models boost the power of randomized experiments without statistical bias?," Papers 2510.05545, arXiv.org, revised Dec 2025.
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    9. Thomas Carr & Toru Kitagawa, 2021. "Testing Instrument Validity with Covariates," Papers 2112.08092, arXiv.org, revised Sep 2023.
    10. Lafférs, Lukáš & Mellace, Giovanni, 2020. "Identification of the average treatment effect when SUTVA is violated," Discussion Papers on Economics 3/2020, University of Southern Denmark, Department of Economics.
    11. Laffers, Lukas, 2013. "Identification in Models with Discrete Variables," Discussion Paper Series in Economics 1/2013, Norwegian School of Economics, Department of Economics.
    12. Ben Deaner & Soonwoo Kwon, 2025. "Extrapolation in Regression Discontinuity Design Using Comonotonicity," Papers 2507.00289, arXiv.org.
    13. Federico A. Bugni & Ivan A. Canay & Xiaoxia Shi, 2014. "Inference for functions of partially identified parameters in moment inequality models," CeMMAP working papers CWP05/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Li, Jia & Liao, Zhipeng & Zhou, Wenyu, 2025. "A general test for functional inequalities," Journal of Econometrics, Elsevier, vol. 251(C).
    15. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," CeMMAP working papers CWP51/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    176. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    182. D'Haultfoeuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2014. "Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap," IZA Discussion Papers 8256, IZA Network @ LISER.
    183. Tomiyama, Hideyuki & Otsu, Taisuke, 2022. "Inference on incomplete information games with multi-dimensional actions," Economics Letters, Elsevier, vol. 215(C).
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    185. Wooyoung Kim & Koohyun Kwon & Soonwoo Kwon & Sokbae (Simon) Lee, 2014. "The identification power of smoothness assumptions in models with counterfactual outcomes," CeMMAP working papers CWP17/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    186. Andrew Chesher & Adam Rosen, 2012. "Simultaneous equations for discrete outcomes: coherence, completeness, and identification," CeMMAP working papers CWP21/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    187. Ian Crawford & Matthew Polisson, 2015. "Demand analysis with partially observed prices," IFS Working Papers W15/16, Institute for Fiscal Studies.
    188. Bhattacharya, Jay & Shaikh, Azeem M. & Vytlacil, Edward, 2012. "Treatment effect bounds: An application to Swan–Ganz catheterization," Journal of Econometrics, Elsevier, vol. 168(2), pages 223-243.
    189. Cosaert, S. & Demuynck, T., 2014. "Nonparametric welfare and demand analysis with unobserved individual heterogeneity," Research Memorandum 010, Maastricht University, Graduate School of Business and Economics (GSBE).
    190. Joachim Freyberger & Joel L. Horowitz, 2013. "Identification and shape restrictions in nonparametric instrumental variables estimation," CeMMAP working papers CWP31/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    191. Dimitrios Nikolaou, 2022. "Identifying the effects of bullying victimization on schooling," Contemporary Economic Policy, Western Economic Association International, vol. 40(1), pages 162-189, January.
    192. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    193. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    194. Matthew J Tudball & Rachael A Hughes & Kate Tilling & Jack Bowden & Qingyuan Zhao, 2023. "Sample-constrained partial identification with application to selection bias," Biometrika, Biometrika Trust, vol. 110(2), pages 485-498.
    195. Chiburis, Richard C., 2010. "Semiparametric bounds on treatment effects," Journal of Econometrics, Elsevier, vol. 159(2), pages 267-275, December.
    196. Atsushi Inoue & Tong Li & Qi Xu, 2021. "Two Sample Unconditional Quantile Effect," Papers 2105.09445, arXiv.org.
    197. Fan Yang & Dylan S. Small, 2016. "Using post-outcome measurement information in censoring-by-death problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 299-318, January.
    198. Andrew Chesher & Adam Rosen, 2012. "An instrumental variable random coefficients model for binary outcomes," CeMMAP working papers CWP34/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    199. Eibich, Peter & Lorenti, Angelo & Mosca, Irene, 2022. "Does retirement affect voluntary work provision? Evidence from Europe and the U.S," Labour Economics, Elsevier, vol. 76(C).
    200. Fan, Yanqin & Park, Sang Soo, 2014. "Nonparametric inference for counterfactual means: Bias-correction, confidence sets, and weak IV," Journal of Econometrics, Elsevier, vol. 178(P1), pages 45-56.
    201. Lu, Jiannan, 2018. "On the partial identification of a new causal measure for ordinal outcomes," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 1-7.
    202. Isabel Mourifie & Marc Henry, 2011. "Set Inference in Latent Variables Models," CIRJE F-Series CIRJE-F-820, CIRJE, Faculty of Economics, University of Tokyo.
    203. Richard A Dunn, 2012. "Consistently bounding parameter values with one instrument and two endogenous explanatory variables," Economics Bulletin, AccessEcon, vol. 32(2), pages 1074-1081.
    204. Tsunao Okumura & Emiko Usui, 2014. "Concave‐monotone treatment response and monotone treatment selection: With an application to the returns to schooling," Quantitative Economics, Econometric Society, vol. 5, pages 175-194, March.
    205. Matthew A. Masten & Alexandre Poirier, 2018. "Salvaging Falsified Instrumental Variable Models," Papers 1812.11598, arXiv.org, revised Jan 2020.
    206. Lin, Haizhen & McCarthy, Ian M. & Richards, Michael, 2021. "Hospital Pricing Following Integration with Physician Practices," Journal of Health Economics, Elsevier, vol. 77(C).
    207. Clément de Chaisemartin & Diego Ciccia & Xavier D’Haultfoeuille & Felix Knau, 2024. "Two-way Fixed Effects and Differences-in-Differences in Heterogeneous Adoption Designs without Stayers," Working Papers 2025-01, Center for Research in Economics and Statistics.
    208. Andrew Chesher, 2011. "Semiparametric structural models of binary response: shape restrictions and partial identification," CeMMAP working papers CWP31/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    209. Peter Horvath & Jia Li & Zhipeng Liao & Andrew J. Patton, 2022. "A consistent specification test for dynamic quantile models," Quantitative Economics, Econometric Society, vol. 13(1), pages 125-151, January.
    210. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    211. Yuehao Bai & Shunzhuang Huang & Max Tabord-Meehan, 2024. "Sharp Testable Implications of Encouragement Designs," Papers 2411.09808, arXiv.org, revised Nov 2025.
    212. Wei, Waverly & Zhou, Yuqing & Zheng, Zeyu & Wang, Jingshen, 2024. "Inference on the best policies with many covariates," Journal of Econometrics, Elsevier, vol. 239(2).
    213. Matias D. Cattaneo & Max H. Farrell & Yingjie Feng, 2018. "Large Sample Properties of Partitioning-Based Series Estimators," Papers 1804.04916, arXiv.org, revised Jun 2019.
    214. Timothy B. Armstrong & Michal Koles�r, 2016. "Optimal Inference in a Class of Regression Models," Cowles Foundation Discussion Papers 2043R2, Cowles Foundation for Research in Economics, Yale University, revised Dec 2017.
    215. Dominic Coey & Bradley Larsen & Kane Sweeney, 2014. "The Bidder Exclusion Effect," NBER Working Papers 20523, National Bureau of Economic Research, Inc.
    216. Mikkel Plagborg-Møller & Christian K. Wolf, 2021. "Instrumental Variable Identification of Dynamic Variance Decompositions," Working Papers 2021-40, Princeton University. Economics Department..
    217. Obradović, Filip, 2024. "Measuring diagnostic test performance using imperfect reference tests: A partial identification approach," Journal of Econometrics, Elsevier, vol. 244(1).
    218. Zhu, Rong & Onur, Ilke, 2023. "Does retirement (really) increase informal caregiving? Quasi-experimental evidence from Australia," Journal of Health Economics, Elsevier, vol. 87(C).
    219. Sokbae Lee & Martin Weidner, 2021. "Bounding Treatment Effects by Pooling Limited Information across Observations," Papers 2111.05243, arXiv.org, revised Feb 2026.
    220. Pietro Emilio Spini, 2021. "Robustness, Heterogeneous Treatment Effects and Covariate Shifts," Papers 2112.09259, arXiv.org, revised Aug 2024.
    221. Victor Chernozhukov & Roberto Rigobon & Thomas M. Stoker, 2010. "Set identification and sensitivity analysis with Tobin regressors," Quantitative Economics, Econometric Society, vol. 1(2), pages 255-277, November.
    222. Tabri, Rami V., 2015. "Empirical Likelihood for Robust Poverty Comparisons," Working Papers 2015-02, University of Sydney, School of Economics, revised May 2015.
    223. Bradley Larsen, 2014. "The Efficiency of Real-World Bargaining: Evidence from Wholesale Used-Auto Auctions," NBER Working Papers 20431, National Bureau of Economic Research, Inc.
    224. Yoonseok Lee & Sung Jae Jun & Youngki Shin, 2014. "Treatment Effects with Unobserved Heterogeneity: A Set Identification Approach," Center for Policy Research Working Papers 169, Center for Policy Research, Maxwell School, Syracuse University.
    225. Daniel Millimet & Manan Roy, 2015. "Partial identification of the long-run causal effect of food security on child health," Empirical Economics, Springer, vol. 48(1), pages 83-141, February.
    226. Yuan Liao & Anna Simoni, 2016. "Bayesian Inference for Partially Identified Convex Models: Is it Valid for Frequentist Inference?," Departmental Working Papers 201607, Rutgers University, Department of Economics.
    227. Bei, Xinyue, 2024. "Local linearization based subvector inference in moment inequality models," Journal of Econometrics, Elsevier, vol. 238(1).
    228. Joachim Freyberger & Joel L. Horowitz, 2012. "Identification and shape restrictions in nonparametric instrumental variables estimation," CeMMAP working papers 15/12, Institute for Fiscal Studies.
    229. Sungwon Lee, 2024. "Partial identification and inference for conditional distributions of treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 107-127, January.
    230. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

  133. Victor Chernozhukov & Ivan Fernandez-Val, 2009. "Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks," Papers 0912.5013, arXiv.org.

    Cited by:

    1. Becker, Sascha O. & Hvide, Hans K., 2013. "Do entrepreneurs matter?," CEPR Discussion Papers 9295, C.E.P.R. Discussion Papers.
    2. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    3. Yuya Sasaki & Yulong Wang, 2024. "Extreme Quantile Treatment Effects under Endogeneity: Evaluating Policy Effects for the Most Vulnerable Individuals," Papers 2409.03979, arXiv.org.
    4. Calluzzo, Paul & Dong, Gang Nathan, 2015. "Has the financial system become safer after the crisis? The changing nature of financial institution risk," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 233-248.
    5. Hou, Yanxi & Leng, Xuan & Peng, Liang & Zhou, Yinggang, 2024. "Panel quantile regression for extreme risk," Journal of Econometrics, Elsevier, vol. 240(1).
    6. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    7. Martina Pons, 2022. "The impact of air pollution on birthweight: evidence from grouped quantile regression," Empirical Economics, Springer, vol. 62(1), pages 279-296, January.
    8. Daouia, Abdelaati & Stupfler, Gilles & Usseglio-Carleve, Antoine, 2022. "Inference for extremal regression with dependent heavy-tailed data," TSE Working Papers 22-1324, Toulouse School of Economics (TSE), revised 29 Aug 2023.
    9. D’Haultfœuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2018. "Extremal quantile regressions for selection models and the black–white wage gap," Journal of Econometrics, Elsevier, vol. 203(1), pages 129-142.
    10. Wang, Yulong & Xiao, Zhijie, 2022. "Estimation and inference about tail features with tail censored data," Journal of Econometrics, Elsevier, vol. 230(2), pages 363-387.
    11. Martínez-Iriarte, Julián & Montes-Rojas, Gabriel & Sun, Yixiao, 2024. "Unconditional effects of general policy interventions," Journal of Econometrics, Elsevier, vol. 238(2).
    12. Valentina Corradi & Daniel Gutknecht, 2019. "Testing for Quantile Sample Selection," Papers 1907.07412, arXiv.org, revised Jan 2021.
    13. Grigory Franguridi & Bulat Gafarov & Kaspar Wüthrich, 2021. "Conditional Quantile Estimators: A Small Sample Theory," CESifo Working Paper Series 9046, CESifo.
    14. David M. Kaplan, 2013. "Improved Quantile Inference Via Fixed-Smoothing Asymptotics And Edgeworth Expansion," Working Papers 1313, Department of Economics, University of Missouri.
    15. Daisuke Kurisu & Taisuke Otsu, 2021. "Nonparametric inference for extremal conditional quantiles," STICERD - Econometrics Paper Series 616, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    16. Vladislav Morozov, 2022. "Inference on Extreme Quantiles of Unobserved Individual Heterogeneity," Papers 2210.08524, arXiv.org, revised Jun 2025.
    17. Marilena Furno & Francesco Caracciolo, 2020. "Multi-valued Double Robust quantile treatment effect," Empirical Economics, Springer, vol. 58(5), pages 2545-2571, May.
    18. Michal Franta & Jan Vlcek, 2024. "Wage-Price Spirals: A Risk-Based Approach," Working Papers 2024/1, Czech National Bank, Research and Statistics Department.
    19. Kurisu, Daisuke & Otsu, Taisuke, 2023. "Subsampling inference for nonparametric extremal conditional quantiles," LSE Research Online Documents on Economics 120365, London School of Economics and Political Science, LSE Library.
    20. Difang Huang & Jiti Gao & Tatsushi Oka, 2022. "Semiparametric Single-Index Estimation for Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 10/22, Monash University, Department of Econometrics and Business Statistics.
    21. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
    22. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    23. Michal Franta & Jan Vlcek, 2025. "Inflation at Risk: The Czech Case," Working Papers 2025/8, Czech National Bank, Research and Statistics Department.
    24. Yuya Sasaki & Yulong Wang, 2020. "Testing Finite Moment Conditions for the Consistency and the Root-N Asymptotic Normality of the GMM and M Estimators," Papers 2006.02541, arXiv.org, revised Sep 2020.
    25. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    26. Fan, Ye & Lin, Nan, 2025. "Sequential quantile regression for stream data by least squares," Journal of Econometrics, Elsevier, vol. 249(PA).
    27. Udo Brixy & Martin Murmann, 2025. "Hiring opportunities for new firms and the business cycle," Small Business Economics, Springer, vol. 64(3), pages 1387-1413, March.
    28. Yuya Sasaki & Yulong Wang, 2019. "Fixed-k Inference for Conditional Extremal Quantiles," Papers 1909.00294, arXiv.org, revised Jul 2020.
    29. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    30. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly, 2022. "Fast algorithms for the quantile regression process," Empirical Economics, Springer, vol. 62(1), pages 7-33, January.
    31. Wei‐han Liu, 2020. "Are Gold and Government Bond Safe‐Haven Assets? An Extremal Quantile Regression Analysis," International Review of Finance, International Review of Finance Ltd., vol. 20(2), pages 451-483, June.
    32. Andres Sagner, 2020. "High Dimensional Quantile Factor Analysis," Working Papers Central Bank of Chile 886, Central Bank of Chile.
    33. Grimsby, Gjermund, 2018. "Partly risky, partly solid – Performance study of public innovation loans," Research Policy, Elsevier, vol. 47(7), pages 1344-1365.
    34. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2015. "VAR for VaR: Measuring tail dependence using multivariate regression quantiles," Journal of Econometrics, Elsevier, vol. 187(1), pages 169-188.
    35. Franguridi, Grigory & Gafarov, Bulat & Wüthrich, Kaspar, 2025. "Bias correction for quantile regression estimators," Journal of Econometrics, Elsevier, vol. 251(C).
    36. Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2022. "Weighted-average quantile regression," Papers 2203.03032, arXiv.org.
    37. D'Haultfoeuille, Xavier & Maurel, Arnaud & Qiu, Xiaoyun & Zhang, Yichong, 2019. "Estimating Selection Models without Instrument with Stata," IZA Discussion Papers 12486, IZA Network @ LISER.
    38. Oliver Himmler, 2009. "The Effects of School Competition on Academic Achievement and Grading Standards," CESifo Working Paper Series 2676, CESifo.
    39. David M. Kaplan, 2014. "Nonparametric Inference on Quantile Marginal Effects," Working Papers 1413, Department of Economics, University of Missouri.
    40. Victor Chernozhukov & Iv'an Fern'andez-Val & Jonas Meier & Aico van Vuuren & Francis Vella, 2024. "Conditional Rank-Rank Regression," Papers 2407.06387, arXiv.org, revised Oct 2025.
    41. Xiong, Qizhou, 2015. "Censored Fractional Response Model: Estimating Heterogeneous Relative Risk Aversion of European Households," IWH Discussion Papers 11/2015, Halle Institute for Economic Research (IWH).
    42. Aida Caldera Sánchez & Oliver Röhn, 2016. "How do policies influence GDP tail risks?," OECD Economics Department Working Papers 1339, OECD Publishing.
    43. Sulkhan Chavleishvili & Simone Manganelli, 2024. "Forecasting and stress testing with quantile vector autoregression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 66-85, January.
    44. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2015. "IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade," NBER Working Papers 21033, National Bureau of Economic Research, Inc.
    45. Takuma Yoshida, 2021. "Additive models for extremal quantile regression with Pareto-type distributions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 103-134, March.
    46. He, Fengyang & Wang, Huixia Judy & Zhou, Yuejin, 2022. "Extremal quantile autoregression for heavy-tailed time series," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).

  134. Victor Chernozhukov & Ivan Fernandez-Val & Jinyong Hahn & Whitney Newey, 2009. "Identification and Estimation of Marginal Effects in Nonlinear Panel Models," Boston University - Department of Economics - Working Papers Series wp2009-b, Boston University - Department of Economics.

    Cited by:

    1. Martin Browning & Jesus M. Carro, 2009. "Dynamic binary outcome models with maximal heterogeneity," Economics Series Working Papers 426, University of Oxford, Department of Economics.
    2. Andrei Markevich & Ekaterina Zhuravskaya, 2009. "Career Concerns in a Political Hierarchy: A Case of Regional Leaders in Soviet Russia," Working Papers w0040, Center for Economic and Financial Research (CEFIR).
    3. Ali Fakih, 2014. "Vacation Leave, Work Hours and Wages: New Evidence from Linked Employer-Employee Data," CIRANO Working Papers 2014s-40, CIRANO.
    4. Hoderlein, Stefan & White, Halbert, 2012. "Nonparametric identification in nonseparable panel data models with generalized fixed effects," Journal of Econometrics, Elsevier, vol. 168(2), pages 300-314.
    5. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Identification of Panel Data Models with Endogenous Censoring," MPRA Paper 30373, University Library of Munich, Germany.
    6. Philip Kostov & Julie Le Gallo, 2015. "Convergence: A Story of Quantiles and Spillovers," Kyklos, Wiley Blackwell, vol. 68(4), pages 552-576, November.
    7. Markevich, Andrei & Zhuravskaya, Ekaterina, 2011. "M-form hierarchy with poorly-diversified divisions: A case of Khrushchev's reform in Soviet Russia," Journal of Public Economics, Elsevier, vol. 95(11), pages 1550-1560.
    8. Manuel Arellano & Stéphane Bonhomme, 2009. "Identifying distributional characteristics in random coefficients panel data models," CeMMAP working papers CWP22/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Ciani, Emanuele, 2012. "Informal adult care and caregivers' employment in Europe," Labour Economics, Elsevier, vol. 19(2), pages 155-164.
    10. Bryan S. Graham & James Powell, 2008. "Identification and Estimation of 'Irregular' Correlated Random Coefficient Models," NBER Working Papers 14469, National Bureau of Economic Research, Inc.
    11. Hyland, Marie & Bertsch, Valentin, 2018. "The Role of Community Involvement Mechanisms in Reducing Resistance to Energy Infrastructure Development," Ecological Economics, Elsevier, vol. 146(C), pages 447-474.
    12. Kyungchul Song, 2009. "Point Decisions for Interval-Identified Parameters," PIER Working Paper Archive 09-036, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    13. Bertsch, Valentin & Hyland, Marie & Mahony, Michael, 2017. "What drives people's opinions of electricity infrastructure? Empirical evidence from Ireland," Energy Policy, Elsevier, vol. 106(C), pages 472-497.
    14. Amaresh Tiwari & Franz Palm, 2011. "Nonlinear Panel Data Models with Expected a Posteriori Values of Correlated Random Effects," CREPP Working Papers 1113, Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège.
    15. Shiu, Ji-Liang & Hu, Yingyao, 2013. "Identification and estimation of nonlinear dynamic panel data models with unobserved covariates," Journal of Econometrics, Elsevier, vol. 175(2), pages 116-131.
    16. Rosen, Adam M., 2012. "Set identification via quantile restrictions in short panels," Journal of Econometrics, Elsevier, vol. 166(1), pages 127-137.
    17. Lewbel, Arthur & Yang, Thomas Tao, 2016. "Identifying the average treatment effect in ordered treatment models without unconfoundedness," Journal of Econometrics, Elsevier, vol. 195(1), pages 1-22.
    18. Bester, C. Alan & Hansen, Christian B., 2016. "Grouped effects estimators in fixed effects models," Journal of Econometrics, Elsevier, vol. 190(1), pages 197-208.
    19. Hu, Yingyao, 2017. "The Econometrics of Unobservables -- Latent Variable and Measurement Error Models and Their Applications in Empirical Industrial Organization and Labor Economics [The Econometrics of Unobservables]," Economics Working Paper Archive 64578, The Johns Hopkins University,Department of Economics, revised 2021.

  135. Alexandre Belloni & Victor Chernozhukov, 2009. "L1-Penalized Quantile Regression in High-Dimensional Sparse Models," Papers 0904.2931, arXiv.org, revised Sep 2019.

    Cited by:

    1. Fan, Zengyan & Lian, Heng, 2018. "Quantile regression for additive coefficient models in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 164(C), pages 54-64.
    2. Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
    3. Kenechukwu E. Anadu & James Bohn & Lina Lu & Matthew Pritsker & Andrei Zlate, 2019. "Reach for Yield by U.S. Public Pension Funds," Finance and Economics Discussion Series 2019-048, Board of Governors of the Federal Reserve System (U.S.).
    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    5. Fang, Libing & Sun, Boyang & Li, Huijing & Yu, Honghai, 2018. "Systemic risk network of Chinese financial institutions," Emerging Markets Review, Elsevier, vol. 35(C), pages 190-206.
    6. Weihua Zhao & Jianbo Li & Heng Lian, 2018. "Adaptive varying-coefficient linear quantile model: a profiled estimating equations approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 553-582, June.
    7. Sven Klaassen & Jannis Kueck & Martin Spindler, 2017. "Transformation Models in High-Dimensions," Papers 1712.07364, arXiv.org.
    8. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers CWP28/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Härdle, Wolfgang Karl & Ritov, Ya'acov & Song, Song, 2010. "Partial linear quantile regression and bootstrap confidence bands," SFB 649 Discussion Papers 2010-002, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    10. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Iv'an Fern'andez-Val, 2011. "Conditional Quantile Processes based on Series or Many Regressors," Papers 1105.6154, arXiv.org, revised Aug 2018.
    11. Pierre Alquier & Vincent Cottet & Guillaume Lecué, 2017. "Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions," Working Papers 2017-30, Center for Research in Economics and Statistics.
    12. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2017. "Quantile graphical models: prediction and conditional independence with applications to systemic risk," CeMMAP working papers CWP54/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    134. Facundo Arga~naraz & Juan Carlos Escanciano, 2025. "Debiased Machine Learning for Unobserved Heterogeneity: High-Dimensional Panels and Measurement Error Models," Papers 2507.13788, arXiv.org.
    135. Rodolphe Desbordes & Markus Eberhardt, 2019. "Gravity," Discussion Papers 2019-02, University of Nottingham, GEP.
    136. Fu Ouyang & Thomas Tao Yang, 2020. "Semiparametric Estimation of Dynamic Binary Choice Panel Data Models," ANU Working Papers in Economics and Econometrics 2020-671, Australian National University, College of Business and Economics, School of Economics.
    137. Michael Bates & Seolah Kim, 2024. "Estimating the price elasticity of gasoline demand in correlated random coefficient models with endogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(4), pages 679-696, June.
    138. Harding, Matthew & Lamarche, Carlos, 2014. "Estimating and testing a quantile regression model with interactive effects," Journal of Econometrics, Elsevier, vol. 178(P1), pages 101-113.
    139. Fan, Yanqin & Shi, Xuetao, 2023. "Wald, QLR, and score tests when parameters are subject to linear inequality constraints," Journal of Econometrics, Elsevier, vol. 235(2), pages 2005-2026.
    140. Sherrilyn Billger & Carlos Lamarche, 2015. "A panel data quantile regression analysis of the immigrant earnings distribution in the United Kingdom and United States," Empirical Economics, Springer, vol. 49(2), pages 705-750, September.
    141. Van Gestel, R.; Müller, T.; Bosmans, J.;, 2017. "Learning from failure in healthcare: dynamic panel evidence of a physician shock effect," Health, Econometrics and Data Group (HEDG) Working Papers 17/24, HEDG, c/o Department of Economics, University of York.
    142. Fu Ouyang & Thomas Tao Yang, 2020. "Semiparametric Estimation of Dynamic Binary Choice Panel Data Models," Discussion Papers Series 626, School of Economics, University of Queensland, Australia.
    143. Michelle Marcus & Pedro H. C. Sant'Anna, 2020. "The role of parallel trends in event study settings: An application to environmental economics," Papers 2009.01963, arXiv.org.
    144. Stéphane Bonhomme & Kevin Dano & Bryan S. Graham, 2025. "Moment restrictions for nonlinear panel data models with feedback," CeMMAP working papers 12/25, Institute for Fiscal Studies.
    145. Zhang, Zhao & Zhang, Feipeng & Ma, Caoyuan, 2024. "Does carbon emission trading scheme inhibit corporate executives' pursuit of excess compensation? Evidence from a quasi-natural experiment in China," Energy Economics, Elsevier, vol. 139(C).
    146. Andrew Chesher & Adam M. Rosen & Yuanqi Zhang, 2024. "Robust Analysis of Short Panels," Papers 2401.06611, arXiv.org.
    147. Yoonseok Lee & Sung Jae Jun & Youngki Shin, 2014. "Treatment Effects with Unobserved Heterogeneity: A Set Identification Approach," Center for Policy Research Working Papers 169, Center for Policy Research, Maxwell School, Syracuse University.
    148. Brantly Callaway, 2022. "Difference-in-Differences for Policy Evaluation," Papers 2203.15646, arXiv.org.
    149. Sloczynski, Tymon, 2015. "New Evidence on Linear Regression and Treatment Effect Heterogeneity," IZA Discussion Papers 9491, IZA Network @ LISER.
    150. Ali Aghamohammadi, 2018. "Bayesian analysis of dynamic panel data by penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 91-108, March.
    151. Sylvia Klosin, 2024. "Dynamic Biases of Static Panel Data Estimators," Papers 2410.16112, arXiv.org.
    152. Michael P. Leung & Hyungsik Roger Moon, 2019. "Normal Approximation in Large Network Models," Papers 1904.11060, arXiv.org, revised Aug 2025.
    153. Geert Dhaene & Martin Weidner, 2023. "Approximate Functional Differencing," Papers 2301.13736, arXiv.org, revised May 2023.
    154. Irene Botosaru & Chris Muris, 2022. "Identification of time-varying counterfactual parameters in nonlinear panel models," Papers 2212.09193, arXiv.org, revised Nov 2023.

  137. Alexandre Belloni & Victor Chernozhukov, 2009. "Posterior Inference in Curved Exponential Families under Increasing Dimensions," Papers 0904.3132, arXiv.org, revised Apr 2014.

    Cited by:

    1. Gallant, A. Ronald & Hong, Han & Leung, Michael P. & Li, Jessie, 2022. "Constrained estimation using penalization and MCMC," Journal of Econometrics, Elsevier, vol. 228(1), pages 85-106.

  138. Daron Acemoglu & Victor Chernozhukov & Muhamet Yildiz, 2009. "Fragility of Asymptotic Agreement under Bayesian Learning," Levine's Working Paper Archive 814577000000000139, David K. Levine.

    Cited by:

    1. Antonio Jiménez-Martínez, 2014. "A model of belief influence in large social networks," Working Papers DTE 572, CIDE, División de Economía.
    2. Jean-Pierre Beno t & Juan Dubra, 2018. "When do populations polarize? An explanation," Documentos de Trabajo/Working Papers 1801, Facultad de Ciencias Empresariales y Economia. Universidad de Montevideo..
    3. Piotr Evdokimov & Umberto Garfagnini, 2022. "Higher-order learning," Experimental Economics, Springer;Economic Science Association, vol. 25(4), pages 1234-1266, September.
    4. Kenneth J. Singleton, 2021. "Presidential Address: How Much “Rationality” Is There in Bond‐Market Risk Premiums?," Journal of Finance, American Finance Association, vol. 76(4), pages 1611-1654, August.
    5. Levy, Gilat & Razin, Ronny, 2021. "A maximum likelihood approach to combining forecasts," LSE Research Online Documents on Economics 104116, London School of Economics and Political Science, LSE Library.
    6. Wei Xiong, 2013. "Bubbles, Crises, and Heterogeneous Beliefs," NBER Working Papers 18905, National Bureau of Economic Research, Inc.
    7. Mira Frick & Ryota Iijima & Yuhta Ishii, 2021. "Learning Efficiency of Multi-Agent Information Structures," Cowles Foundation Discussion Papers 2299R, Cowles Foundation for Research in Economics, Yale University, revised Dec 2021.
    8. Alice Hsiaw & Ing-Haw Cheng, 2016. "Distrust in Experts and the Origins of Disagreement," Working Papers 110R3, Brandeis University, Department of Economics and International Business School, revised Mar 2018.
    9. Rajiv Sethi & Muhamet Yildiz, 2009. "Public Disagreement," Economics Working Papers 0089, Institute for Advanced Study, School of Social Science.
    10. Gabriel Martinez & Nicholas H. Tenev, 2020. "Optimal Echo Chambers," Papers 2010.01249, arXiv.org, revised Jun 2025.
    11. Hestermann, Nina & Le Yaouanq, Yves, 2018. "It\'s not my Fault! Self-Confidence and Experimentation," Rationality and Competition Discussion Paper Series 124, CRC TRR 190 Rationality and Competition.
    12. Mandler, Michael, 2012. "The fragility of information aggregation in large elections," Games and Economic Behavior, Elsevier, vol. 74(1), pages 257-268.
    13. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    14. Prat, Andrea & Montiel Olea , José Luis & Ortoleva, Pietro & Pai, Mallesh, 2019. "Competing Models," CEPR Discussion Papers 14066, C.E.P.R. Discussion Papers.
      • Jose Luis Montiel Olea & Pietro Ortoleva & Mallesh M Pai & Andrea Prat, 2019. "Competing Models," Papers 1907.03809, arXiv.org, revised Nov 2021.
    15. George J. Mailath & Larry Samuelson, 2019. "Learning under Diverse World Views: Model-Based Inference," PIER Working Paper Archive 19-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    16. Leung, B. T. K., 2020. "Learning in a Small/Big World," Cambridge Working Papers in Economics 2085, Faculty of Economics, University of Cambridge.
    17. Ing-Haw Cheng & Alice Hsiaw, 2023. "Bayesian Doublespeak," Working Papers 135, Brandeis University, Department of Economics and International Business School.
    18. S. Nageeb Ali, 2009. "Learning Self-Control," Levine's Working Paper Archive 814577000000000384, David K. Levine.
    19. de la Torre, Augusto & Ize, Alain, 2013. "The foundations of macroprudential regulation : a conceptual roadmap," Policy Research Working Paper Series 6575, The World Bank.
    20. Le Yaouanq, Yves, 2018. "A Model of Ideological Thinking," Rationality and Competition Discussion Paper Series 85, CRC TRR 190 Rationality and Competition.
    21. Kondor, Péter, 2011. "The more we know on the fundamental, the less we agree on the price," CEPR Discussion Papers 8455, C.E.P.R. Discussion Papers.
    22. Carsten Hefeker & Michael Neugart, 2019. "Policy Coordination under Model Disagreement and Uncertainty," CESifo Working Paper Series 7477, CESifo.
    23. Mira Frick & Ryota Iijima & Yuhta Ishii, 2019. "Misinterpreting Others and the Fragility of Social Learning," Cowles Foundation Discussion Papers 2160R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2020.
    24. Hefeker, Carsten, 2022. "Policy coordination under model disagreement and asymmetric shocks," Economic Modelling, Elsevier, vol. 114(C).
    25. Benson Tsz Kin Leung, 2020. "Learning in a Small/Big World," Papers 2009.11917, arXiv.org, revised Mar 2023.
    26. Emilien Macault, 2022. "Stochastic Consensus and the Shadow of Doubt," Papers 2201.12100, arXiv.org.
    27. Adriani, Fabrizio & Sonderegger, Silvia, 2009. "Trust, Introspection, and Market Participation: an Evolutionary Approach," MPRA Paper 16110, University Library of Munich, Germany.
    28. Tuval Danenberg, 2025. "Bayesian Polarization," Papers 2509.02513, arXiv.org.
    29. Stone, Daniel, 2018. "Just a big misunderstanding? Bias and Bayesian affective polarization," SocArXiv 58sru, Center for Open Science.
    30. Gieczewski, Germán, 2022. "Verifiable communication on networks," Journal of Economic Theory, Elsevier, vol. 204(C).
    31. Larry G. Epstein & Kyoungwon Seo, 2013. "De Finetti Meets Ellsberg," CIRANO Working Papers 2013s-35, CIRANO.
    32. Floyd Jiuyun Zhang, 2023. "Political endorsement by Nature and trust in scientific expertise during COVID-19," Nature Human Behaviour, Nature, vol. 7(5), pages 696-706, May.
    33. Emiliano Catonini & Tatiana Mayskaya, 2024. "Talking with an extremist," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 77(3), pages 675-697, May.
    34. Meng, Delong & Wang, Siyu, 2024. "Impact of open-mindedness on information avoidance: Tailored vs. generic communication," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 108(C).
    35. Fulghieri, Paolo & Dicks, David, 2015. "Ambiguity, Disagreement, and Allocation of Control in Firms," CEPR Discussion Papers 10400, C.E.P.R. Discussion Papers.
    36. Antonio Jiménez-Martínez, 2012. "Consensus in Communication Networks under Bayesian Updating," Working Papers DTE 529, CIDE, División de Economía.
    37. Larry G. Epstein & Yoram Halevy, 2019. "Hard-to-Interpret Signals," Working Papers tecipa-634, University of Toronto, Department of Economics.
    38. Sandeep Baliga & Eran Hanany & Peter Klibanoff, 2013. "Polarization and Ambiguity," Discussion Papers 1558, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    39. Stephen Morris & Muhamet Yildiz, 2016. "Crises: Equilibrium Shifts and Large Shocks," Working Papers 083_2016, Princeton University, Department of Economics, Econometric Research Program..
    40. Dani Rodrik, 2013. "When Ideas Trump Interests : Preferences, World Views, and Policy  Innovations," Working Papers id:5558, eSocialSciences.
    41. Suen, Richard M. H., 2025. "Biased Information and Opinion Polarisation," MPRA Paper 124953, University Library of Munich, Germany.
    42. Matthew Gentzkow & Jesse M. Shapiro, 2010. "Ideological Segregation Online and Offline," NBER Working Papers 15916, National Bureau of Economic Research, Inc.
    43. Ing-Haw Cheng & Alice Hsiaw, 2016. "Trust in Signals and the Origins of Disagreement," Working Papers 110R4, Brandeis University, Department of Economics and International Business School, revised Dec 2018.
    44. Azomahou, T. & Opolot, D., 2014. "Beliefs dynamics in communication networks," MERIT Working Papers 2014-034, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    45. Benoît, Jean-Pierre & Dubra, Juan, 2014. "A Theory of Rational Attitude Polarization," MPRA Paper 60129, University Library of Munich, Germany.
    46. Ceren Baysan, 2017. "Can More Information Lead to More Voter Polarization? Experimental Evidence from Turkey," 2017 Papers pba1551, Job Market Papers.
    47. Jeong, Daeyoung, 2019. "Using cheap talk to polarize or unify a group of decision makers," Journal of Economic Theory, Elsevier, vol. 180(C), pages 50-80.
    48. Rajiv Sethi & Muhamet Yildiz, 2013. "Perspectives, Opinions, and Information Flows," Levine's Working Paper Archive 786969000000000934, David K. Levine.
    49. Le Yaouanq, Yves, 2023. "A model of voting with motivated beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 394-408.
    50. Pai, Mallesh & Hansen, Karsten, 2020. "Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms," CEPR Discussion Papers 14372, C.E.P.R. Discussion Papers.
    51. Xavier Gabaix & David Laibson & Deyuan Li & Hongyi Li & Sidney Resnick & Casper G. de Vries, 2013. "The Impact of Competition on Prices with Numerous Firms," Working Papers 13-07, Chapman University, Economic Science Institute.
    52. Bohren, J. Aislinn, 2016. "Informational herding with model misspecification," Journal of Economic Theory, Elsevier, vol. 163(C), pages 222-247.
    53. Simsek, Alp, 2012. "Belief Disagreements and Collateral Constraints," Scholarly Articles 9561259, Harvard University Department of Economics.
    54. Mohamed Mostagir & James Siderius, 2022. "Learning in a Post-Truth World," Management Science, INFORMS, vol. 68(4), pages 2860-2868, April.
    55. In-Koo Cho & Kenneth Kasa, 2017. "Model Averaging and Persistent Disagreement," Review, Federal Reserve Bank of St. Louis, vol. 99(3), pages 279-294.
    56. Anderson, Axel & Pkhakadze, Nikoloz, 2025. "Polarizing persuasion," Games and Economic Behavior, Elsevier, vol. 152(C), pages 181-198.
    57. Fudenberg, Drew & Lanzani, Giacomo & Strack, Philipp, 2023. "Pathwise concentration bounds for Bayesian beliefs," Theoretical Economics, Econometric Society, vol. 18(4), November.
    58. Liu, Qi & Sun, Bo, 2018. "Managerial manipulation, corporate governance, and limited market participation," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 98-117.

  139. Victor Chernozhukov & Patrick Gagliardini & Olivier Scaillet, 2008. "Nonparametric Instrumental Variable Estimators of Structural Quantile Effects," Swiss Finance Institute Research Paper Series 08-03, Swiss Finance Institute, revised Aug 2009.

    Cited by:

    1. Florens, Jean-Pierre & Simon, Guillaume, 2010. "Endogeneity and Instrumental Variables in Dynamic Models," TSE Working Papers 10-178, Toulouse School of Economics (TSE).
    2. Senay Sokullu, 2012. "Nonparametric Analysis of Two-Sided Markets," Bristol Economics Discussion Papers 12/628, School of Economics, University of Bristol, UK.
    3. Carrasco, Marine & Florens, Jean-Pierre, 2002. "Spectral Method for Deconvolving a Density," IDEI Working Papers 138, Institut d'Économie Industrielle (IDEI), Toulouse, revised 2009.
    4. Xiaohong Chen & Demian Pouzo, 2008. "Estimation of nonparametric conditional moment models with possibly nonsmooth moments," CeMMAP working papers CWP12/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Xiaohong Chen & Demian Pouzo, 2008. "Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals," Cowles Foundation Discussion Papers 1650RR, Cowles Foundation for Research in Economics, Yale University, revised Jan 2011.
    6. Juan Carlos Escanciano & Wei Li, 2013. "On the identification of structural linear functionals," CeMMAP working papers CWP48/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  140. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2008. "Improving Point and Interval Estimates of Monotone Functions by Rearrangement," Papers 0806.4730, arXiv.org, revised Nov 2008.

    Cited by:

    1. Wu, Ximing & Sickles, Robin, 2018. "Semiparametric estimation under shape constraints," Econometrics and Statistics, Elsevier, vol. 6(C), pages 74-89.
    2. Tae-Hwy Lee & Weiping Yang, 2014. "Granger-Causality in Quantiles between Financial Markets: Using Copula Approach," Working Papers 201406, University of California at Riverside, Department of Economics.
    3. Amy Finkelstein & Erzo F.P. Luttmer & Matthew J. Notowidigdo, 2008. "What Good Is Wealth Without Health? The Effect of Health on the Marginal Utility of Consumption," NBER Working Papers 14089, National Bureau of Economic Research, Inc.
    4. José F. Tudón M., 2019. "Perception, utility, and evolution," Economic Theory Bulletin, Springer;Society for the Advancement of Economic Theory (SAET), vol. 7(2), pages 191-208, December.
    5. Catania, Leopoldo & Luati, Alessandra, 2023. "Semiparametric modeling of multiple quantiles," Journal of Econometrics, Elsevier, vol. 237(2).
    6. Fan, Zengyan & Lian, Heng, 2018. "Quantile regression for additive coefficient models in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 164(C), pages 54-64.
    7. Heuchenne, Cédric & Jacquemain, Alexandre, 2022. "Inference for monotone single-index conditional means: A Lorenz regression approach," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    8. Victor Chernozhukov & Mert Demirer & Esther Duflo & Ivan Fernandez-Val, 2017. "Generic machine learning inference on heterogenous treatment effects in randomized experiments," CeMMAP working papers CWP61/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Joel L. Horowitz & Sokbae (Simon) Lee, 2015. "Nonparametric estimation and inference under shape restrictions," CeMMAP working papers CWP67/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Tae-Hwy Lee & Yundong Tu & Aman Ullah, 2014. "Nonparametric and Semiparametric Regressions Subject to Monotonicity Constraints: Estimation and Forecasting," Working Papers 201404, University of California at Riverside, Department of Economics.
    11. Jun Ma & Vadim Marmer & Artyom Shneyerov & Pai Xu, 2019. "Monotonicity-Constrained Nonparametric Estimation and Inference for First-Price Auctions," Papers 1909.12974, arXiv.org.
    12. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
    13. Dong, Chaohua & Chen, Rong & Xiao, Zhijie & Liu, Weiyi, 2024. "Functional quantile autoregression," Journal of Econometrics, Elsevier, vol. 244(2).
    14. Santiago Acerenza & Vitor Possebom & Pedro H. C. Sant'Anna, 2023. "Was Javert right to be suspicious? Marginal Treatment Effects with Duration Outcomes," Papers 2311.13969, arXiv.org, revised Apr 2025.
    15. Senay Sokullu, 2012. "Nonparametric Analysis of Two-Sided Markets," Bristol Economics Discussion Papers 12/628, School of Economics, University of Bristol, UK.
    16. Marc Henry & Koen Jochmans & Bernard Salanié, 2014. "Inference on Mixtures Under Tail Restrictions," Sciences Po Economics Discussion Papers hal-01053810, HAL.
    17. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly & Kaspar W thrich, 2016. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Diskussionsschriften dp1607, Universitaet Bern, Departement Volkswirtschaft.
    18. Ilaria Lucrezia Amerise, 2013. "Weighted Non-Crossing Quantile Regressions," Working Papers 201308, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    19. Rodrigues, T. & Dortet-Bernadet, J.-L. & Fan, Y., 2019. "Simultaneous fitting of Bayesian penalised quantile splines," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 93-109.
    20. Blundell, Richard & Kristensen, Dennis & Matzkin, Rosa, 2014. "Bounding quantile demand functions using revealed preference inequalities," Journal of Econometrics, Elsevier, vol. 179(2), pages 112-127.
    21. Philippe Van Kerm & Seunghee Yu & Chung Choe, 2016. "Decomposing quantile wage gaps: a conditional likelihood approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 507-527, August.
    22. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers 59/15, Institute for Fiscal Studies.
    23. Denis Chetverikov & Daniel Wilhelm, 2016. "Nonparametric instrumental variable estimation under monotonicity," CeMMAP working papers 48/16, Institute for Fiscal Studies.
    24. Gabriel Montes-Rojas & Lucas Siga & Ram Mainali, 2017. "Mean and quantile regression Oaxaca-Blinder decompositions with an application to caste discrimination," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(3), pages 245-255, September.
    25. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2024. "Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials," Papers 2407.14074, arXiv.org, revised Jan 2025.
    26. Victor Chernozhukov & Iv'an Fern'andez-Val & Martin Weidner, 2018. "Network and Panel Quantile Effects Via Distribution Regression," Papers 1803.08154, arXiv.org, revised Jun 2020.
    27. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2009. "Inference on Counterfactual Distributions," Papers 0904.0951, arXiv.org, revised Sep 2013.
    28. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2024. "Inflation Target at Risk: A Time-varying Parameter Distributional Regression," Papers 2403.12456, arXiv.org, revised Jan 2026.
    29. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Ivan Fernandez-Val, 2016. "Conditional quantile processes based on series or many regressors," CeMMAP working papers 46/16, Institute for Fiscal Studies.
    30. Thomas R. Covert & Richard L. Sweeney, 2023. "Relinquishing Riches: Auctions versus Informal Negotiations in Texas Oil and Gas Leasing," American Economic Review, American Economic Association, vol. 113(3), pages 628-663, March.
    31. Henderson, Daniel J. & List, John A. & Millimet, Daniel L. & Parmeter, Christopher F. & Price, Michael K., 2012. "Empirical implementation of nonparametric first-price auction models," Journal of Econometrics, Elsevier, vol. 168(1), pages 17-28.
    32. Marc Henry & Romuald Meango & Ismael Mourifie, 2020. "Role models and revealed gender-specific costs of STEM in an extended Roy model of major choice," Papers 2005.09095, arXiv.org, revised Aug 2023.
    33. Trabs, Mathias, 2011. "Calibration of self-decomposable Lévy models," SFB 649 Discussion Papers 2011-073, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    34. Wu, Guojun & Song, Ge & Lv, Xiaoxiang & Luo, Shikai & Shi, Chengchun & Zhu, Hongtu, 2023. "DNet: distributional network for distributional individualized treatment effects," LSE Research Online Documents on Economics 122895, London School of Economics and Political Science, LSE Library.
    35. Koen Jochmans, 2013. "Pairwise-comparison estimation with nonparametric controls," Sciences Po Economics Discussion Papers hal-00973068, HAL.
    36. Sokbae (Simon) Lee & Ryo Okui & Yoon-Jae Whang, 2016. "Doubly robust uniform confidence band for the conditional average treatment effect function," CeMMAP working papers CWP03/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    37. Tatiana Komarova & Javier Hidalgo, 2019. "Testing nonparametric shape restrictions," Papers 1909.01675, arXiv.org, revised Jun 2020.
    38. Ghossoub, Mario, 2011. "Monotone equimeasurable rearrangements with non-additive probabilities," MPRA Paper 37629, University Library of Munich, Germany, revised 23 Mar 2012.
    39. Avdulaj Krenar & Barunik Jozef, 2017. "A semiparametric nonlinear quantile regression model for financial returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(1), pages 81-97, February.
    40. Wang, Yunyun & Oka, Tatsushi & Zhu, Dan, 2023. "Bivariate distribution regression with application to insurance data," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 215-232.
    41. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy & Didier Maillard, 2017. "Computation of the Corrected Cornish-Fisher Expansion using the Response Surface Methodology: Application to V aR and CV aR," Thema Working Papers 2017-21, THEMA (Théorie Economique, Modélisation et Applications), CY Cergy-Paris University, ESSEC and CNRS.
    42. Yijian Huang, 2017. "Restoration of Monotonicity Respecting in Dynamic Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 613-622, April.
    43. Horowitz, Joel L. & Lee, Sokbae, 2012. "Uniform confidence bands for functions estimated nonparametrically with instrumental variables," Journal of Econometrics, Elsevier, vol. 168(2), pages 175-188.
    44. Schaumburg, Julia, 2012. "Predicting extreme value at risk: Nonparametric quantile regression with refinements from extreme value theory," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4081-4096.
    45. Bonhomme, Stéphane & Jochmans, Koen & Robin, Jean-Marc, 2017. "Nonparametric estimation of non-exchangeable latent-variable models," Journal of Econometrics, Elsevier, vol. 201(2), pages 237-248.
    46. Kroft, Kory & Lange, Fabian & Notowidigdo, Matthew J., 2012. "Duration Dependence and Labor Market Conditions: Theory and Evidence from a Field Experiment," CLSSRN working papers clsrn_admin-2012-21, Vancouver School of Economics, revised 28 Sep 2012.
    47. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    48. Florent Dubois, 2017. "The Sources of Segregation," Working Papers halshs-01524506, HAL.
    49. Stéphane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2016. "Non-parametric estimation of finite mixtures from repeated measurements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 211-229, January.
    50. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy, 2022. "Proper use of the modified Sharpe ratios in performance measurement: rearranging the Cornish Fisher expansion," Annals of Operations Research, Springer, vol. 313(2), pages 691-712, June.
    51. Tibor Szendrei & Arnab Bhattacharjee & Mark E. Schaffer, 2024. "Fused LASSO as Non-Crossing Quantile Regression," Papers 2403.14036, arXiv.org, revised Apr 2025.
    52. Kneib, Thomas & Silbersdorff, Alexander & Säfken, Benjamin, 2023. "Rage Against the Mean – A Review of Distributional Regression Approaches," Econometrics and Statistics, Elsevier, vol. 26(C), pages 99-123.
    53. Gwennaëlle Mabon, 2017. "Adaptive Deconvolution on the Non-negative Real Line," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 707-740, September.
    54. Marc Henry & Romuald Meango & Ismael Mourifié, 2020. "Revealing Gender-Specific Costs of STEM in an Extended Roy Model of Major Choice," Working Papers 2020-035, Human Capital and Economic Opportunity Working Group.
    55. Patrick Link & Miltiadis Poursanidis & Jochen Schmid & Rebekka Zache & Martin Kurnatowski & Uwe Teicher & Steffen Ihlenfeldt, 2022. "Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2129-2142, October.
    56. Isaiah Andrews & Jesse M. Shapiro, 2020. "A Model of Scientific Communication," NBER Working Papers 26824, National Bureau of Economic Research, Inc.
    57. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy & Jean-Luc Prigent & Donald Keenan & Mahdi Mokrane, 2017. "Modified Sharpe Ratios in Real Estate Performance Measurement: Beyond the Standard Cornish Fisher Expansion," Thema Working Papers 2017-20, THEMA (Théorie Economique, Modélisation et Applications), CY Cergy-Paris University, ESSEC and CNRS.
    58. Victor Chernozhukov & Kaspar Wuthrich & Yinchu Zhu, 2019. "Distributional conformal prediction," Papers 1909.07889, arXiv.org, revised Aug 2021.
    59. Yunyun Wang & Tatsushi Oka & Dan Zhu, 2023. "Distributional Vector Autoregression: Eliciting Macro and Financial Dependence," Papers 2303.04994, arXiv.org.
    60. Lian, Heng & Meng, Jie & Fan, Zengyan, 2015. "Simultaneous estimation of linear conditional quantiles with penalized splines," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 1-21.
    61. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2007. "Quantile and Probability Curves Without Crossing," Papers 0704.3649, arXiv.org, revised Jul 2014.
    62. Lee, Ji Hyung & Park, Byoung G., 2023. "Nonparametric identification and estimation of the extended Roy model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1087-1113.
    63. Anatolyev, Stanislav & Baruník, Jozef, 2019. "Forecasting dynamic return distributions based on ordered binary choice," International Journal of Forecasting, Elsevier, vol. 35(3), pages 823-835.
    64. Elodie Brunel & Fabienne Comte & Valentine Genon-Catalot, 2016. "Nonparametric density and survival function estimation in the multiplicative censoring model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 570-590, September.
    65. Bradley Larsen, 2014. "The Efficiency of Real-World Bargaining: Evidence from Wholesale Used-Auto Auctions," NBER Working Papers 20431, National Bureau of Economic Research, Inc.
    66. David H. Autor & Susan N. Houseman & Sari Pekkala Kerr, 2017. "The Effect of Work First Job Placements on the Distribution of Earnings: An Instrumental Variable Quantile Regression Approach," Journal of Labor Economics, University of Chicago Press, vol. 35(1), pages 149-190.

  141. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2007. "Rearranging Edgeworth-Cornish-Fisher Expansions," Papers 0708.1627, arXiv.org, revised May 2013.

    Cited by:

    1. Brenda Castillo-Brais & Ángel León & Juan Mora, 2022. "Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    2. Charles-Olivier Amedee-Manesme & Fabrice Barthélémy, 2012. "Cornish-Fisher expansion for real estate value at risk," ERES eres2012_044, European Real Estate Society (ERES).
    3. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2019. "Posterior moments and quantiles for the normal location model with Laplace prior," EIEF Working Papers Series 1911, Einaudi Institute for Economics and Finance (EIEF), revised Jun 2019.
    4. Ghossoub, Mario, 2011. "Monotone equimeasurable rearrangements with non-additive probabilities," MPRA Paper 37629, University Library of Munich, Germany, revised 23 Mar 2012.
    5. Liu, Nianqing & Vuong, Quang & Xu, Haiqing, 2017. "Rationalization and identification of binary games with correlated types," Journal of Econometrics, Elsevier, vol. 201(2), pages 249-268.
    6. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy & Didier Maillard, 2017. "Computation of the Corrected Cornish-Fisher Expansion using the Response Surface Methodology: Application to V aR and CV aR," Thema Working Papers 2017-21, THEMA (Théorie Economique, Modélisation et Applications), CY Cergy-Paris University, ESSEC and CNRS.
    7. Victor Chernozhukov & Pierre-André Chiappori & Marc Henry, 2010. "Introduction," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 42(2), pages 271-273, February.
    8. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy, 2022. "Proper use of the modified Sharpe ratios in performance measurement: rearranging the Cornish Fisher expansion," Annals of Operations Research, Springer, vol. 313(2), pages 691-712, June.
    9. Candelon, Bertrand & Fuerst, Franz & Hasse, Jean-Baptiste, 2021. "Diversification potential in real estate portfolios," International Economics, Elsevier, vol. 166(C), pages 126-139.
    10. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy & Jean-Luc Prigent & Donald Keenan & Mahdi Mokrane, 2017. "Modified Sharpe Ratios in Real Estate Performance Measurement: Beyond the Standard Cornish Fisher Expansion," Thema Working Papers 2017-20, THEMA (Théorie Economique, Modélisation et Applications), CY Cergy-Paris University, ESSEC and CNRS.
    11. Stéphane Hamayon & Florence Legros & Pradat Yannick, 2016. "Non gaussian returns: which impact on default options retirement plans? [Distribution non gaussienne des rendements : quel impact sur les options par défaut des plans d'épargne retraite ?]," Working Papers hal-03003588, HAL.
    12. Karlygash Kurlbayeva & Samuel Malone, 2012. "The determinants of extreme commodity prices," OxCarre Working Papers 096, Oxford Centre for the Analysis of Resource Rich Economies, University of Oxford.
    13. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy & Donald Keenan, 2015. "Cornish-Fisher Expansion for Commercial Real Estate Value at Risk," The Journal of Real Estate Finance and Economics, Springer, vol. 50(4), pages 439-464, May.
    14. Lee, Wing Yan & Li, Xiaolong & Liu, Fangda & Shi, Yifan & Yam, Sheung Chi Phillip, 2021. "A Fourier-cosine method for finite-time ruin probabilities," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 256-267.
    15. Zang, Zhaoqi & Xu, Xiangdong & Yang, Chao & Chen, Anthony, 2018. "A closed-form estimation of the travel time percentile function for characterizing travel time reliability," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 228-247.
    16. Carnero, M. Angeles & León, Angel & Ñíguez, Trino-Manuel, 2023. "Skewness in energy returns: estimation, testing and retain-->implications for tail risk," The Quarterly Review of Economics and Finance, Elsevier, vol. 90(C), pages 178-189.

  142. Daron Acemoglu & Victor Chernozhukov & Muhamet Yildiz, 2007. "Learning and Disagreement in an Uncertain World," Carlo Alberto Notebooks 48, Collegio Carlo Alberto.

    Cited by:

    1. Martin W. Cripps & Jeffrey C. Ely & George J. Mailath & Larry Samuelson, 2006. "Common Learning," Cowles Foundation Discussion Papers 1575R, Cowles Foundation for Research in Economics, Yale University, revised Jun 2007.
    2. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
    3. Yu, Jialin, 2011. "Disagreement and return predictability of stock portfolios," Journal of Financial Economics, Elsevier, vol. 99(1), pages 162-183, January.
    4. Piotr Evdokimov & Umberto Garfagnini, 2023. "Cognitive Ability and Perceived Disagreement in Learning," Rationality and Competition Discussion Paper Series 381, CRC TRR 190 Rationality and Competition.
    5. Fudenberg, Drew & Takahashi, Satoru, 2011. "Heterogeneous beliefs and local information in stochastic fictitious play," Games and Economic Behavior, Elsevier, vol. 71(1), pages 100-120, January.
    6. Tim Bollerslev & Jia Li & Yuan Xue, 2016. "Volume, Volatility and Public News Announcements," CREATES Research Papers 2016-19, Department of Economics and Business Economics, Aarhus University.
    7. Jeffrey Hobbs & Hei Wai Lee & Vivek Singh, 2017. "New evidence on the effect of belief heterogeneity on stock returns," Review of Quantitative Finance and Accounting, Springer, vol. 48(2), pages 289-309, February.
    8. Craig Burnside & Martin Eichenbaum & Sergio Rebelo, 2011. "Understanding Booms and Busts in Housing Markets," NBER Working Papers 16734, National Bureau of Economic Research, Inc.
    9. Jose Alvaro Rodrigues-Neto, 2011. "The Cycles Approach," ANU Working Papers in Economics and Econometrics 2011-547, Australian National University, College of Business and Economics, School of Economics.
    10. , & , & ,, 2016. "Fragility of asymptotic agreement under Bayesian learning," Theoretical Economics, Econometric Society, vol. 11(1), January.
    11. Alexander Zimper & Alexander Ludwig, 2009. "On attitude polarization under Bayesian learning with non-additive beliefs," Journal of Risk and Uncertainty, Springer, vol. 39(2), pages 181-212, October.
    12. David C. Chan, Jr, 2016. "Informational Frictions and Practice Variation: Evidence from Physicians in Training," NBER Working Papers 21855, National Bureau of Economic Research, Inc.
    13. Patton, Andrew J. & Timmermann, Allan, 2010. "Why do forecasters disagree? Lessons from the term structure of cross-sectional dispersion," Journal of Monetary Economics, Elsevier, vol. 57(7), pages 803-820, October.
    14. Maurizio Bovi, 2014. "Shocks and the Expectations Formation Process. A Tale of Two Expectations," Natural Field Experiments 00390, The Field Experiments Website.
    15. Daron Acemoglu & Asuman Ozdaglar, 2011. "Opinion Dynamics and Learning in Social Networks," Dynamic Games and Applications, Springer, vol. 1(1), pages 3-49, March.
    16. Leung, Benson Tsz Kin, 2020. "Limited cognitive ability and selective information processing," Games and Economic Behavior, Elsevier, vol. 120(C), pages 345-369.
    17. Lena Dräger & Michael J. Lamla, 2015. "Disagreement à la Taylor: Evidence from Survey Microdata," Macroeconomics and Finance Series 201503, University of Hamburg, Department of Socioeconomics.
    18. Alonso, Ricardo & Câmara, Odilon, 2016. "Bayesian persuasion with heterogeneous priors," LSE Research Online Documents on Economics 67950, London School of Economics and Political Science, LSE Library.
    19. Lindqvist, Erik & Östling, Robert, 2008. "Political Polarization and the Size of Government," Working Paper Series 749, Research Institute of Industrial Economics.
    20. Wen Chen & Mozaffar Khan & Leonid Kogan & George Serafeim, 2021. "Cross‐firm return predictability and accounting quality," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 48(1-2), pages 70-101, January.
    21. Carlin, Bruce I. & Longstaff, Francis A. & Matoba, Kyle, 2014. "Disagreement and asset prices," Journal of Financial Economics, Elsevier, vol. 114(2), pages 226-238.
    22. Thomas Maag & Michael J. Lamla, 2009. "The role of media for inflation forecast disagreement of households and professional forecasters," KOF Working papers 09-223, KOF Swiss Economic Institute, ETH Zurich.
    23. Wei Xiong & Hongjun Yan & Review Financial, 2007. "Heterogeneous Expectations and Bond Markets," Yale School of Management Working Papers amz2614, Yale School of Management, revised 01 Jun 2009.
    24. Karl Schmedders & Felix Kubler, 2012. "Life-Cycle Portfolio Choice, the Wealth Distribution and Asset Prices," 2012 Meeting Papers 536, Society for Economic Dynamics.
    25. Pierre L. Siklos, 2012. "Sources of Disagreement in Inflation Forecasts: An International Empirical Investigation," CAMA Working Papers 2012-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    26. Dixit, Avinash & Weibull, Jörgen, 2006. "Political Polarization," SSE/EFI Working Paper Series in Economics and Finance 655, Stockholm School of Economics, revised 19 Apr 2007.
    27. Alonso, Ricardo & Câmara, Odilon, 2014. "Persuading skeptics and reaffirming believers," LSE Research Online Documents on Economics 58680, London School of Economics and Political Science, LSE Library.
    28. Xavier Gabaix & David Laibson & Deyuan Li & Hongyi Li & Sidney Resnick & Casper G. de Vries, 2013. "The Impact of Competition on Prices with Numerous Firms," Working Papers 13-07, Chapman University, Economic Science Institute.
    29. Bruce I. Carlin & Francis A. Longstaff & Kyle Matoba, 2012. "Disagreement and Asset Prices," NBER Working Papers 18619, National Bureau of Economic Research, Inc.
    30. Eric Van den Steen, 2011. "Overconfidence by Bayesian-Rational Agents," Management Science, INFORMS, vol. 57(5), pages 884-896, May.
    31. Linardi, Sera, 2017. "Accounting for noise in the microfoundations of information aggregation," Games and Economic Behavior, Elsevier, vol. 101(C), pages 334-353.
    32. Mark Westerfield & Tobias Adrian, 2007. "Disagreement and Learning in a Dynamic Contracting Model," 2007 Meeting Papers 270, Society for Economic Dynamics.
    33. Martin M. Guzman & Joseph E. Stiglitz, 2020. "Towards a Dynamic Disequilibrium Theory with Randomness," NBER Working Papers 27453, National Bureau of Economic Research, Inc.
    34. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.
    35. Valsecchi, Irene, 2025. "Forecasts as repeated cheap talk from an expert of unknown statistical bias," Journal of Economic Behavior & Organization, Elsevier, vol. 233(C).

  143. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2007. "Improving Estimates of Monotone Functions by Rearrangement," Papers 0704.3686, arXiv.org, revised Nov 2010.

    Cited by:

    1. Holger Dette & Stanislav Volgushev, 2008. "Non‐crossing non‐parametric estimates of quantile curves," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 609-627, July.
    2. Andrew Shephard, 2011. "Equilibrium Search and Tax Credit Reform," Working Papers 1336, Princeton University, Department of Economics, Center for Economic Policy Studies..
    3. Henderson, Daniel J. & Parmeter, Christopher F., 2009. "Imposing Economic Constraints in Nonparametric Regression: Survey, Implementation and Extension," IZA Discussion Papers 4103, IZA Network @ LISER.
    4. Matthew J. Notowidigdo, 2011. "The Incidence of Local Labor Demand Shocks," NBER Working Papers 17167, National Bureau of Economic Research, Inc.
    5. Birke, Melanie, 2008. "Shape constrained kernel density estimation," Technical Reports 2008,08, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    6. Henderson, Daniel J. & List, John A. & Millimet, Daniel L. & Parmeter, Christopher F. & Price, Michael K., 2008. "Imposing Monotonicity Nonparametrically in First-Price Auctions," MPRA Paper 8769, University Library of Munich, Germany.
    7. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2008. "Improving point and interval estimates of monotone functions by rearrangement," CeMMAP working papers CWP17/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Birke, Melanie & Bissantz, Nicolai, 2007. "Shape constrained estimators in inverse regression models with convolution-type operator," Technical Reports 2007,35, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    9. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2007. "Rearranging Edgeworth-Cornish-Fisher expansions," CeMMAP working papers CWP19/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  144. Victor Chernozhukov & Ivan Fernandez-Val & Alfred Galichon, 2007. "Quantile and Probability Curves Without Crossing," Papers 0704.3649, arXiv.org, revised Jul 2014.

    Cited by:

    1. Romuald Meango & Esther Mirjam Girsberger, 2023. "Just Ask Them Twice: Choice Probabilities and Identification of Ex ante returns and Willingness-To-Pay," Papers 2303.03009, arXiv.org, revised May 2025.
    2. Nicodemo, Catia & Raya, Josep M., 2012. "Change in the Distribution of House Prices across Spanish Cities," IZA Discussion Papers 6503, IZA Network @ LISER.
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.
    4. Ivan Fernandez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-rank approximations of nonseparable panel models," CeMMAP working papers CWP52/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Gaglianone, Wagner Piazza & Guillén, Osmani Teixeira de Carvalho & Figueiredo, Francisco Marcos Rodrigues, 2018. "Estimating inflation persistence by quantile autoregression with quantile-specific unit roots," Economic Modelling, Elsevier, vol. 73(C), pages 407-430.
    6. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey & Sami Stouli & Francis Vella, 2017. "Semiparametric estimation of structural functions in nonseparable triangular models," CeMMAP working papers CWP48/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    8. Holger Dette & Stanislav Volgushev, 2008. "Non‐crossing non‐parametric estimates of quantile curves," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 609-627, July.
    9. Alexander, Carol & Han, Yang & Meng, Xiaochun, 2023. "Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1078-1096.
    10. Charlier, Isabelle & Paindaveine, Davy & Saracco, Jérôme, 2015. "Conditional quantile estimation based on optimal quantization: From theory to practice," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 20-39.
    11. Christian Manuel Posso Su�rez, 2008. "Desigualdad salarial en Colombia 1984-2005: cambios en la composici�n del mercado laboral y retornos a la educaci�n post-secundaria," Borradores de Economia 5003, Banco de la Republica.
    12. Samantha Leorato & Franco Peracchi, 2015. "Comparing Distribution and Quantile Regression," EIEF Working Papers Series 1511, Einaudi Institute for Economics and Finance (EIEF), revised Oct 2015.
    13. Catania, Leopoldo & Luati, Alessandra, 2023. "Semiparametric modeling of multiple quantiles," Journal of Econometrics, Elsevier, vol. 237(2).
    14. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    15. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results," Papers 1212.0442, arXiv.org, revised Jun 2015.
    16. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    17. Artur J. Lemonte & Germán Moreno-Arenas, 2020. "On a heavy-tailed parametric quantile regression model for limited range response variables," Computational Statistics, Springer, vol. 35(1), pages 379-398, March.
    18. Xenxo Vidal-Llana & Carlos Salort Sánchez & Vincenzo Coia & Montserrat Guillen, 2022. ""Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation estimations with non-crossing conditions"," IREA Working Papers 202215, University of Barcelona, Research Institute of Applied Economics, revised Oct 2022.
    19. Young-Joo Kim & Vincent Daly, 2019. "The Education Gradient in Health: The Case of Obesity in the UK and US," Economics Discussion Papers 2019-4, School of Economics, Kingston University London.
    20. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    21. Sohag, Kazi & Kliestik, Tomas & Shams, S.M. Riad & Mariev, Oleg & Davidson, Natalia, 2022. "Capital market deepening, Governor’s characteristics and Russian regional enterprises: A big data analysis," Journal of Business Research, Elsevier, vol. 149(C), pages 340-352.
    22. Choonsung Park, 2020. "Consumption, Reservation Wages, and Aggregate Labor Supply," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 37, pages 54-80, July.
    23. Gail Pacheco & Bill Cochrane, 2015. "Decomposing the temporary-permanent wage gap in New Zealand," Working Papers 2015-07, Auckland University of Technology, Department of Economics.
    24. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2018. "Distribution regression with sample selection, with an application to wage decompositions in the UK," CeMMAP working papers CWP68/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    25. Ruhose, Jens & Parey, Matthias & Waldinger, Fabian & Netz, Nicolai, 2015. "The Selection of High-Skilled Migrants," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113148, Verein für Socialpolitik / German Economic Association.
    26. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    27. Qi Li & Juan Lin & Jeffrey S. Racine, 2012. "Optimal Bandwidth Selection for Nonparametric Conditional Distribution and Quantile Functions," Department of Economics Working Papers 2012-10, McMaster University.
    28. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    29. Zongwu Cai & Ying Fang & Ming Lin & Yaqian Wu, 2024. "Estimating Counterfactual Distribution Functions via Optimal Distribution Balancing with Applications," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202415, University of Kansas, Department of Economics.
    30. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    31. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly, 2012. "Inference on counterfactual distributions," CeMMAP working papers 05/12, Institute for Fiscal Studies.
    32. Patrick A. Adams & Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2020. "Forecasting Macroeconomic Risks," Staff Reports 914, Federal Reserve Bank of New York.
    33. Jonathan Berrisch & Florian Ziel, 2023. "Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices," Papers 2303.10019, arXiv.org, revised Feb 2024.
    34. Xing Yan & Weizhong Zhang & Lin Ma & Wei Liu & Qi Wu, 2020. "Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning," Papers 2010.08263, arXiv.org.
    35. Michael Gurkov & Osnat Zohar, 2022. "Growth at Risk: Forecast Distribution of GDP Growth in Israel," Bank of Israel Working Papers 2022.08, Bank of Israel.
    36. Brantly Callaway, 2017. "Job Displacement during the Great Recession: Tight Bounds on Distributional Treatment Effect Parameters using Panel Data," DETU Working Papers 1703, Department of Economics, Temple University.
    37. Samantha Leorato & Franco Peracchi, 2015. "Shape Regressions," EIEF Working Papers Series 1506, Einaudi Institute for Economics and Finance (EIEF), revised Jul 2015.
    38. Julio Galvez & Javier Mencía, 2014. "Distributional Linkages between European Sovereign Bond and Bank Asset Returns," Working Papers wp2014_1407, CEMFI.
    39. Dong, Chaohua & Chen, Rong & Xiao, Zhijie & Liu, Weiyi, 2024. "Functional quantile autoregression," Journal of Econometrics, Elsevier, vol. 244(2).
    40. Kazi Musa & Norli Ali & Jamaliah Said & Farha Ghapar & Oleg Mariev & Norhayati Mohamed & Hirnissa Mohd Tahir, 2023. "Does the Effectiveness of Budget Deficit Vary between Welfare and Non-Welfare Countries?," Sustainability, MDPI, vol. 15(5), pages 1-22, February.
    41. Tibor Szendrei, 2025. "Crossing penalised CAViaR," Papers 2501.10564, arXiv.org.
    42. Manuel Arellano & Stéphane Bonhomme, 2016. "Quantile Selection Models with an Application to Understanding Changes in Wage Inequality," Working Papers wp2016_1610, CEMFI.
    43. Li, Ting & Shi, Chengchun & Lu, Zhaohua & Li, Yi & Zhu, Hongtu, 2024. "Evaluating dynamic conditional quantile treatment effects with applications in ridesharing," LSE Research Online Documents on Economics 122488, London School of Economics and Political Science, LSE Library.
    44. David Kohns & Tibor Szendrei, 2025. "Joint Quantile Shrinkage: A State-Space Approach toward Non-Crossing Bayesian Quantile Models," Papers 2506.13257, arXiv.org, revised Aug 2025.
    45. Markus Frolich & Blaise Melly, 2010. "Estimation of quantile treatment effects with Stata," Stata Journal, StataCorp LLC, vol. 10(3), pages 423-457, September.
    46. He, Yaoyao & Zheng, Yaya, 2018. "Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function," Energy, Elsevier, vol. 154(C), pages 143-156.
    47. Ivan Fernandez-Val & Wayne Yuan Gao & Yuan Liao & Francis Vella, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," Papers 2202.04154, arXiv.org, revised Jul 2025.
    48. Roger Koenker & Samantha Leorato & Franco Peracchi, 2013. "Distributional vs. Quantile Regression," CEIS Research Paper 300, Tor Vergata University, CEIS, revised 17 Dec 2013.
    49. Ziel, Florian, 2022. "M5 competition uncertainty: Overdispersion, distributional forecasting, GAMLSS, and beyond," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1546-1554.
    50. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    51. Ilaria Lucrezia Amerise, 2013. "Weighted Non-Crossing Quantile Regressions," Working Papers 201308, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    52. Siyi Wang & Xing Yan & Bangqi Zheng & Hu Wang & Wangli Xu & Nanbo Peng & Qi Wu, 2021. "Risk and return prediction for pricing portfolios of non-performing consumer credit," Papers 2110.15102, arXiv.org.
    53. Cannon, Alex J., 2017. "Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes," Earth Arxiv wg7sn, Center for Open Science.
    54. Gimenes, Nathalie & Guerre, Emmanuel, 2022. "Quantile regression methods for first-price auctions," Journal of Econometrics, Elsevier, vol. 226(2), pages 224-247.
    55. Tibor Szendrei & Arnab Bhattacharjee, 2024. "Momentum Informed Inflation-at-Risk," Papers 2408.12286, arXiv.org.
    56. Le Gal La Salle, Josselin & Badosa, Jordi & David, Mathieu & Pinson, Pierre & Lauret, Philippe, 2020. "Added-value of ensemble prediction system on the quality of solar irradiance probabilistic forecasts," Renewable Energy, Elsevier, vol. 162(C), pages 1321-1339.
    57. Bonaccolto, G. & Caporin, M. & Gupta, R., 2018. "The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 446-469.
    58. Alonso-Suárez, R. & David, M. & Branco, V. & Lauret, P., 2020. "Intra-day solar probabilistic forecasts including local short-term variability and satellite information," Renewable Energy, Elsevier, vol. 158(C), pages 554-573.
    59. Romauld Méango, 2023. "Identification of ex ante returns using elicited choice probabilities," Economics Series Working Papers 1007, University of Oxford, Department of Economics.
    60. Gabriel Montes-Rojas & Lucas Siga & Ram Mainali, 2017. "Mean and quantile regression Oaxaca-Blinder decompositions with an application to caste discrimination," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 15(3), pages 245-255, September.
    61. Iv'an Fern'andez-Val & Hugo Freeman & Martin Weidner, 2020. "Low-Rank Approximations of Nonseparable Panel Models," Papers 2010.12439, arXiv.org, revised Mar 2021.
    62. Frölich, Markus & Melly, Blaise, 2008. "Quantile Treatment Effects in the Regression Discontinuity Design," IZA Discussion Papers 3638, IZA Network @ LISER.
    63. Francisco Covas & Ben Rump & Egon Zakrajšek, 2013. "Stress-testing U.S. bank holding companies: a dynamic panel quantile regression approach," Finance and Economics Discussion Series 2013-55, Board of Governors of the Federal Reserve System (U.S.).
    64. Runyao Yu & Yuchen Tao & Fabian Leimgruber & Tara Esterl & Jochen Stiasny & Derek W. Bunn & Qingsong Wen & Hongye Guo & Jochen L. Cremer, 2025. "OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting," Papers 2502.06830, arXiv.org, revised Jan 2026.
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    208. Kai Feng & Han Hong & Denis Nekipelov, 2024. "Statistical Inference of Optimal Allocations I: Regularities and their Implications," Papers 2403.18248, arXiv.org, revised Feb 2026.
    209. Wagner Piazza Gaglianone & Luiz Renato Lima, 2012. "Constructing Density Forecasts from Quantile Regressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1589-1607, December.
    210. Giuseppe Storti & Chao Wang, 2023. "Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1648-1663, November.

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    1. Jorge E. Galán, 2020. "The benefits are at the tail: uncovering the impact of macroprudential policy on growth-at-risk," Working Papers 2007, Banco de España.
    2. Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2020. "Factorisable Multitask Quantile Regression," IRTG 1792 Discussion Papers 2020-004, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Lee, Ji Hyung, 2016. "Predictive quantile regression with persistent covariates: IVX-QR approach," Journal of Econometrics, Elsevier, vol. 192(1), pages 105-118.
    4. Matthew A Masten & Alexandre Poirier, 2023. "Choosing exogeneity assumptions in potential outcome models," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 327-349.
    5. Eric Blankmeyer, 2012. "Estimating an inflation index by quantile regression," Applied Economics Letters, Taylor & Francis Journals, vol. 19(2), pages 185-187, February.
    6. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
    7. Schimke, Antje, 2014. "Aging workforce and firm growth in the context of "extreme" employment growth events," Working Paper Series in Economics 54, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    8. Lach, Saul & Moraga-González, José-Luis, 2009. "Heterogeneous Price Information and the Effect of Competition," CEPR Discussion Papers 7319, C.E.P.R. Discussion Papers.
    9. Alexandre Belloni & Victor Chernozhukov, 2009. "L1-Penalized quantile regression in high-dimensional sparse models," CeMMAP working papers CWP10/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. López-Espinosa, Germán & Moreno, Antonio & Rubia, Antonio & Valderrama, Laura, 2015. "Systemic risk and asymmetric responses in the financial industry," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 471-485.
    11. Calluzzo, Paul & Dong, Gang Nathan, 2015. "Has the financial system become safer after the crisis? The changing nature of financial institution risk," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 233-248.
    12. Fabrice Etilé, 2009. "Food price policies and the distribution of body mass index: Theory and empirical evidence from France," Working Papers halshs-00586720, HAL.
    13. Hou, Yanxi & Leng, Xuan & Peng, Liang & Zhou, Yinggang, 2024. "Panel quantile regression for extreme risk," Journal of Econometrics, Elsevier, vol. 240(1).
    14. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    15. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    16. D’Haultfœuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2018. "Extremal quantile regressions for selection models and the black–white wage gap," Journal of Econometrics, Elsevier, vol. 203(1), pages 129-142.
    17. Galina Besstremyannaya & Sergei Golovan, 2023. "Measuring heterogeneity in hospital productivity: a quantile regression approach," Journal of Productivity Analysis, Springer, vol. 59(1), pages 15-43, February.
    18. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    19. Carlos Martins-Filho & Feng Yao & Maximo Torero, 2012. "Nonparametric estimation of conditional value-at-risk and expected shortfall based on extreme value theory," Working Papers 13-05, Department of Economics, West Virginia University.
    20. Sottile, Gianluca & Frumento, Paolo, 2022. "Robust estimation and regression with parametric quantile functions," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    21. Falconio, Andrea & Manganelli, Simone, 2025. "Financial conditions, business cycle fluctuations and growth-at-risk," Journal of Economic Dynamics and Control, Elsevier, vol. 176(C).
    22. Burdekin, Richard C.K. & Siklos, Pierre L., 2022. "Armageddon and the stock market: US, Canadian and Mexican market responses to the 1962 Cuban Missile Crisis," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 112-127.
    23. Dolado, Juan J & Chen, Liang & Gonzalo, Jesus, 2018. "Quantile Factor Models," CEPR Discussion Papers 12716, C.E.P.R. Discussion Papers.
    24. Joseph G. Altonji & Hidehiko Ichimura & Taisuke Otsu, 2008. "Estimating Derivatives in Nonseparable Models with Limited Dependent Variables," Cowles Foundation Discussion Papers 1668R, Cowles Foundation for Research in Economics, Yale University, revised May 2011.
    25. Carlos Martins-Filho & Feng Yao & Maximo Torero, 2015. "High-Order Conditional Quantile Estimation Based on Nonparametric Models of Regression," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 907-958, December.
    26. Stéphane Girard & Gilles Claude Stupfler & Antoine Usseglio-Carleve, 2021. "Extreme Conditional Expectile Estimation in Heavy-Tailed Heteroscedastic Regression Models," Post-Print hal-03306230, HAL.
    27. Abdelaati Daouia & Gilles Claude Stupfler & Antoine Usseglio-Carleve, 2023. "Inference for extremal regression with dependent heavy-tailed data," Post-Print hal-04554050, HAL.
    28. Duschl, Matthias & Schimke, Antje & Brenner, Thomas & Luxen, Dennis, 2011. "Firm growth and the spatial impact of geolocated external factors: Empirical evidence for German manufacturing firms," Working Paper Series in Economics 36, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    29. Benjamin Hamidi & Emmanuel Jurczenko & Bertrand Maillet, 2009. "D'un multiple conditionnel en assurance de portefeuille : CAViaR pour les gestionnaires ?," Post-Print halshs-00389773, HAL.
    30. Philippe Van Kerm & Seunghee Yu & Chung Choe, 2016. "Decomposing quantile wage gaps: a conditional likelihood approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 507-527, August.
    31. Galina Besstremyannaya & Richard Dasher & Sergei Golovan, 2022. "Quantifying heterogeneity in the relationship between R&D intensity and growth at innovative Japanese firms: A quantile regression approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 67, pages 27-45.
    32. Liao, Wen-Chi & Zhao, Daxuan, 2019. "The selection and quantile treatment effects on the economic returns of green buildings," Regional Science and Urban Economics, Elsevier, vol. 74(C), pages 38-48.
    33. Daisuke Kurisu & Taisuke Otsu, 2021. "Nonparametric inference for extremal conditional quantiles," STICERD - Econometrics Paper Series 616, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    34. Vladislav Morozov, 2022. "Inference on Extreme Quantiles of Unobserved Individual Heterogeneity," Papers 2210.08524, arXiv.org, revised Jun 2025.
    35. Sergey Alexeev, 2023. "Technical change and wage premiums amongst skilled labour: Evidence from the economic transition," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 31(1), pages 189-216, January.
    36. Michal Franta & Jan Vlcek, 2024. "Wage-Price Spirals: A Risk-Based Approach," Working Papers 2024/1, Czech National Bank, Research and Statistics Department.
    37. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Adewuyi, Adeolu O. & Lee, Chien-Chiang, 2022. "Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak," Energy Economics, Elsevier, vol. 113(C).
    38. Kurisu, Daisuke & Otsu, Taisuke, 2023. "Subsampling inference for nonparametric extremal conditional quantiles," LSE Research Online Documents on Economics 120365, London School of Economics and Political Science, LSE Library.
    39. Jennifer Betz & Maximilian Nagl & Daniel Rösch, 2022. "Credit line exposure at default modelling using Bayesian mixed effect quantile regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2035-2072, October.
    40. Bousebata, Meryem & Enjolras, Geoffroy & Girard, Stéphane, 2023. "Extreme partial least-squares," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    41. Liu, Yanbo & Phillips, Peter C.B., 2023. "Robust inference with stochastic local unit root regressors in predictive regressions," Journal of Econometrics, Elsevier, vol. 235(2), pages 563-591.
    42. Lidia Sanchis-Marco & Antonio Rubia Serrano, 2011. "On downside risk predictability through liquidity and trading activity: a quantile regression approach," Working Papers. Serie AD 2011-14, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    43. Joseph Altonji & Hidehiko Ichimura & Taisuke Otsu, 2019. "Nonparametric intermediate order regression quantiles," STICERD - Econometrics Paper Series 608, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    44. Alex Maynard & Katsumi Shimotsu & Nina Kuriyama, 2023. "Inference in Predictive Quantile Regressions," Papers 2306.00296, arXiv.org, revised May 2024.
    45. Qi Zheng & Colin Gallagher & K.B. Kulasekera, 2013. "Adaptively weighted kernel regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(4), pages 855-872, December.
    46. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
    47. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    48. Matthias Duschl & Antje Schimke & Thomas Brenner & Dennis Luxen, 2011. "Firm Growth and the Spatial Impact of Geolocated External Factors – Empirical Evidence for German Manufacturing Firms," Working Papers on Innovation and Space 2011-03, Philipps University Marburg, Department of Geography.
    49. Schimke, Antje, 2012. "Entrepreneurial aging and employment growth in the context of extreme growth events," Working Paper Series in Economics 39, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    50. Jerry Hausman & Haoyang Liu & Ye Luo & Christopher Palmer, 2021. "Errors in the Dependent Variable of Quantile Regression Models," Econometrica, Econometric Society, vol. 89(2), pages 849-873, March.
    51. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    52. Lach, Saul & Moraga-González, José-Luis, 2015. "Asymmetric Price Effects of Competition," CEPR Discussion Papers 10456, C.E.P.R. Discussion Papers.
    53. Firpo, Sergio & Galvao, Antonio F. & Pinto, Cristine & Poirier, Alexandre & Sanroman, Graciela, 2022. "GMM quantile regression," Journal of Econometrics, Elsevier, vol. 230(2), pages 432-452.
    54. International Monetary Fund, 2012. "Short-Term Wholesale Funding and Systemic Risk: A Global Covar Approach," IMF Working Papers 2012/046, International Monetary Fund.
    55. Chen, Zhao & Cheng, Vivian Xinyi & Liu, Xu, 2024. "Hypothesis testing on high dimensional quantile regression," Journal of Econometrics, Elsevier, vol. 238(1).
    56. Zernov, Serguei & Zinde-Walsh, Victoria & Galbraith, John W., 2009. "Asymptotics for estimation of quantile regressions with truncated infinite-dimensional processes," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 497-508, March.
    57. Yuya Sasaki & Yulong Wang, 2022. "Extreme Changes in Changes," Papers 2211.14870, arXiv.org, revised May 2023.
    58. Norman Maswanganyi & Caston Sigauke & Edmore Ranganai, 2021. "Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data," Energies, MDPI, vol. 14(20), pages 1-21, October.
    59. Wei‐han Liu, 2020. "Are Gold and Government Bond Safe‐Haven Assets? An Extremal Quantile Regression Analysis," International Review of Finance, International Review of Finance Ltd., vol. 20(2), pages 451-483, June.
    60. López-Espinosa, Germán & Rubia, Antonio & Valderrama, Laura & Antón, Miguel, 2013. "Good for one, bad for all: Determinants of individual versus systemic risk," Journal of Financial Stability, Elsevier, vol. 9(3), pages 287-299.
    61. Daouia, Abdelaati & Gardes, Laurent & Girard, Stephane, 2011. "On kernel smoothing for extremal quantile regression," LIDAM Discussion Papers ISBA 2011031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    62. Stefan Holst Bache & Christian M. Dahl & Johannes Tang, "undated". "Headlights on tobacco road to low birthweight outcomes - Evidence from a battery of quantile regression estimators and a heterogeneous panelCreation-Date: 20080508," CREATES Research Papers 2008-20, Department of Economics and Business Economics, Aarhus University.
    63. Germán López-Espinosa & Antonio Moreno & Antonio Rubia & Laura Valderrama, 2012. "Short-term Wholesale Funding and Systemic Risk: A Global CoVaR Approach," Faculty Working Papers 02/12, School of Economics and Business Administration, University of Navarra.
    64. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2015. "VAR for VaR: Measuring tail dependence using multivariate regression quantiles," Journal of Econometrics, Elsevier, vol. 187(1), pages 169-188.
    65. Juraj Bodik, 2024. "Extreme Treatment Effect: Extrapolating Dose-Response Function into Extreme Treatment Domain," Mathematics, MDPI, vol. 12(10), pages 1-36, May.
    66. Franguridi, Grigory & Gafarov, Bulat & Wüthrich, Kaspar, 2025. "Bias correction for quantile regression estimators," Journal of Econometrics, Elsevier, vol. 251(C).
    67. Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2022. "Weighted-average quantile regression," Papers 2203.03032, arXiv.org.
    68. Matthias Fischer & Daniel Kraus & Marius Pfeuffer & Claudia Czado, 2017. "Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression," Risks, MDPI, vol. 5(3), pages 1-13, July.
    69. He, Fengyang & Wang, Huixia Judy, 2025. "Extremal local linear quantile regression for nonlinear dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 206(C).
    70. E. Fusco & R. Benedetti & F. Vidoli, 2023. "Stochastic frontier estimation through parametric modelling of quantile regression coefficients," Empirical Economics, Springer, vol. 64(2), pages 869-896, February.
    71. Patrick Bajari & Han Hong & Minjung Park & Robert Town, 2011. "Regression Discontinuity Designs with an Endogenous Forcing Variable and an Application to Contracting in Health Care," NBER Working Papers 17643, National Bureau of Economic Research, Inc.
    72. Chen, Zhao & Cheng, Vivian Xinyi & Liu, Xu, 2024. "Reprint: Hypothesis testing on high dimensional quantile regression," Journal of Econometrics, Elsevier, vol. 239(2).
    73. Jurecková, Jana, 2010. "Finite-sample distribution of regression quantiles," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1940-1946, December.
    74. Xiong, Qizhou, 2015. "Censored Fractional Response Model: Estimating Heterogeneous Relative Risk Aversion of European Households," IWH Discussion Papers 11/2015, Halle Institute for Economic Research (IWH).
    75. M. Carvalho & S. Pereira & P. Pereira & P. Zea Bermudez, 2022. "An Extreme Value Bayesian Lasso for the Conditional Left and Right Tails," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 222-239, June.
    76. Sulkhan Chavleishvili & Simone Manganelli, 2024. "Forecasting and stress testing with quantile vector autoregression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 66-85, January.
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    Cited by:

    1. Arie Beresteanu, 2009. "Sharp Identification Regions in Models with Convex Predictions: Games, Individual Choice, and Incomplete Data," Working Paper 428, Department of Economics, University of Pittsburgh, revised Sep 2010.
    2. Andrew Chesher, 2003. "Nonparametric identification under discrete variation," CeMMAP working papers CWP19/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Molinari, Francesca, 2005. "Missing Treatments," Working Papers 05-11, Cornell University, Center for Analytic Economics.
    4. Donald W.K. Andrews & Patrik Guggenberger, 2007. "Validity of Subsampling and "Plug-in Asymptotic" Inference for Parameters Defined by Moment Inequalities," Cowles Foundation Discussion Papers 1620, Cowles Foundation for Research in Economics, Yale University.
    5. Guido Imbens & Charles F. Manski, 2003. "Confidence intervals for partially identified parameters," CeMMAP working papers CWP09/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Adam Rosen, 2007. "Identification and estimation of firms' marginal cost functions with incomplete knowledge of strategic behavior," CeMMAP working papers CWP03/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Fan, Yanqin & Park, Sang Soo, 2009. "Partial identification of the distribution of treatment effects and its confidence sets," MPRA Paper 37148, University Library of Munich, Germany.
    8. Victor Aguirregabiria & Pedro mira, 2007. "Dynamic Discrete Choice Structural Models: A Survey," Working Papers tecipa-297, University of Toronto, Department of Economics.
    9. Bajari, Patrick & Benkard, C. Lanier & Levin, Jonathan, 2007. "Estimating Dynamic Models of Imperfect Competition," Research Papers 1852r1, Stanford University, Graduate School of Business.
    10. J. Stoye, 2009. "Charles F. Manski, Identification for Prediction and Decision (Harvard University Press 2007)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 857-862.
    11. Kreider, Brent & Pepper, John V., 2011. "Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean Independence," Staff General Research Papers Archive 12496, Iowa State University, Department of Economics.
    12. Arie Beresteanu & Francesca Molinari, 2006. "Asymptotic properties for a class of partially identified models," CeMMAP working papers CWP10/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Adam Rosen, 2006. "Confidence sets for partially identified parameters that satisfy a finite number of moment inequalities," CeMMAP working papers CWP25/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Molinari, Francesca, 2005. "Partial Identification of Probability Distributions with Misclassified Data," Working Papers 05-10, Cornell University, Center for Analytic Economics.
    15. Schorfheide, Frank & Moon, Hyungsik Roger, 2006. "Boosting Your Instruments: Estimation with Overidentifying Inequality Moment Conditions," CEPR Discussion Papers 5605, C.E.P.R. Discussion Papers.
    16. Patrik Guggenberger, 2006. "The limit of finite sample size and a problem with subsampling (joint with D.W.K. Andrews), June 2005, this version March 2007," UCLA Economics Online Papers 372, UCLA Department of Economics.
    17. Fan, Yanqin & Park, Sang Soo, 2010. "Confidence sets for some partially identified parameters," MPRA Paper 37149, University Library of Munich, Germany.
    18. Andrew M. Cohen & Beth A. Freeborn & Brian McManus, 2007. "Competition and Crowding-Out among Public, Non-Profit and For-Profit Organizations: Evidence from Outpatient Substance Abuse Treatment," Working Papers 52, Economics Department, William & Mary.
    19. Esteban-Bravo, Mercedes & Vidal-Sanz, Jose M., 2007. "Worst-case estimation for econometric models with unobservable components," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3330-3354, April.
    20. Guido M. Imbens & Jeffrey M. Wooldridge, 2008. "Recent Developments in the Econometrics of Program Evaluation," NBER Working Papers 14251, National Bureau of Economic Research, Inc.

  147. I. Fernandez-Val & J. Angrist & V. Chernozhukov, 2004. "Quantile Regression under Misspecification," Econometric Society 2004 North American Winter Meetings 198, Econometric Society.

    Cited by:

    1. Juergen Jung & Michael Makowsky, 2014. "The determinants of federal and state enforcement of workplace safety regulations: OSHA inspections 1990–2010," Journal of Regulatory Economics, Springer, vol. 45(1), pages 1-33, February.
    2. Fitzenberger, Bernd & Kohn, Karsten & Lembcke, Alexander, 2008. "Union density and varieties of coverage: the anatomy of union wage effects in Germany," LSE Research Online Documents on Economics 19622, London School of Economics and Political Science, LSE Library.
    3. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    4. Wiji Arulampalam & Alison Booth & Mark Bryan, 2010. "Are there asymmetries in the effects of training on the conditional male wage distribution?," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(1), pages 251-272, January.
    5. Taisuke Otsu, 2009. "RESET for quantile regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 381-391, August.

  148. Joshua Angrist & Victor Chernozhukov & Ivan Fernandez-Val, 2004. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," NBER Working Papers 10428, National Bureau of Economic Research, Inc.

    Cited by:

    1. Stefan Bache & Christian Dahl & Johannes Kristensen, 2013. "Headlights on tobacco road to low birthweight outcomes," Empirical Economics, Springer, vol. 44(3), pages 1593-1633, June.
    2. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "On the asymptotic theory for least squares series: pointwise and uniform results," CeMMAP working papers 73/13, Institute for Fiscal Studies.
    3. Huber, Martin & Melly, Blaise, 2011. "Quantile Regression in the Presence of Sample Selection," Economics Working Paper Series 1109, University of St. Gallen, School of Economics and Political Science.
    4. Lee, Tae-Hwy & Ullah, Aman & Wang, He, 2018. "The second-order bias of quantile estimators," Economics Letters, Elsevier, vol. 173(C), pages 143-147.
    5. Battistin, Erich & Lamarche, Carlos & Rettore, Enrico, 2020. "Quantiles of the Gain Distribution of an Early Childhood Intervention," IZA Discussion Papers 13101, IZA Network @ LISER.
    6. Machado, Jose A F & Santos Silva, Joao M C, 2008. "Quantiles for Fractions and Other Mixed Data," Economics Discussion Papers 3550, University of Essex, Department of Economics.
    7. Andini, Corrado, 2017. "Tertiary Education for All and Wage Inequality: Policy Insights from Quantile Regression," IZA Policy Papers 132, Institute of Labor Economics (IZA).
    8. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, "undated". "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    9. Sasaki, Yuya, 2015. "What Do Quantile Regressions Identify For General Structural Functions?," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1102-1116, October.
    10. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    11. Luciano de Castro & Antonio F. Galvao & David M. Kaplan & Xin Liu, 2017. "Smoothed GMM for quantile models," Papers 1707.03436, arXiv.org, revised Feb 2018.
    12. Samantha Leorato & Franco Peracchi, 2015. "Comparing Distribution and Quantile Regression," EIEF Working Papers Series 1511, Einaudi Institute for Economics and Finance (EIEF), revised Oct 2015.
    13. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    14. Klaus Friesenbichler, 2014. "EU Accession, Domestic Market Competition and Total Factor Productivity. Firm Level Evidence," WIFO Working Papers 492, WIFO.
    15. Javier Alejo & Antonio F. Galvao & Julian Martinez-Iriarte & Gabriel Montes-Rojas, 2023. "Unconditional Quantile Partial Effects via Conditional Quantile Regression," Papers 2301.07241, arXiv.org, revised Dec 2023.
    16. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Some New Asymptotic Theory for Least Squares Series: Pointwise and Uniform Results," Papers 1212.0442, arXiv.org, revised Jun 2015.
    17. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    18. Du, Zaichao & Escanciano, Juan Carlos & Zhu, Guangwei, 2023. "The case for CASE: Estimating heterogeneous systemic effects," Journal of Banking & Finance, Elsevier, vol. 157(C).
    19. Ruosha Li & Limin Peng, 2017. "Assessing quantile prediction with censored quantile regression models," Biometrics, The International Biometric Society, vol. 73(2), pages 517-528, June.
    20. Adelino, Manuel & Dinc, I. Serdar, 2014. "Corporate distress and lobbying: Evidence from the Stimulus Act," Journal of Financial Economics, Elsevier, vol. 114(2), pages 256-272.
    21. Battistin, Erich & Lamarche, Carlos & Rettore, Enrico, 2020. "Quantiles of the Gain Distribution of an Early Child Intervention," CEPR Discussion Papers 14721, C.E.P.R. Discussion Papers.
    22. Wang, Xichen & Duan, Xiaomei, 2024. "What leads some countries to experience larger decreases in foreign flows during low-flow episodes? Evidence from international portfolio flows," Journal of International Money and Finance, Elsevier, vol. 148(C).
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    167. Olivier Bargain & Prudence Kwenda, 2009. "The Informal Sector Wage Gap: New Evidence Using Quantile Regressions on Panel Data," CEDI Discussion Paper Series 09-06, Centre for Economic Development and Institutions(CEDI), Brunel University.
    168. An, Yonghong & Hong, Shengjie & Zhang, Daiqiang, 2023. "A structural analysis of simple contracts," Journal of Econometrics, Elsevier, vol. 236(2).
    169. Yingying Dong & Ying-Ying Lee, 2023. "Doubly Robust Identification of Causal Effects of a Continuous Treatment using Discrete Instruments," Papers 2310.18504, arXiv.org, revised Feb 2024.
    170. Kosovka Ognjenović, 2011. "Wage Differences between the Private and the Public Sector in Serbia: Some Evidence from Survey Data," wiiw Balkan Observatory Working Papers 91, The Vienna Institute for International Economic Studies, wiiw.
    171. Noh, Hohsuk & El Ghouch, Anouar & Van Keilegom, Ingrid, 2013. "Assessing model adequacy in possibly misspecified quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 558-569.
    172. David H. Autor & Lawrence F. Katz & Melissa S. Kearney, 2005. "Trends in U.S. Wage Inequality: Re-Assessing the Revisionists," NBER Working Papers 11627, National Bureau of Economic Research, Inc.
    173. Liu, Yang & Xia, Lucy & Hu, Feifang, 2025. "Testing heterogeneous treatment effect with quantile regression under covariate-adaptive randomization," Journal of Econometrics, Elsevier, vol. 249(PA).
    174. Lee, Sokbae & Liao, Yuan & Seo, Myung Hwan & Shin, Youngki, 2025. "Fast inference for quantile regression with tens of millions of observations," Journal of Econometrics, Elsevier, vol. 249(PA).
    175. Sebastian Bayer & Timo Dimitriadis, 2022. "Regression-Based Expected Shortfall Backtesting [Backtesting Expected Shortfall]," Journal of Financial Econometrics, Oxford University Press, vol. 20(3), pages 437-471.
    176. Takuya Ishihara, 2020. "Panel Data Quantile Regression for Treatment Effect Models," Papers 2001.04324, arXiv.org, revised Nov 2021.
    177. Wiji Arulampalam & Alison Booth & Mark Bryan, 2010. "Are there asymmetries in the effects of training on the conditional male wage distribution?," Journal of Population Economics, Springer;European Society for Population Economics, vol. 23(1), pages 251-272, January.
    178. Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2022. "Weighted-average quantile regression," Papers 2203.03032, arXiv.org.
    179. Tu, Yundong & Wang, Siwei, 2025. "Quantile prediction with factor-augmented regression: Structural instability and model uncertainty," Journal of Econometrics, Elsevier, vol. 249(PB).
    180. Ruofan Xu & Jiti Gao & Tatsushi Oka & Yoon-Jae Whang, 2022. "Estimation of Heterogeneous Treatment Effects Using Quantile Regression with Interactive Fixed Effects," Monash Econometrics and Business Statistics Working Papers 13/22, Monash University, Department of Econometrics and Business Statistics.
    181. Escanciano, J.C. & Goh, S.C., 2014. "Specification analysis of linear quantile models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 495-507.
    182. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference for high-dimensional sparse econometric models," CeMMAP working papers CWP41/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    183. Manuel Landajo & Javier De Andrés & Pedro Lorca, 2008. "Measuring firm performance by using linear and non‐parametric quantile regressions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(2), pages 227-250, April.
    184. David Powell, 2022. "Quantile regression with nonadditive fixed effects," Empirical Economics, Springer, vol. 63(5), pages 2675-2691, November.
    185. Chen, Songnian, 2023. "Two-step estimation of censored quantile regression for duration models with time-varying regressors," Journal of Econometrics, Elsevier, vol. 235(2), pages 1310-1336.
    186. Yanggyu Byun & Hae-shin Hwang, 2015. "Sectoral shifts or aggregate shocks? A new test of sectoral shifts hypothesis," Empirical Economics, Springer, vol. 49(2), pages 481-502, September.
    187. Taisuke Otsu, 2009. "RESET for quantile regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(2), pages 381-391, August.
    188. Antonio Galvao & Kengo Kato & Gabriel Montes-Rojas & Jose Olmo, 2014. "Testing linearity against threshold effects: uniform inference in quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 413-439, April.
    189. Kato Kengo, 2011. "A note on moment convergence of bootstrap M-estimators," Statistics & Risk Modeling, De Gruyter, vol. 28(1), pages 51-61, March.
    190. David M. Kaplan, 2014. "Nonparametric Inference on Quantile Marginal Effects," Working Papers 1413, Department of Economics, University of Missouri.
    191. Andreas Behr, Ulrich Pötter, "undated". "Analysing wage differences between the USA and Germany using proportional hazards models," Working Papers 201158, Institute of Spatial and Housing Economics, Munster Universitary.
    192. Middleditch, Paul & Moindrot, Will & Rudkin, Simon, 2022. "Teaching with Twitter: An extension to the traditional learning environment," International Review of Economics Education, Elsevier, vol. 41(C).
    193. Daniel Pollmann & Thomas Dohmen & Franz Palm, 2020. "Robust Estimation of Wage Dispersion with Censored Data: An Application to Occupational Earnings Risk and Risk Attitudes," De Economist, Springer, vol. 168(4), pages 519-540, December.
    194. Guilherme DePaula, 2018. "The Distributional Impact of Climate Change in Brazilian Agriculture: A Ricardian Quantile Analysis with Census Data," Center for Agricultural and Rural Development (CARD) Publications 18-wp583, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    195. Chen, Zhao & Cheng, Vivian Xinyi & Liu, Xu, 2024. "Reprint: Hypothesis testing on high dimensional quantile regression," Journal of Econometrics, Elsevier, vol. 239(2).
    196. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    197. Lee, Bong Soo & Li, Ming-Yuan Leon, 2012. "Diversification and risk-adjusted performance: A quantile regression approach," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2157-2173.
    198. Bera Anil K. & Galvao Antonio F. & Montes-Rojas Gabriel V. & Park Sung Y., 2016. "Asymmetric Laplace Regression: Maximum Likelihood, Maximum Entropy and Quantile Regression," Journal of Econometric Methods, De Gruyter, vol. 5(1), pages 79-101, January.
    199. Luciano de Castro & Antonio F. Galvao & David M. Kaplan, 2017. "Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations," Working Papers 1710, Department of Economics, University of Missouri, revised 28 Feb 2018.
    200. Franco Peracchi & Andrei V. Tanase, 2008. "On estimating the conditional expected shortfall," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 471-493, September.
    201. Zhang, Yingying & Wang, Huixia Judy & Zhu, Zhongyi, 2019. "Quantile-regression-based clustering for panel data," Journal of Econometrics, Elsevier, vol. 213(1), pages 54-67.
    202. Bargain, Olivier B. & Magejo, Prudence, 2010. "Is Informality Bad? Evidence from Brazil, Mexico and South Africa," IZA Discussion Papers 4711, IZA Network @ LISER.
    203. Graetz, Georg & Feng, Andy, 2014. "Rise of the Machines: The Effects of Labor-Saving Innovations on Jobs and Wages," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100401, Verein für Socialpolitik / German Economic Association.
    204. Sherrilyn Billger & Carlos Lamarche, 2015. "A panel data quantile regression analysis of the immigrant earnings distribution in the United Kingdom and United States," Empirical Economics, Springer, vol. 49(2), pages 705-750, September.
    205. Ji-Eun Choi & Dong Wan Shin, 2022. "Quantile correlation coefficient: a new tail dependence measure," Statistical Papers, Springer, vol. 63(4), pages 1075-1104, August.
    206. Elena Martínez Sanchis & Ilker Kandemir & Juan Mora López, 2011. "Counterfactual distributions of wages via quantile regression with endogeneity," Working Papers. Serie AD 2011-25, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    207. Cai, Zongwu & Chen, Linna & Fang, Ying, 2018. "A semiparametric quantile panel data model with an application to estimating the growth effect of FDI," Journal of Econometrics, Elsevier, vol. 206(2), pages 531-553.
    208. Ricardo Masini, 2022. "Distributional Counterfactual Analysis in High-Dimensional Setup," Papers 2202.11671, arXiv.org, revised Sep 2023.
    209. Boudarbat, Brahim & Lemieux, Thomas & Riddell, W. Craig, 2009. "The Evolution of the Returns to Human Capital in Canada, 1980 - 2006," CLSSRN working papers clsrn_admin-2009-8, Vancouver School of Economics, revised 02 Feb 2009.
    210. Patrick Kline & Andres Santos, 2010. "Sensitivity to Missing Data Assumptions: Theory and An Evaluation of the U.S. Wage Structure," NBER Working Papers 15716, National Bureau of Economic Research, Inc.
    211. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
    212. Ferreira, Francisco H. G. & Firpo, Sergio & Galvao, Antonio F., 2017. "Estimation and Inference for Actual and Counterfactual Growth Incidence Curves," IZA Discussion Papers 10473, IZA Network @ LISER.
    213. Giessing, Alexander & He, Xuming, 2019. "On the predictive risk in misspecified quantile regression," Journal of Econometrics, Elsevier, vol. 213(1), pages 235-260.
    214. Qiang Chen & Zhijie Xiao, 2025. "Spread Regression, Skewness Regression, and Kurtosis Regression With an Application to the US Wage Structure," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(3), pages 325-340, April.
    215. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2015. "IV Quantile Regression for Group-level Treatments, with an Application to the Distributional Effects of Trade," NBER Working Papers 21033, National Bureau of Economic Research, Inc.
    216. P. Čížek & S. Sadikoglu, 2018. "Bias-corrected quantile regression estimation of censored regression models," Statistical Papers, Springer, vol. 59(1), pages 215-247, March.
    217. Bang, James T. & Mitra, Aniruddha & Wunnava, Phanindra V., 2016. "Do remittances improve income inequality? An instrumental variable quantile analysis of the Kenyan case," Economic Modelling, Elsevier, vol. 58(C), pages 394-402.
    218. Rodney J. Andrews & Jing Li & Michael F. Lovenheim, 2012. "Quantile Treatment Effects of College Quality on Earnings: Evidence from Administrative Data in Texas," NBER Working Papers 18068, National Bureau of Economic Research, Inc.
    219. Andreas Behr & Ulrich Pötter, 2009. "Analysing Wage Differences between the USA and Germany Using Proportional Hazards Models," LABOUR, CEIS, vol. 23(2), pages 319-347, June.

  149. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, "undated". "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.

    Cited by:

    1. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Baumöhl, Eduard & Bouri, Elie & Hoang, Thi-Hong-Van & Hussain Shahzad, Syed Jawad & Výrost, Tomáš, 2022. "Measuring systemic risk in the global banking sector: A cross-quantilogram network approach," Economic Modelling, Elsevier, vol. 109(C).
    3. Torri, Gabriele & Giacometti, Rosella & Tichý, Tomáš, 2021. "Network tail risk estimation in the European banking system," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    4. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
    5. Hossein Alidaee & Eric Auerbach & Michael P. Leung, 2020. "Recovering Network Structure from Aggregated Relational Data using Penalized Regression," Papers 2001.06052, arXiv.org.

  150. Victor Chernozhukov & Ivan Fernandez-Val & Amanda E. Kowalski, "undated". "Censored Quantile Instrumental Variable Estimation via Control Functions," Boston University - Department of Economics - Working Papers Series wp2009-012, Boston University - Department of Economics.

    Cited by:

    1. Kwak, Sungil, 2011. "The Impact of Taxes on Charitable Giving: Empirical Evidence from the Korean Labor and Income Panel Study," MPRA Paper 36845, University Library of Munich, Germany.
    2. Pravin K. Trivedi, 2010. "Keynote lecture: Estimation of count-data panel models," Mexican Stata Users' Group Meetings 2010 06, Stata Users Group.
    3. Ubfal, Diego, 2012. "How General Are Time Preferences? Eliciting Good-Specific Discount Rates," IZA Discussion Papers 6774, IZA Network @ LISER.

  151. V. Chernozhukov & Ivan Fernandez-Val, "undated". "Quantile and Average Effects in Nonseparable Panel Models," Boston University - Department of Economics - Working Papers Series wp2009-011, Boston University - Department of Economics.

    Cited by:

    1. Kato, Kengo & F. Galvao, Antonio & Montes-Rojas, Gabriel V., 2012. "Asymptotics for panel quantile regression models with individual effects," Journal of Econometrics, Elsevier, vol. 170(1), pages 76-91.
    2. Fernando Ubeda & Francisco Pérez-Hernández, 2017. "Absorptive Capacity and Geographical Distance Two Mediating Factors of FDI Spillovers: a Threshold Regression Analysis for Spanish Firms," Journal of Industry, Competition and Trade, Springer, vol. 17(1), pages 1-28, March.
    3. Stefan Hoderlein & Yuya Sasaki, 2011. "On the role of time in nonseparable panel data models," CeMMAP working papers CWP15/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. David Powell, 2010. "Unconditional Quantile Treatment Effects in the Presence of Covariates," Working Papers WR-816, RAND Corporation.
    5. Amaresh Tiwari & Franz Palm, 2011. "Nonlinear Panel Data Models with Expected a Posteriori Values of Correlated Random Effects," CREPP Working Papers 1113, Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège.
    6. Rosen, Adam M., 2012. "Set identification via quantile restrictions in short panels," Journal of Econometrics, Elsevier, vol. 166(1), pages 127-137.
    7. Gao, Yichen & Li, Cong & Liang, Zhongwen, 2015. "Binary response correlated random coefficient panel data models," Journal of Econometrics, Elsevier, vol. 188(2), pages 421-434.
    8. Charles F. Manski & John V. Pepper, 2011. "Deterrence and the Death Penalty: Partial Identification Analysis Using Repeated Cross Sections," NBER Working Papers 17455, National Bureau of Economic Research, Inc.
    9. Harding, Matthew & Lamarche, Carlos, 2012. "Estimating and Testing a Quantile Regression Model with Interactive Effects," IZA Discussion Papers 6802, IZA Network @ LISER.

Articles

  1. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, 2025. "Quantile graphical models: Prediction and conditional independence with applications to systemic risk," Journal of Econometrics, Elsevier, vol. 252(PA).
    See citations under working paper version above.
  2. Bajari, P. & Cen, Z. & Chernozhukov, V. & Manukonda, M. & Vijaykumar, S. & Wang, J. & Huerta, R. & Li, J. & Leng, L. & Monokroussos, G. & Wang, S., 2025. "Hedonic prices and quality adjusted price indices powered by AI," Journal of Econometrics, Elsevier, vol. 251(C).
    See citations under working paper version above.
  3. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández‐Val, 2025. "Fisher–Schultz Lecture: Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, With an Application to Immunization in India," Econometrica, Econometric Society, vol. 93(4), pages 1121-1164, July.
    See citations under working paper version above.
  4. Chernozhukov, Victor & Fernández-Val, Iván & Weidner, Martin, 2024. "Network and panel quantile effects via distribution regression," Journal of Econometrics, Elsevier, vol. 240(2).
    See citations under working paper version above.
  5. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2023. "Constrained Conditional Moment Restriction Models," Econometrica, Econometric Society, vol. 91(2), pages 709-736, March.
    See citations under working paper version above.
  6. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.

    Cited by:

    1. Kaicheng Chen, 2025. "Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity," Papers 2504.18772, arXiv.org, revised Dec 2025.
    2. Stéphane Bonhomme & Koen Jochmans & Martin Weidner, 2025. "A neyman-orthogonalization approach to the incidental parameter problem," CeMMAP working papers 05/25, Institute for Fiscal Studies.
    3. Ciżkowicz, Piotr & Ledóchowski, Michał & Rzońca, Andrzej, 2025. "Fiscal policy and government bond yields: New evidence from the EU," Economic Modelling, Elsevier, vol. 147(C).

  7. V Chernozhukov & W K Newey & R Singh, 2023. "A simple and general debiased machine learning theorem with finite-sample guarantees," Biometrika, Biometrika Trust, vol. 110(1), pages 257-264.
    See citations under working paper version above.
  8. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2022. "Locally Robust Semiparametric Estimation," Econometrica, Econometric Society, vol. 90(4), pages 1501-1535, July.
    See citations under working paper version above.
  9. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2022. "Automatic Debiased Machine Learning of Causal and Structural Effects," Econometrica, Econometric Society, vol. 90(3), pages 967-1027, May.
    See citations under working paper version above.
  10. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2022. "Debiased machine learning of global and local parameters using regularized Riesz representers [Semiparametric instrumental variable estimation of treatment response models]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 576-601.
    See citations under working paper version above.
  11. Guillaume Carlier & Victor Chernozhukov & Gwendoline De Bie & Alfred Galichon, 2022. "Vector quantile regression and optimal transport, from theory to numerics," Empirical Economics, Springer, vol. 62(1), pages 35-62, January.
    See citations under working paper version above.
  12. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly, 2022. "Fast algorithms for the quantile regression process," Empirical Economics, Springer, vol. 62(1), pages 7-33, January.
    See citations under working paper version above.
  13. Chernozhukov, Victor & Kasahara, Hiroyuki & Schrimpf, Paul, 2021. "Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S," Journal of Econometrics, Elsevier, vol. 220(1), pages 23-62.
    See citations under working paper version above.
  14. Vira Semenova & Victor Chernozhukov, 2021. "Debiased machine learning of conditional average treatment effects and other causal functions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 264-289.

    Cited by:

    1. Goller, Daniel & Diem, Andrea & Wolter, Stefan C., 2022. "Sitting Next to a Dropout: Academic Success of Students with More Educated Peers," IZA Discussion Papers 15378, IZA Network @ LISER.
    2. Phillip Heiler & Michael C. Knaus, 2025. "Heterogeneity Analysis with Heterogeneous Treatments," Papers 2507.01517, arXiv.org, revised Feb 2026.
    3. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    4. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    5. Kawata, Keisuke & Komura, Mizuki, 2025. "Only-Child Matching Penalty in the Marriage Market," IZA Discussion Papers 18198, IZA Network @ LISER.
    6. Vira Semenova, 2023. "Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters," Papers 2303.00982, arXiv.org, revised May 2025.
    7. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    8. Alejandro Sanchez-Becerra, 2023. "Robust inference for the treatment effect variance in experiments using machine learning," Papers 2306.03363, arXiv.org.
    9. Dmitry Arkhangelsky & Kazuharu Yanagimoto & Tom Zohar, 2024. "On Causal Inference with Model-Based Outcomes," Papers 2403.19563, arXiv.org, revised Jan 2026.
    10. Patrick Rehill & Nicholas Biddle, 2023. "Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability," Papers 2310.13240, arXiv.org, revised Mar 2024.
    11. Yao Cui & Andrew M. Davis, 2022. "Tax-Induced Inequalities in the Sharing Economy," Management Science, INFORMS, vol. 68(10), pages 7202-7220, October.
    12. Patrick Rehill & Nicholas Biddle, 2023. "Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making," Papers 2309.00805, arXiv.org.
    13. Dana Turjeman & Fred M. Feinberg, 2024. "When the Data Are Out: Measuring Behavioral Changes Following a Data Breach," Marketing Science, INFORMS, vol. 43(2), pages 440-461, March.
    14. Ang Yu & Felix Elwert, 2023. "Nonparametric Causal Decomposition of Group Disparities," Papers 2306.16591, arXiv.org, revised Dec 2024.
    15. Di Liu, 2024. "Treatment-effects estimation using lasso," Chinese Stata Conference 2024 09, Stata Users Group.
    16. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    17. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jul 2024.
    18. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
    19. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    20. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Jan 2025.
    21. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
    22. Adam Baybutt, 2024. "Dynamic Latent-Factor Model with High-Dimensional Asset Characteristics," Papers 2405.15721, arXiv.org.
    23. Taiyo Fukai & Keisuke Kawata & Mizuki Komura & Takahiro Toriyabe, 2024. "Gender gap in the ask salaries: Evidence from larger administrative data," Discussion Paper Series 284, School of Economics, Kwansei Gakuin University.
    24. Tesary Lin & Avner Strulov-Shlain, 2023. "Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data," Papers 2308.13496, arXiv.org.
    25. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
    26. Philipp Schwarz & Oliver Schacht & Sven Klaassen & Daniel Grunbaum & Sebastian Imhof & Martin Spindler, 2024. "Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?," Papers 2406.11308, arXiv.org.
    27. Max Vilgalys, 2023. "A Machine Learning Approach to Measuring Climate Adaptation," Papers 2302.01236, arXiv.org.
    28. Serguei Maliar & Bernard Salanie, 2024. "Testing for Asymmetric Information in Insurance with Deep Learning," Papers 2404.18207, arXiv.org.
    29. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    30. Martin Huber & Jannis Kueck, 2022. "Testing the identification of causal effects in observational data," Papers 2203.15890, arXiv.org, revised Jun 2023.
    31. Yingheng Zhang & Haojie Li & Gang Ren, 2025. "Data-driven exploration of heterogeneous gasoline price elasticities using generalized random forests," Transportation, Springer, vol. 52(1), pages 215-237, February.
    32. Kaul, Rupali & Anderson, Stephen J. & Chintagunta, Pradeep K. & Vilcassim, Naufel, 2025. "Call me maybe: does customer feedback seeking impact nonsolicited customers?," LSE Research Online Documents on Economics 125615, London School of Economics and Political Science, LSE Library.
    33. Patrick Rehill, 2024. "How do applied researchers use the Causal Forest? A methodological review of a method," Papers 2404.13356, arXiv.org, revised Dec 2024.
    34. Lin Li & Kecheng Wei & Jiliang Han & Yuchun Zhu, 2025. "Digital empowerment and the development resilience in rural households: causal inference based on double machine learning," Empirical Economics, Springer, vol. 69(3), pages 1187-1227, September.
    35. Paul B. Ellickson & Wreetabrata Kar & James C. Reeder, 2023. "Estimating Marketing Component Effects: Double Machine Learning from Targeted Digital Promotions," Marketing Science, INFORMS, vol. 42(4), pages 704-728, July.
    36. Phillip Heiler & Michael C. Knaus, 2021. "Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments," Papers 2110.01427, arXiv.org, revised Aug 2023.
    37. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    38. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    39. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    40. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    41. Vishalie Shah & Julia Hatamyar & Taufik Hidayat & Noemi Kreif, 2025. "Exploring the heterogeneous impacts of Indonesia's conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests," Papers 2501.12803, arXiv.org.
    42. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org, revised Oct 2025.
    43. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    44. Daniel Goller & Andrea Diem & Stefan C. Wolter, 2022. "Sitting next to a dropout: Study success of students with peers that came to the lecture hall by a different route," Economics of Education Working Paper Series 0190, University of Zurich, Department of Business Administration (IBW).
    45. Abdul-Nasah Soale & Emmanuel Selorm Tsyawo, 2023. "Clustered Covariate Regression," Papers 2302.09255, arXiv.org, revised Jul 2025.
    46. Semenova, Vira, 2025. "Generalized Lee bounds," Journal of Econometrics, Elsevier, vol. 251(C).
    47. Achim Ahrens & Victor Chernozhukov & Christian Hansen & Damian Kozbur & Mark Schaffer & Thomas Wiemann, 2025. "An Introduction to Double/Debiased Machine Learning," Papers 2504.08324, arXiv.org, revised Feb 2026.
    48. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Apr 2025.
    49. Patrick Rehill, 2024. "Distilling interpretable causal trees from causal forests," Papers 2408.01023, arXiv.org.
    50. Franziska Zimmert & Michael Zimmert, 2024. "Part‐time subsidies and maternal reemployment: Evidence from a difference‐in‐differences analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(6), pages 1149-1171, September.
    51. Abhinandan Dalal & Eric J. Tchetgen Tchetgen, 2025. "Partial Identification of Causal Effects for Endogenous Continuous Treatments," Papers 2508.13946, arXiv.org.
    52. Adam Baybutt & Manu Navjeevan, 2023. "Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls," Papers 2301.06283, arXiv.org.
    53. Hui Lan & Vasilis Syrgkanis, 2023. "Causal Q-Aggregation for CATE Model Selection," Papers 2310.16945, arXiv.org, revised Apr 2025.
    54. Amilcar Velez, 2024. "On the Asymptotic Properties of Debiased Machine Learning Estimators," Papers 2411.01864, arXiv.org.
    55. Bonev, Petyo & Matsumoto, Shigeru, 2022. "An empirical evaluation of environmental Alternative Dispute Resolution methods," Economics Working Paper Series 2208, University of St. Gallen, School of Economics and Political Science.
    56. Kyle Myers & Wei Yang Tham, 2023. "Money, Time, and Grant Design," Papers 2312.06479, arXiv.org.
    57. Patrick Rehill & Nicholas Biddle, 2024. "Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning," Papers 2401.07075, arXiv.org, revised Mar 2024.
    58. Lucas Z. Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org, revised Dec 2025.
    59. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.

  15. Daron Acemoglu & Victor Chernozhukov & Iván Werning & Michael D. Whinston, 2021. "Optimal Targeted Lockdowns in a Multigroup SIR Model," American Economic Review: Insights, American Economic Association, vol. 3(4), pages 487-502, December.
    See citations under working paper version above.
  16. Victor Chernozhukov & Kaspar Wüthrich & Yinchu Zhu, 2021. "An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1849-1864, October.
    See citations under working paper version above.
  17. Victor Chernozhukov & Alfred Galichon & Marc Henry & Brendan Pass, 2021. "Identification of Hedonic Equilibrium and Nonseparable Simultaneous Equations," Journal of Political Economy, University of Chicago Press, vol. 129(3), pages 842-870.
    See citations under working paper version above.
  18. Victor Chernozhukov & Hiroyuki Kasahara & Paul Schrimpf, 2021. "The association of opening K–12 schools with the spread of COVID-19 in the United States: County-level panel data analysis," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(42), pages 2103420118-, October.
    See citations under working paper version above.
  19. Victor Chernozhukov & Iván Fernández‐Val & Whitney Newey & Sami Stouli & Francis Vella, 2020. "Semiparametric estimation of structural functions in nonseparable triangular models," Quantitative Economics, Econometric Society, vol. 11(2), pages 503-533, May.
    See citations under working paper version above.
  20. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly & Kaspar Wüthrich, 2020. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 123-137, January.
    See citations under working paper version above.
  21. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Fernández-Val, Iván, 2019. "Conditional quantile processes based on series or many regressors," Journal of Econometrics, Elsevier, vol. 213(1), pages 4-29.
    See citations under working paper version above.
  22. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Aureo de Paula, 2019. "Inference on Causal and Structural Parameters using Many Moment Inequalities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(5), pages 1867-1900.
    See citations under working paper version above.
  23. Victor Chernozhukov & Ivan Fernández-Val & Sukjin Han & Amanda Kowalski, 2019. "Censored quantile instrumental-variable estimation with Stata," Stata Journal, StataCorp LLC, vol. 19(4), pages 768-781, December.
    See citations under working paper version above.
  24. Chernozhukov, Victor & Fernández-Val, Iván & Newey, Whitney K., 2019. "Nonseparable multinomial choice models in cross-section and panel data," Journal of Econometrics, Elsevier, vol. 211(1), pages 104-116.
    See citations under working paper version above.
  25. Shuowen Chen & Victor Chernozhukov & Iván Fernández-Val, 2019. "Mastering Panel Metrics: Causal Impact of Democracy on Growth," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 77-82, May.
    See citations under working paper version above.
  26. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    See citations under working paper version above.
  27. Patrick Bajari & Victor Chernozhukov & Ali Hortaçsu & Junichi Suzuki, 2019. "The Impact of Big Data on Firm Performance: An Empirical Investigation," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 33-37, May.
    See citations under working paper version above.
  28. Victor Chernozhukov & Iván Fernández‐Val & Ye Luo, 2018. "The Sorted Effects Method: Discovering Heterogeneous Effects Beyond Their Averages," Econometrica, Econometric Society, vol. 86(6), pages 1911-1938, November.
    See citations under working paper version above.
  29. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    See citations under working paper version above.
  30. Carlier, Guillaume & Chernozhukov, Victor & Galichon, Alfred, 2017. "Vector quantile regression beyond the specified case," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 96-102.

    Cited by:

    1. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Sciences Po Economics Publications (main) hal-03936221, HAL.
    2. Guillaume Carlier & Victor Chernozhukov & Gwendoline De Bie & Alfred Galichon, 2022. "Vector quantile regression and optimal transport, from theory to numerics," Empirical Economics, Springer, vol. 62(1), pages 35-62, January.
    3. Balcilar, Mehmet & Ozdemir, Zeynel Abidin & Ozdemir, Huseyin & Wohar, Mark E., 2020. "Transmission of US and EU Economic Policy Uncertainty Shock to Asian Economies in Bad and Good Times," IZA Discussion Papers 13274, IZA Network @ LISER.
    4. Marc Hallin, 2021. "Measure Transportation and Statistical Decision Theory," Working Papers ECARES 2021-04, ULB -- Universite Libre de Bruxelles.
    5. Nadja Klein & Torsten Hothorn & Luisa Barbanti & Thomas Kneib, 2022. "Multivariate conditional transformation models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 116-142, March.
    6. Hongjian Shi & Mathias Drton & Marc Hallin & Fang Han, 2023. "Semiparametrically Efficient Tests of Multivariate Independence Using Center-Outward Quadrant, Spearman, and Kendall Statistics," Working Papers ECARES 2023-03, ULB -- Universite Libre de Bruxelles.
    7. Donald Lien & Zijun Wang, 2019. "Quantile information share," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(1), pages 38-55, January.
    8. Alfred Galichon, 2021. "The Unreasonable Effectiveness of Optimal Transport in Economics," Working Papers hal-03936221, HAL.
    9. Alfred Galichon, 2021. "The unreasonable effectiveness of optimal transport in economics," Papers 2107.04700, arXiv.org.

  31. A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017. "Program Evaluation and Causal Inference With High‐Dimensional Data," Econometrica, Econometric Society, vol. 85, pages 233-298, January.
    See citations under working paper version above.
  32. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey, 2017. "Double/Debiased/Neyman Machine Learning of Treatment Effects," American Economic Review, American Economic Association, vol. 107(5), pages 261-265, May.

    Cited by:

    1. TELLO, Mario D., 2024. "Inversión Pública En Infraestructura Y Crecimiento Regional En Perú, 2005-2020: Un Análisis Basado En Técnicas De Aprendizaje Automático Causal," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 24(2), pages 195-222.
    2. Hadi Movaghari & Serafeim Tsoukas & Evangelos Vagenas‐Nanos, 2025. "Corporate cash policy and double machine learning," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(3), pages 3261-3279, July.
    3. Xing, Lu & Han, DongHao & Hui, Xie, 2023. "The impact of carbon policy on corporate risk-taking with a double/debiased machine learning based difference-in-differences approach," Finance Research Letters, Elsevier, vol. 58(PC).
    4. Delprato, Marcos & Frola, Alessia & Antequera, Germán, 2022. "Indigenous and non-Indigenous proficiency gaps for out-of-school and in-school populations: A machine learning approach," International Journal of Educational Development, Elsevier, vol. 93(C).
    5. Xiong, Ruoxuan & Koenecke, Allison & Powell, Michael & Shen, Zhu & Vogelstein, Joshua T. & Athey, Susan, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Research Papers 3990, Stanford University, Graduate School of Business.
    6. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Tinbergen Institute Discussion Papers 21-001/V, Tinbergen Institute.
    7. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    8. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    9. Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    10. Oliver Hines & Stijn Vansteelandt & Karla Diaz-Ordaz, 2021. "Robust Inference for Mediated Effects in Partially Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 86(2), pages 595-618, June.
    11. Jong Hee Park & Byung Koo Kim, 2020. "Why your neighbor matters: Positions in preferential trade agreement networks and export growth in global value chains," Economics and Politics, Wiley Blackwell, vol. 32(3), pages 381-410, November.
    12. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    13. Vira Semenova, 2017. "Debiased Machine Learning of Set-Identified Linear Models," Papers 1712.10024, arXiv.org, revised Dec 2022.
    14. Ziwei Cong & Jia Liu & Puneet Manchanda, 2021. "The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest," Papers 2107.01629, arXiv.org, revised Sep 2022.
    15. Amit Sharma & Emre Kiciman, 2020. "DoWhy: An End-to-End Library for Causal Inference," Papers 2011.04216, arXiv.org.
    16. Feng, Sanying & Kong, Kaidi & Kong, Yinfei & Li, Gaorong & Wang, Zhaoliang, 2022. "Statistical inference of heterogeneous treatment effect based on single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    17. Miquel Oliu-Barton & Bary S R Pradelski & Nicolas Woloszko & Lionel Guetta-Jeanrenaud & Philippe Aghion & Patrick Artus & Arnaud Fontanet & Philippe Martin & Guntram B Wolff, 2022. "The Effect of COVID Certificates on Vaccine Uptake, Health Outcomes, and the Economy," Post-Print hal-03813557, HAL.
    18. Victor Chernozhukov & Whitney Newey & Vira Semenova, 2019. "Welfare Analysis in Dynamic Models," Papers 1908.09173, arXiv.org, revised May 2025.
    19. Waverly Wei & Maya Petersen & Mark J van der Laan & Zeyu Zheng & Chong Wu & Jingshen Wang, 2023. "Efficient targeted learning of heterogeneous treatment effects for multiple subgroups," Biometrics, The International Biometric Society, vol. 79(3), pages 1934-1946, September.
    20. Jinyong Hahn & Jerry Hausman, 2021. "Problems with the Control Variable Approach in Achieving Unbiased Estimates in Nonlinear Models in the Presence of Many Instruments," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 39-58, December.
    21. Sven Klaassen & Jannis Kuck & Martin Spindler & Victor Chernozhukov, 2018. "Uniform Inference in High-Dimensional Gaussian Graphical Models," Papers 1808.10532, arXiv.org, revised Dec 2018.
    22. Bihua Zhou & Yun Huang & Hang Su & Jingshan Wang, 2025. "Mechanism conflicts: carbon reduction pathways and optimization in China’s Big Data Policy," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-15, December.
    23. Isaac Meza, 2025. "Residual Balancing for Non-Linear Outcome Models in High Dimensions," Papers 2511.00324, arXiv.org.
    24. Wang, Hongning & Ma, Sanjun, 2022. "Preventing crimes against public health with artificial intelligence and machine learning capabilities," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    25. Bruno Paolo Bosco & Paolo Maranzano, 2025. "Vis Inertiae and Statistical Inference: A Review of Difference-in-Differences Methods Employed in Economics and Other Subjects," Econometrics, MDPI, vol. 13(4), pages 1-56, September.
    26. Yong Bian & Xiqian Wang & Qin Zhang, 2023. "How Does China's Household Portfolio Selection Vary with Financial Inclusion?," Papers 2311.01206, arXiv.org.
    27. Betts,Alexander Milton Stedman & Stierna,Maria Flinder & Omata,Naohiko & Sterck,Olivier Christian Brigitte, 2022. "Social Cohesion and Refugee-Host Interactions : Evidence from East Africa," Policy Research Working Paper Series 9917, The World Bank.
    28. Monica Andini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Viola Salvestrini, 2017. "Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy," Temi di discussione (Economic working papers) 1158, Bank of Italy, Economic Research and International Relations Area.
    29. Chenyu Hou, 2023. "Learning and Subjective Expectation Formation: A Recurrent Neural Network Approach," Discussion Papers dp23-13, Department of Economics, Simon Fraser University.
    30. Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
    31. Bobonis, Gustavo & Gertler, Paul & Gonzalez-Navarro, Marco & Nichter, Simeon, 2023. "Does Combating Corruption Reduce Clientelism?," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt13k514pd, Department of Agricultural & Resource Economics, UC Berkeley.
    32. Hirofumi Fukuyama & Mike Tsionas & Yong Tan, 2024. "Incorporating causal modeling into data envelopment analysis for performance evaluation," Annals of Operations Research, Springer, vol. 342(3), pages 1865-1904, November.
    33. Sven Klaassen & Jannis Kueck & Martin Spindler, 2017. "Transformation Models in High-Dimensions," Papers 1712.07364, arXiv.org.
    34. Helmut Wasserbacher & Martin Spindler, 2022. "Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls," Digital Finance, Springer, vol. 4(1), pages 63-88, March.
    35. Jikai Jin & Vasilis Syrgkanis, 2024. "Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation," Papers 2402.14264, arXiv.org, revised Jun 2025.
    36. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
    37. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2023. "Source Condition Double Robust Inference on Functionals of Inverse Problems," Papers 2307.13793, arXiv.org.
    38. Vira Semenova, 2018. "Machine Learning for Dynamic Discrete Choice," Papers 1808.02569, arXiv.org, revised Nov 2018.
    39. Anna Baiardi & Andrea A. Naghi, 2024. "The effect of plough agriculture on gender roles: A machine learning approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1396-1402, November.
    40. Carl Bonander & Mikael Svensson, 2021. "Using causal forests to assess heterogeneity in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 30(8), pages 1818-1832, August.
    41. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    42. Valente, Marica, 2023. "Policy evaluation of waste pricing programs using heterogeneous causal effect estimation," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    43. Jeonghwan Lee & Cong Ma, 2025. "Learning bounds for doubly-robust covariate shift adaptation," Papers 2511.11003, arXiv.org.
    44. Maria Cuellar & Edward H. Kennedy, 2020. "A non‐parametric projection‐based estimator for the probability of causation, with application to water sanitation in Kenya," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1793-1818, October.
    45. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
    46. Anna Baiardi & Andrea A. Naghi, 2021. "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies," Papers 2101.00878, arXiv.org.
    47. Iván Díaz & Nicholas Williams & Katherine L. Hoffman & Nima S. Hejazi, 2025. "Author correction to: “causal survival analysis under competing risks using longitudinal modified treatment policies”," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 31(2), pages 442-471, April.
    48. Su, Xiaoying & Zha, Donglan & Zhang, Chaoqun & Zhao, Kuokuo, 2025. "Does household lifestyle impact the decarbonization effect of carbon generalized system of preferences? Evidence from household survey in China," Journal of Asian Economics, Elsevier, vol. 98(C).
    49. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    50. Kroczek, Martin & Kugler, Philipp, 2024. "Heterogeneous effects of monetary and non-monetary job characteristics on job attractiveness in nursing," Labour Economics, Elsevier, vol. 91(C).
    51. Anna Baiardi & Andrea A Naghi, 2024. "The value added of machine learning to causal inference: evidence from revisited studies," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 213-234.
    52. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    53. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    54. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers CWP41/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    55. Julia Hatamyar & Noemi Kreif, 2023. "Policy Learning with Rare Outcomes," Papers 2302.05260, arXiv.org, revised Oct 2023.
    56. Yang, Zixuan & Yu, Huang, 2025. "Unleashing the power of Energy Saving and Emission Reduction Fiscal Policy: Balancing urban ecological resilience and efficiency," Energy, Elsevier, vol. 327(C).
    57. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    58. Wang, Xiqian & Bian, Yong & Zhang, Qin, 2023. "The effect of cooking fuel choice on the elderly’s well-being: Evidence from two non-parametric methods," Energy Economics, Elsevier, vol. 125(C).
    59. Enji Li & Ziwei An & Chen Zhang & Hua Li, 2022. "Impact of economic growth target constraints on enterprise technological innovation: Evidence from China," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-21, August.
    60. Elliott Ash & Daniel L. Chen & Sergio Galletta, 2022. "Measuring Judicial Sentiment: Methods and Application to US Circuit Courts," Economica, London School of Economics and Political Science, vol. 89(354), pages 362-376, April.
    61. Heiler, Phillip & Kazak, Ekaterina, 2021. "Valid inference for treatment effect parameters under irregular identification and many extreme propensity scores," Journal of Econometrics, Elsevier, vol. 222(2), pages 1083-1108.
    62. Pradhi Aggarwal & Alec Brandon & Ariel Goldszmidt & Justin Holz & John List & Ian Muir & Gregory Sun & Thomas Yu, 2022. "High-frequency location data shows that race affects the likelihood of being stopped and fined for speeding," Natural Field Experiments 00764, The Field Experiments Website.
    63. Songul Cinaroglu, 2020. "Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 168-181, October.
    64. Jiaming Mao & Zhesheng Zheng, 2020. "Structural Regularization," Papers 2004.12601, arXiv.org, revised Jun 2020.
    65. Matt Goldman & Brian Quistorff, 2018. "Pricing Engine: Estimating Causal Impacts in Real World Business Settings," Papers 1806.03285, arXiv.org, revised Jun 2018.
    66. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
    67. Miruna Oprescu & Vasilis Syrgkanis & Zhiwei Steven Wu, 2018. "Orthogonal Random Forest for Causal Inference," Papers 1806.03467, arXiv.org, revised Sep 2019.
    68. Gareth Liu-Evans & Shalini Mitra, 2023. "Formal sector enforcement and welfare," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(3), pages 706-728, June.
    69. Jiaming Mao & Jingzhi Xu, 2020. "Ensemble Learning with Statistical and Structural Models," Papers 2006.05308, arXiv.org.
    70. Mochen Yang & Edward McFowland & Gordon Burtch & Gediminas Adomavicius, 2022. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 138-155, October.
    71. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2025. "Robust Semiparametric Inference for Bayesian Additive Regression Trees," Papers 2509.24634, arXiv.org, revised Oct 2025.
    72. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    73. Fernando Delbianco & Fernando Tohmé, 2023. "Individualized Conformal," Working Papers 247, Red Nacional de Investigadores en Economía (RedNIE).
    74. Sander Gerritsen & Mark Kattenberg & Sonny Kuijpers, 2019. "The impact of age at arrival on education and mental health," CPB Discussion Paper 389, CPB Netherlands Bureau for Economic Policy Analysis.
    75. Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021. "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers 2112.13398, arXiv.org, revised May 2024.
    76. Jiang, Fuyang & Kazmi, Hussain, 2025. "What-if: A causal machine learning approach to control-oriented modelling for building thermal dynamics," Applied Energy, Elsevier, vol. 377(PC).
    77. Su, Liangjun & Ura, Takuya & Zhang, Yichong, 2019. "Non-separable models with high-dimensional data," Journal of Econometrics, Elsevier, vol. 212(2), pages 646-677.
    78. Fukuyama, Hirofumi & Tsionas, Mike & Tan, Yong, 2023. "Dynamic network data envelopment analysis with a sequential structure and behavioural-causal analysis: Application to the Chinese banking industry," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1360-1373.
    79. Andrew Bennett & Nathan Kallus & Xiaojie Mao & Whitney Newey & Vasilis Syrgkanis & Masatoshi Uehara, 2022. "Inference on Strongly Identified Functionals of Weakly Identified Functions," Papers 2208.08291, arXiv.org, revised Jun 2023.
    80. Gazeaud, Jules & Khan, Nausheen & Mvukiyehe, Eric & Sterck, Olivier, 2023. "With or without him? Experimental evidence on cash grants and gender-sensitive trainings in Tunisia," Journal of Development Economics, Elsevier, vol. 165(C).
    81. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    82. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
    83. Kan Chen & Qishuo Yin & Qi Long, 2025. "Covariate-Balancing-Aware Interpretable Deep Learning Models for Treatment Effect Estimation," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 17(1), pages 132-150, April.
    84. Derksen, Laura & Kerwin, Jason Theodore & Reynoso, Natalia Ordaz & Sterck, Olivier, 2021. "Appointments: A More Effective Commitment Device for Health Behaviors," SocArXiv y8gh7, Center for Open Science.
    85. Iván Díaz & Katherine L. Hoffman & Nima S. Hejazi, 2024. "Causal survival analysis under competing risks using longitudinal modified treatment policies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(1), pages 213-236, January.
    86. Max Cytrynbaum, 2021. "Optimal Stratification of Survey Experiments," Papers 2111.08157, arXiv.org, revised Aug 2023.
    87. Dongcheng Zhang & Kunpeng Zhang, 2020. "Weighting-Based Treatment Effect Estimation via Distribution Learning," Papers 2012.13805, arXiv.org, revised May 2023.
    88. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
    89. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    90. Alexander Buchholz & Vito Bellini & Giuseppe Di Benedetto & Yannik Stein & Matteo Ruffini & Fabian Moerchen, 2022. "Fair Effect Attribution in Parallel Online Experiments," Papers 2210.08338, arXiv.org.
    91. Andreas Fuster & Paul Goldsmith‐Pinkham & Tarun Ramadorai & Ansgar Walther, 2022. "Predictably Unequal? The Effects of Machine Learning on Credit Markets," Journal of Finance, American Finance Association, vol. 77(1), pages 5-47, February.
    92. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges," Management Science, INFORMS, vol. 66(6), pages 2495-2522, June.
    93. Mei, Ziwei & Shi, Zhentao, 2024. "On LASSO for high dimensional predictive regression," Journal of Econometrics, Elsevier, vol. 242(2).
    94. Uribe, Jorge M., 2025. "Investment in intangible assets and economic complexity," Research Policy, Elsevier, vol. 54(1).
    95. Heigle, Julia & Pfeiffer, Friedhelm, 2019. "An analysis of selected labor market outcomes of college dropouts in Germany: A machine learning estimation approach. Research report," ZEW Expertises, ZEW - Leibniz Centre for European Economic Research, number 222378.
    96. Ruonan Cai & Wencan Tian & Rundong Luo & Zhichao Fang & Zhigang Hu, 2025. "Do articles with multiple corresponding authorships have a citation advantage? A double machine learning analysis approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(5), pages 2523-2550, May.
    97. Krantz, Sebastian, 2024. "Mapping Africa's infrastructure potential with geospatial big data and causal ML," Kiel Working Papers 2276, Kiel Institute for the World Economy.
    98. Bonaccolto-Töpfer, Marina & Satlukal, Sascha, 2024. "Gender differences in reservation wages: New evidence for Germany," Labour Economics, Elsevier, vol. 91(C).
    99. Qiu, Yong & Guo, Ziyang & Li, Junlong & Li, Biao & Qiao, Dan, 2024. "Impact of controlling shareholders’ equity pledges on firms’ labour investment efficiency," Finance Research Letters, Elsevier, vol. 70(C).
    100. Stephan Martin, 2022. "Estimation of Conditional Random Coefficient Models using Machine Learning Techniques," Papers 2201.08366, arXiv.org.
    101. Belloni, Alexandre & Hansen, Christian & Newey, Whitney, 2022. "High-dimensional linear models with many endogenous variables," Journal of Econometrics, Elsevier, vol. 228(1), pages 4-26.
    102. Falco J. Bargagli Stoffi & Kenneth De Beckker & Joana E. Maldonado & Kristof De Witte, 2021. "Assessing Sensitivity of Machine Learning Predictions.A Novel Toolbox with an Application to Financial Literacy," Papers 2102.04382, arXiv.org.
    103. Ashkan Ertefaie & Nima S. Hejazi & Mark J. van der Laan, 2023. "Nonparametric inverse‐probability‐weighted estimators based on the highly adaptive lasso," Biometrics, The International Biometric Society, vol. 79(2), pages 1029-1041, June.
    104. Athey, Susan & Imbens, Guido W. & Metzger, Jonas & Munro, Evan, 2024. "Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations," Journal of Econometrics, Elsevier, vol. 240(2).
    105. Guber, Raphael, 2018. "Instrument Validity Tests with Causal Trees: With an Application to the Same-sex Instrument," MEA discussion paper series 201805, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    106. Pang, Silu & Hua, Guihong, 2024. "How does digital tax administration affect R&D manipulation? Evidence from dual machine learning," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    107. Esfandiar Maasoumi & Jianqiu Wang & Zhuo Wang & Ke Wu, 2024. "Identifying factors via automatic debiased machine learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 438-461, April.
    108. Retsef Levi & Elisabeth Paulson & Georgia Perakis & Emily Zhang, 2024. "Heterogeneous Treatment Effects in Panel Data," Papers 2406.05633, arXiv.org.
    109. Betts, Alexander & Flinder Stierna, Maria & Omata, Naohiko & Sterck, Olivier, 2023. "Refugees welcome? Inter-group interaction and host community attitude formation," World Development, Elsevier, vol. 161(C).
    110. Fang, Yan & Liu, Yinglin & Yang, Yi & Lucey, Brian & Abedin, Mohammad Zoynul, 2025. "How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning," Research in International Business and Finance, Elsevier, vol. 74(C).
    111. Abadie, Alberto & Gu, Jiaying & Shen, Shu, 2024. "Instrumental variable estimation with first-stage heterogeneity," Journal of Econometrics, Elsevier, vol. 240(2).
    112. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2024. "Semiparametric Bayesian Difference-in-Differences," Papers 2412.04605, arXiv.org, revised Jun 2025.
    113. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    114. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.
    115. Ke Sun & Linglong Kong & Hongtu Zhu & Chengchun Shi, 2024. "ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments," Papers 2408.05342, arXiv.org, revised Jan 2025.
    116. Guido Imbens, 2019. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," NBER Working Papers 26104, National Bureau of Economic Research, Inc.
    117. Whitney K. Newey & James M. Robins, 2017. "Cross-fitting and fast remainder rates for semiparametric estimation," CeMMAP working papers 41/17, Institute for Fiscal Studies.
    118. Jushan Bai & Sung Hoon Choi & Yuan Liao, 2021. "Feasible generalized least squares for panel data with cross-sectional and serial correlations," Empirical Economics, Springer, vol. 60(1), pages 309-326, January.
    119. Johannes Jakubik & Stefan Feuerriegel, 2022. "Data‐driven allocation of development aid toward sustainable development goals: Evidence from HIV/AIDS," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2739-2756, June.

  33. Alexandre Belloni & Victor Chernozhukov & Ying Wei, 2016. "Post-Selection Inference for Generalized Linear Models With Many Controls," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 606-619, October.
    See citations under working paper version above.
  34. , & , & ,, 2016. "Fragility of asymptotic agreement under Bayesian learning," Theoretical Economics, Econometric Society, vol. 11(1), January.
    See citations under working paper version above.
  35. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2016. "Inference in High-Dimensional Panel Models With an Application to Gun Control," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 590-605, October.
    See citations under working paper version above.
  36. Chernozhukov, Victor & Chetverikov, Denis & Kato, Kengo, 2016. "Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings," Stochastic Processes and their Applications, Elsevier, vol. 126(12), pages 3632-3651.
    See citations under working paper version above.
  37. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    See citations under working paper version above.
  38. Victor Chernozhukov & Emre Kocatulum & Konrad Menzel, 2015. "Inference on sets in finance," Quantitative Economics, Econometric Society, vol. 6(2), pages 309-358, July.
    See citations under working paper version above.
  39. Belloni, Alexandre & Chernozhukov, Victor & Chetverikov, Denis & Kato, Kengo, 2015. "Some new asymptotic theory for least squares series: Pointwise and uniform results," Journal of Econometrics, Elsevier, vol. 186(2), pages 345-366.
    See citations under working paper version above.
  40. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," American Economic Review, American Economic Association, vol. 105(5), pages 486-490, May.
    See citations under working paper version above.
  41. Chernozhukov, Victor & Fernández-Val, Iván & Hoderlein, Stefan & Holzmann, Hajo & Newey, Whitney, 2015. "Nonparametric identification in panels using quantiles," Journal of Econometrics, Elsevier, vol. 188(2), pages 378-392.
    See citations under working paper version above.
  42. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 649-688, August.
    See citations under working paper version above.
  43. Victor Chernozhukov & Wooyoung Kim & Sokbae Lee & Adam M. Rosen, 2015. "Implementing intersection bounds in Stata," Stata Journal, StataCorp LLC, vol. 15(1), pages 21-44, March.
    See citations under working paper version above.
  44. A. Belloni & V. Chernozhukov & K. Kato, 2015. "Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems," Biometrika, Biometrika Trust, vol. 102(1), pages 77-94.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Jelena Bradic & Weijie Ji & Yuqian Zhang, 2021. "High-dimensional Inference for Dynamic Treatment Effects," Papers 2110.04924, arXiv.org, revised May 2023.
    3. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    5. Victor Chernozhukov & Chen Huang & Weining Wang, 2021. "Uniform Inference on High-dimensional Spatial Panel Networks," Papers 2105.07424, arXiv.org, revised Jul 2025.
    6. Yukun Ma, 2023. "Identification-robust inference for the LATE with high-dimensional covariates," Papers 2302.09756, arXiv.org, revised Nov 2025.
    7. Han, Dongxiao & Huang, Jian & Lin, Yuanyuan & Shen, Guohao, 2022. "Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 230(2), pages 416-431.
    8. Victor Chernozhukov & Whitney Newey & Rahul Singh, 2018. "De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers," Papers 1802.08667, arXiv.org, revised Oct 2022.
    9. Ekaterina Seregina, 2020. "A Basket Half Full: Sparse Portfolios," Papers 2011.04278, arXiv.org, revised Apr 2021.
    10. Deniz, Pinar & Stengos, Thanasis, 2025. "Heterogeneity of institutions and model uncertainty in the income inequality nexus," European Journal of Political Economy, Elsevier, vol. 87(C).
    11. Jelena Bradic & Victor Chernozhukov & Whitney K. Newey & Yinchu Zhu, 2019. "Minimax Semiparametric Learning With Approximate Sparsity," Papers 1912.12213, arXiv.org, revised Jul 2025.
    12. Victor Chernozhukov & Whitney K. Newey & Victor Quintas-Martinez & Vasilis Syrgkanis, 2021. "Automatic Debiased Machine Learning via Riesz Regression," Papers 2104.14737, arXiv.org, revised Mar 2024.
    13. Kai Xu & Nan An, 2024. "A tuning-free efficient test for marginal linear effects in high-dimensional quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(1), pages 93-110, February.
    14. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2017. "Quantile graphical models: prediction and conditional independence with applications to systemic risk," CeMMAP working papers CWP54/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Harold D Chiang & Yukun Ma & Joel Rodrigue & Yuya Sasaki, 2021. "Dyadic double/debiased machine learning for analyzing determinants of free trade agreements," Papers 2110.04365, arXiv.org, revised Dec 2022.
    16. Lina Liao & Cheolwoo Park & Hosik Choi, 2019. "Penalized expectile regression: an alternative to penalized quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 409-438, April.
    17. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey & James Robins, 2017. "Double/debiased machine learning for treatment and structural parameters," CeMMAP working papers 28/17, Institute for Fiscal Studies.
    18. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Yuta Koike, 2022. "High-dimensional Data Bootstrap," Papers 2205.09691, arXiv.org.
    19. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers CWP36/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    20. Harold D. Chiang & Kengo Kato & Yukun Ma & Yuya Sasaki, 2019. "Multiway Cluster Robust Double/Debiased Machine Learning," Papers 1909.03489, arXiv.org, revised Mar 2020.
    21. Lee, Ji Hyung & Shi, Zhentao & Gao, Zhan, 2022. "On LASSO for predictive regression," Journal of Econometrics, Elsevier, vol. 229(2), pages 322-349.
    22. van de Geer, Sara, 2016. "Worst possible sub-directions in high-dimensional models," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 248-260.
    23. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    24. Roger Koenker, 2017. "Quantile regression 40 years on," CeMMAP working papers 36/17, Institute for Fiscal Studies.
    25. Ali Charkhi & Gerda Claeskens, 2018. "Asymptotic post-selection inference for the Akaike information criterion," Biometrika, Biometrika Trust, vol. 105(3), pages 645-664.
    26. Georg Keilbar & Juan M. Rodriguez-Poo & Alexandra Soberon & Weining Wang, 2022. "A projection based approach for interactive fixed effects panel data models," Papers 2201.11482, arXiv.org, revised Feb 2025.
    27. Philipp Bach & Victor Chernozhukov & Martin Spindler, 2019. "Valid simultaneous inference in high-dimensional settings (with the HDM package for R)," CeMMAP working papers CWP30/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    28. Harold D. Chiang, 2018. "Many Average Partial Effects: with An Application to Text Regression," Papers 1812.09397, arXiv.org, revised Jan 2022.
    29. Victor Chernozhukov & Whitney K Newey & Rahul Singh, 2018. "Automatic Debiased Machine Learning of Causal and Structural Effects," Papers 1809.05224, arXiv.org, revised Oct 2022.
    30. Jooyoung Cha & Harold D. Chiang & Yuya Sasaki, 2021. "Inference in high-dimensional regression models without the exact or $L^p$ sparsity," Papers 2108.09520, arXiv.org, revised Dec 2022.
    31. Xiduo Chen & Xingdong Feng & Antonio F. Galvao & Yeheng Ge, 2025. "Treatment Effects Inference with High-Dimensional Instruments and Control Variables," Papers 2503.20149, arXiv.org, revised Oct 2025.
    32. Victor Chernozhukov & Whitney K. Newey & James Robins, 2018. "Double/de-biased machine learning using regularized Riesz representers," CeMMAP working papers CWP15/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    33. Algo Carè & Simone Garatti & Marco C. Campi, 2017. "A coverage theory for least squares," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1367-1389, November.
    34. Harold D. Chiang, 2025. "Maximal Inequalities for Separately Exchangeable Empirical Processes," Papers 2502.11432, arXiv.org, revised Mar 2025.
    35. Dai, Wei & Tsang, Ka Wai, 2023. "A resampling approach for confidence intervals in linear time-series models after model selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

  45. Alexandre Belloni & Victor Chernozhukov, 2014. "Posterior inference in curved exponential families under increasing dimensions," Econometrics Journal, Royal Economic Society, vol. 17(2), pages 75-100, June.
    See citations under working paper version above.
  46. Xiaohong Chen & Victor Chernozhukov & Sokbae Lee & Whitney K. Newey, 2014. "Local Identification of Nonparametric and Semiparametric Models," Econometrica, Econometric Society, vol. 82(2), pages 785-809, March.
    See citations under working paper version above.
  47. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.

    Cited by:

    1. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2018. "Synthetic Difference in Differences," Papers 1812.09970, arXiv.org, revised Jul 2021.
    2. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    3. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    4. Achim Ahrens & Sean Lyons, 2021. "Do rising rents lead to longer commutes? A gravity model of commuting flows in Ireland," Urban Studies, Urban Studies Journal Limited, vol. 58(2), pages 264-279, February.
    5. Joel L. Horowitz & Ahnaf Rafi, 2023. "Bootstrap based asymptotic refinements for high-dimensional nonlinear models," Papers 2303.09680, arXiv.org, revised Feb 2024.
    6. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
    7. TELLO, Mario D., 2024. "Inversión Pública En Infraestructura Y Crecimiento Regional En Perú, 2005-2020: Un Análisis Basado En Técnicas De Aprendizaje Automático Causal," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 24(2), pages 195-222.
    8. Hadi Movaghari & Serafeim Tsoukas & Evangelos Vagenas‐Nanos, 2025. "Corporate cash policy and double machine learning," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(3), pages 3261-3279, July.
    9. Abhijit Banerjee & Esther Duflo & Amy Finkelstein & Lawrence F. Katz & Benjamin A. Olken & Anja Sautmann, 2020. "In Praise of Moderation: Suggestions for the Scope and Use of Pre-Analysis Plans for RCTs in Economics," NBER Working Papers 26993, National Bureau of Economic Research, Inc.
    10. Georgia Papadogeorgou, 2022. "Discussion on “Spatial+: a novel approach to spatial confounding” by Emiko Dupont, Simon N. Wood, and Nicole H. Augustin," Biometrics, The International Biometric Society, vol. 78(4), pages 1305-1308, December.
    11. Deutschmann, Joshua W. & Duru, Maya & Siegal, Kim & Tjernström, Emilia, 2025. "Relaxing multiple agricultural productivity constraints at scale," Journal of Development Economics, Elsevier, vol. 174(C).
    12. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Julián Caballero & Christian Upper, 2023. "What happens to EMEs when US yields go up?," BIS Working Papers 1081, Bank for International Settlements.
    14. Zhong, Wei & Gao, Yang & Zhou, Wei & Fan, Qingliang, 2021. "Endogenous treatment effect estimation using high-dimensional instruments and double selection," Statistics & Probability Letters, Elsevier, vol. 169(C).
    15. Belloni, Alexandre & Chen, Mingli & Chernozhukov, Victor, "undated". "Quantile Graphical Models : Prediction and Conditional Independence with Applications to Financial Risk Management," Economic Research Papers 269321, University of Warwick - Department of Economics.
    16. Bryzgalova, Svetlana & Huang, Jiantao & Julliard, Christian, 2023. "Bayesian solutions for the factor zoo: we just ran two quadrillion models," LSE Research Online Documents on Economics 126151, London School of Economics and Political Science, LSE Library.
    17. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    18. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    19. Meles, Tensay Hadush & Lokina, Razack & Mtenga, Erica Louis & Tibanywana, Julieth Julius, 2023. "Stated preferences with survey consequentiality and outcome uncertainty: A split sample discrete choice experiment," EfD Discussion Paper 23-16, Environment for Development, University of Gothenburg.
    20. Micevska, Maja, 2021. "Revisiting forced migration: A machine learning perspective," European Journal of Political Economy, Elsevier, vol. 70(C).
    21. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    22. Elyasiani, Elyas & Movaghari, Hadi, 2022. "Determinants of corporate cash holdings: An application of a robust variable selection technique," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 967-993.
    23. Martin Huber, 2019. "An introduction to flexible methods for policy evaluation," Papers 1910.00641, arXiv.org.
    24. Uckat, Hannah Irmela, 2023. "Leaning in at Home : Women's Promotions and Intra-household Bargaining in Bangladesh," Policy Research Working Paper Series 10370, The World Bank.
    25. Hongwei Shi & Weichao Yang & Bowen Sun & Xu Guo, 2025. "Tests for high-dimensional partially linear regression models," Statistical Papers, Springer, vol. 66(3), pages 1-23, April.
    26. Fremstad, Anders, 2017. "Does Craigslist Reduce Waste? Evidence from California and Florida," Ecological Economics, Elsevier, vol. 132(C), pages 135-143.
    27. Olaf J de Groot & Carlos Bozzoli & Anousheh Alamir & Tilman Brück, 2022. "The global economic burden of violent conflict," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 259-276, March.
    28. Vira Semenova & Matt Goldman & Victor Chernozhukov & Matt Taddy, 2023. "Inference on heterogeneous treatment effects in high‐dimensional dynamic panels under weak dependence," Quantitative Economics, Econometric Society, vol. 14(2), pages 471-510, May.
    29. Franz Huber & Alan Ponce & Francesco Rentocchini & Thomas Wainwright, 2020. "The Wealth of (Open Data) Nations? Examining the interplay of open government data and country-level institutions for entrepreneurial activity at the country-level," SEEDS Working Papers 1120, SEEDS, Sustainability Environmental Economics and Dynamics Studies, revised Nov 2020.
    30. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    31. Bellmann, Lutz & Hübler, Olaf, 2019. "Personal Attitudes, Job Characteristics and Health," IZA Discussion Papers 12597, IZA Network @ LISER.
    32. Anastasiya-Mariya Asanov & Igor Asanov & Guido Buenstorf & Valon Kadriu & Pia Schoch, 2024. "Patterns of dissertation dissemination: publication-based outcomes of doctoral theses in the social sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(4), pages 2389-2405, April.
    33. Janzen, Sarah A. & Magnan, Nicholas & Mullally, Conner C. & Sharma, Shruti, 2021. "Training and Shifting Gender Norms: Evidence from a training intervention in rural Nepal," 2021 Annual Meeting, August 1-3, Austin, Texas 314065, Agricultural and Applied Economics Association.
    34. Lagarde, Mylène & Riumallo Herl, Carlos, 2025. "Better together? Group incentives and the demand for prevention," Journal of Development Economics, Elsevier, vol. 172(C).
    35. Peter C. B. Phillips & Zhentao Shi, 2021. "Boosting: Why You Can Use The Hp Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(2), pages 521-570, May.
    36. Anaka Aiyar & Naveen Sunder, 2024. "Health insurance and child mortality: Evidence from India," Health Economics, John Wiley & Sons, Ltd., vol. 33(5), pages 870-893, May.
    37. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damages," Working Papers 2020-003, The George Washington University, The Center for Economic Research.
    38. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2016. "Quantile Graphical Models: Prediction and Conditional Independence with Applications to Systemic Risk," Papers 1607.00286, arXiv.org, revised Oct 2019.
    39. Qingliang Fan & Zijian Guo & Ziwei Mei & Cun-Hui Zhang, 2023. "Inference for Nonlinear Endogenous Treatment Effects Accounting for High-Dimensional Covariate Complexity," Papers 2310.08063, arXiv.org, revised Jun 2024.
    40. Kaicheng Chen, 2025. "Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity," Papers 2504.18772, arXiv.org, revised Dec 2025.
    41. Harrison Fell & Melinda Sandler Morrill, 2024. "The Impact of Wind Energy on Air Pollution and Emergency Department Visits," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(1), pages 287-320, January.
    42. Victor Chernozhukov & Wolfgang Härdle & Chen Huang & Weining Wang, 2019. "LASSO-Driven Inference in Time and Space," CeMMAP working papers CWP20/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    43. Zhihao Zhao & Xinyu Zhang & Guohua Zou & Alan T. K. Wan & Geoffrey K. F. Tso, 2024. "Model averaging for estimating treatment effects," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(1), pages 73-92, February.
    44. Byunghoon Kang, 2017. "Inference in Nonparametric Series Estimation with Data-Dependent Undersmoothing," Working Papers 170712442, Lancaster University Management School, Economics Department.
    45. Jelena Bradic & Stefan Wager & Yinchu Zhu, 2019. "Sparsity Double Robust Inference of Average Treatment Effects," Papers 1905.00744, arXiv.org.
    46. Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2022. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," Management Science, INFORMS, vol. 68(11), pages 7918-7939, November.
    47. Bastien Michel & Samuel Kembou & Sonali Wayal & Joanna Murray, 2023. "Leveraging Mobile Phone Expansion in LMICs to Improve Parental Practices," Working Papers hal-03909663, HAL.
    48. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2019. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany’s programmes for long term unemployed," Economics Working Paper Series 1910, University of St. Gallen, School of Economics and Political Science.
    49. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
    50. Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
    51. Gravina, Antonio Francesco & Lanzafame, Matteo, 2025. "‘What's your shape?’ A data-driven approach to estimating the Environmental Kuznets Curve," Energy Economics, Elsevier, vol. 148(C).
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    457. Oliveira, Alessandro V.M. & Oliveira, Bruno F. & Vassallo, Moisés D., 2023. "Airport service quality perception and flight delays: Examining the influence of psychosituational latent traits of respondents in passenger satisfaction surveys," Research in Transportation Economics, Elsevier, vol. 102(C).
    458. Gaduh,Arya Budhiastra & Pradhan,Menno Prasad & Priebe,Jan & Susanti,Dewi, 2021. "Scores, Camera, Action : Social Accountability and Teacher Incentives in Remote Areas," Policy Research Working Paper Series 9748, The World Bank.
    459. Siu, Jade & Sterck, Olivier & Rodgers, Cory, 2023. "The freedom to choose: Theory and quasi-experimental evidence on cash transfer restrictions," Journal of Development Economics, Elsevier, vol. 161(C).
    460. David Bruns-Smith & Oliver Dukes & Avi Feller & Elizabeth L. Ogburn, 2023. "Augmented balancing weights as linear regression," Papers 2304.14545, arXiv.org, revised Jun 2024.
    461. Koen van Ruijven & Joep Tijm, 2022. "Do people value environmental goods? Evidence from the Netherlands," CPB Discussion Paper 438, CPB Netherlands Bureau for Economic Policy Analysis.
    462. Sandro Heiniger, 2024. "Data-driven model selection within the matrix completion method for causal panel data models," Papers 2402.01069, arXiv.org.
    463. Janjala Chirakijja & Seema Jayachandran & Pinchuan Ong, 2023. "The Mortality Effects of Winter Heating Prices," Working Papers 305, Princeton University, Department of Economics, Center for Economic Policy Studies..
    464. Michael Danquah & Solomon Owusu, 2021. "Digital technology and productivity of informal enterprises: Empirical evidence from Nigeria," WIDER Working Paper Series wp-2021-114, World Institute for Development Economic Research (UNU-WIDER).
    465. Harold D. Chiang & Joel Rodrigue & Yuya Sasaki, 2019. "Post-Selection Inference in Three-Dimensional Panel Data," Papers 1904.00211, arXiv.org, revised Apr 2019.
    466. Arugay,Aries & Donald, Aletheia Amalia & Jarvis, Forest Brach & Johnson, Hillary C. & Valenciano,Aletheia, 2024. "When Does Decision-Making Reflect Agency ? Evidence from the Rural Philippines," Policy Research Working Paper Series 10851, The World Bank.
    467. Raf Van Gestel & Niels Broekman & Tobias Müller, 2023. "Surgeon supply and healthcare quality: Are revision rates for hip and knee replacements lower in hospitals that employ more surgeons?," Health Economics, John Wiley & Sons, Ltd., vol. 32(10), pages 2298-2321, October.

  48. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    See citations under working paper version above.
  49. V. Chernozhukov & C. Hansen, 2013. "Quantile Models with Endogeneity," Annual Review of Economics, Annual Reviews, vol. 5(1), pages 57-81, May.
    See citations under working paper version above.
  50. Victor Chernozhukov & Iván Fernández‐Val & Jinyong Hahn & Whitney Newey, 2013. "Average and Quantile Effects in Nonseparable Panel Models," Econometrica, Econometric Society, vol. 81(2), pages 535-580, March.
    See citations under working paper version above.
  51. Victor Chernozhukov & Sokbae Lee & Adam M. Rosen, 2013. "Intersection Bounds: Estimation and Inference," Econometrica, Econometric Society, vol. 81(2), pages 667-737, March.
    See citations under working paper version above.
  52. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    See citations under working paper version above.
  53. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    See citations under working paper version above.
  54. Victor Chernozhukov & Iván Fernández-Val, 2011. "Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(2), pages 559-589.
    See citations under working paper version above.
  55. A. Belloni & V. Chernozhukov & L. Wang, 2011. "Square-root lasso: pivotal recovery of sparse signals via conic programming," Biometrika, Biometrika Trust, vol. 98(4), pages 791-806.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    3. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    4. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    5. Kaicheng Chen, 2025. "Inference in High-Dimensional Panel Models: Two-Way Dependence and Unobserved Heterogeneity," Papers 2504.18772, arXiv.org, revised Dec 2025.
    6. Anders Bredahl Kock, 2013. "Oracle inequalities for high-dimensional panel data models," CREATES Research Papers 2013-20, Department of Economics and Business Economics, Aarhus University.
    7. Alexandre Belloni & Victor Chernozhukov & Lie Wang, 2013. "Pivotal estimation via square-root lasso in nonparametric regression," CeMMAP working papers 62/13, Institute for Fiscal Studies.
    8. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    9. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Central limit theorems and multiplier bootstrap when p is much larger than n," CeMMAP working papers CWP45/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2019. "lassopack: Model selection and prediction with regularized regression in Stata," Papers 1901.05397, arXiv.org.
    11. Anindya Bhadra & Jyotishka Datta & Nicholas G. Polson & Brandon T. Willard, 2020. "Global-Local Mixtures: A Unifying Framework," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 426-447, August.
    12. Laura Freijeiro‐González & Manuel Febrero‐Bande & Wenceslao González‐Manteiga, 2022. "A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates," International Statistical Review, International Statistical Institute, vol. 90(1), pages 118-145, April.
    13. Sardy, Sylvain & Diaz-Rodriguez, Jairo & Giacobino, Caroline, 2022. "Thresholding tests based on affine LASSO to achieve non-asymptotic nominal level and high power under sparse and dense alternatives in high dimension," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    14. Beyhum, Jad, 2019. "Inference robust to outliers with L1‐norm penalization," TSE Working Papers 19-1032, Toulouse School of Economics (TSE).
    15. Shi, Zhentao, 2016. "Econometric estimation with high-dimensional moment equalities," Journal of Econometrics, Elsevier, vol. 195(1), pages 104-119.
    16. Mohamed Ouhourane & Yi Yang & Andréa L. Benedet & Karim Oualkacha, 2022. "Group penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 495-529, September.
    17. Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2013. "Program evaluation with high-dimensional data," CeMMAP working papers 57/13, Institute for Fiscal Studies.
    18. Zhaonan Qu & Yongchan Kwon, 2024. "Distributionally Robust Instrumental Variables Estimation," Papers 2410.15634, arXiv.org, revised Dec 2024.
    19. Corinne Emmenegger & Peter Bühlmann, 2023. "Plug‐in machine learning for partially linear mixed‐effects models with repeated measurements," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(4), pages 1553-1567, December.
    20. Kaixu Yang & Tapabrata Maiti, 2022. "Ultrahigh‐dimensional generalized additive model: Unified theory and methods," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 917-942, September.
    21. Daisuke Ikeda & Mayumi Ojima & Koji Takahashi, 2019. "Financial Interconnectedness, Amplification, and Cross-Border Activity," Bank of Japan Working Paper Series 19-E-11, Bank of Japan.
    22. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    23. Mehmet Caner & Anders Bredahl Kock, 2013. "Oracle Inequalities for Convex Loss Functions with Non-Linear Targets," CREATES Research Papers 2013-51, Department of Economics and Business Economics, Aarhus University.
    24. Jiang, He & Luo, Shihua & Dong, Yao, 2021. "Simultaneous feature selection and clustering based on square root optimization," European Journal of Operational Research, Elsevier, vol. 289(1), pages 214-231.
    25. Fan, Xianqiu & Cheng, Jun & Wang, Hailing & Zhang, Bin & Chen, Zhenzhen, 2024. "A fast trans-lasso algorithm with penalized weighted score function," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    26. Koenker, Roger & Mizera, Ivan, 2014. "Convex Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i05).
    27. Zemin Zheng & Jie Zhang & Yang Li, 2022. "L 0 -Regularized Learning for High-Dimensional Additive Hazards Regression," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2762-2775, September.
    28. Umberto Amato & Anestis Antoniadis & Italia De Feis & Irene Gijbels, 2021. "Penalised robust estimators for sparse and high-dimensional linear models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 1-48, March.
    29. Victor Chernozhukov & Christian Hansen & Yuan Liao, 2015. "A lava attack on the recovery of sums of dense and sparse signals," CeMMAP working papers CWP56/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    30. Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
    31. Sokbae (Simon) Lee & Yuan Liao & Myung Hwan Seo & Youngki Shin, 2020. "Sparse HP filter: Finding kinks in the COVID-19 contact rate," CeMMAP working papers CWP32/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    32. Wu, Xiaofei & Liang, Rongmei & Zhang, Zhimin & Cui, Zhenyu, 2025. "A unified consensus-based parallel algorithm for high-dimensional regression with combined regularizations," Computational Statistics & Data Analysis, Elsevier, vol. 203(C).
    33. Jokubaitis, Saulius & Celov, Dmitrij & Leipus, Remigijus, 2021. "Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run," International Journal of Forecasting, Elsevier, vol. 37(2), pages 759-776.
    34. Xie, Jichun & Kang, Jian, 2017. "High-dimensional tests for functional networks of brain anatomic regions," Journal of Multivariate Analysis, Elsevier, vol. 156(C), pages 70-88.
    35. Mert Hakan Hekimoğlu & Burak Kazaz, 2020. "Analytics for Wine Futures: Realistic Prices," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2096-2120, September.
    36. Saulius Jokubaitis & Remigijus Leipus, 2022. "Asymptotic Normality in Linear Regression with Approximately Sparse Structure," Mathematics, MDPI, vol. 10(10), pages 1-28, May.
    37. Susan Athey & Guido W. Imbens & Stefan Wager, 2016. "Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions," Papers 1604.07125, arXiv.org, revised Jan 2018.
    38. Chao, Shih-Kang & Härdle, Wolfgang Karl & Yuan, Ming, 2015. "Factorisable sparse tail event curves," SFB 649 Discussion Papers 2015-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    39. Yao Dong & He Jiang, 2018. "A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model," Complexity, Hindawi, vol. 2018, pages 1-12, November.
    40. Zhu, Ying, 2013. "Sparse Linear Models and Two-Stage Estimation in High-Dimensional Settings with Possibly Many Endogenous Regressors," MPRA Paper 49846, University Library of Munich, Germany.
    41. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    42. Alexandre Belloni & Mingli Chen & Victor Chernozhukov, 2017. "Quantile graphical models: prediction and conditional independence with applications to systemic risk," CeMMAP working papers CWP54/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    43. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019. "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers 1902.10991, arXiv.org, revised Dec 2020.
    44. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    45. Xie, Fang & Xu, Lihu & Yang, Youcai, 2017. "Lasso for sparse linear regression with exponentially β-mixing errors," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 64-70.
    46. Jacob Bien & Irina Gaynanova & Johannes Lederer & Christian L. Müller, 2019. "Prediction error bounds for linear regression with the TREX," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 451-474, June.
    47. Zemin Zheng & Jinchi Lv & Wei Lin, 2021. "Nonsparse Learning with Latent Variables," Operations Research, INFORMS, vol. 69(1), pages 346-359, January.
    48. Federico Crescenzi, 2023. "Hedonic pricing modelling with unstructured predictors: an application to Italian Fashion Industry," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(4), pages 733-753, December.
    49. Adam Nowak & Patrick Smith, 2015. "Textual Analysis in Real Estate," Working Papers 15-34, Department of Economics, West Virginia University.
    50. Gaglianone, Wagner Piazza & Issler, João Victor, 2015. "Microfounded forecasting," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 766, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    51. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Papers 1312.7186, arXiv.org, revised Jun 2016.
    52. Quoc Tran-Dinh, 2019. "Proximal alternating penalty algorithms for nonsmooth constrained convex optimization," Computational Optimization and Applications, Springer, vol. 72(1), pages 1-43, January.
    53. Patric Müller & Sara Geer, 2015. "The Partial Linear Model in High Dimensions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 580-608, June.
    54. Simon B chler, Maximilian v. Ehrlich, 2021. "Quantifying Land Use Regulation and its Determinants - Ease of Residential Development across Swiss Municipalities," Diskussionsschriften credresearchpaper32, Universitaet Bern, Departement Volkswirtschaft - CRED.
    55. Achim Ahrens & Arnab Bhattacharjee, 2015. "Two-Step Lasso Estimation of the Spatial Weights Matrix," Econometrics, MDPI, vol. 3(1), pages 1-28, March.
    56. Olga Klopp, 2012. "Noisy Low-rank Matrix Completion with General Sampling Distribution," Working Papers 2012-06, Center for Research in Economics and Statistics.
    57. Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
    58. Mingrui Zhong & Zanhua Yin & Zhichao Wang, 2023. "Variable Selection for Sparse Logistic Regression with Grouped Variables," Mathematics, MDPI, vol. 11(24), pages 1-21, December.
    59. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2013. "Robust inference in high-dimensional approximately sparse quantile regression models," CeMMAP working papers CWP70/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    60. Ismail Shah & Hina Naz & Sajid Ali & Amani Almohaimeed & Showkat Ahmad Lone, 2023. "A New Quantile-Based Approach for LASSO Estimation," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    61. Ayed M. Alrashdi & Meshari Alazmi & Masad A. Alrasheedi, 2023. "Generalized Penalized Constrained Regression: Sharp Guarantees in High Dimensions with Noisy Features," Mathematics, MDPI, vol. 11(17), pages 1-27, August.
    62. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
    63. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    64. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    65. T. Tony Cai & Zijian Guo & Yin Xia, 2023. "Statistical inference and large-scale multiple testing for high-dimensional regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(4), pages 1135-1171, December.
    66. Jana Janková & Rajen D. Shah & Peter Bühlmann & Richard J. Samworth, 2020. "Goodness‐of‐fit testing in high dimensional generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 773-795, July.
    67. van de Geer, Sara, 2016. "Worst possible sub-directions in high-dimensional models," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 248-260.
    68. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney K. Newey, 2016. "Double machine learning for treatment and causal parameters," CeMMAP working papers CWP49/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    69. Sophie Brana & Dalila Chenaf-Nicet & Delphine Lahet, 2023. "Drivers of cross-border bank claims: The role of foreign-owned banks in emerging countries," Working Papers 2023.06, International Network for Economic Research - INFER.
    70. Sujeong Park & David Powell, 2020. "Is the Rise in Illicit Opioids Affecting Labor Supply and Disability Claiming Rates?," NBER Working Papers 27804, National Bureau of Economic Research, Inc.
    71. Olga Klopp, 2012. "High Dimensional Matrix Estimation With Unknown Variance Of The Noise," Working Papers 2012-05, Center for Research in Economics and Statistics.
    72. Alexandre Belloni & Victor Chernozhukov & Abhishek Kaul & Mathieu Rosenbaum & Alexandre B. Tsybakov, 2017. "Pivotal Estimation Via Self-Normalization for High-Dimensional Linear Models with Errors in Variables," Working Papers 2017-26, Center for Research in Economics and Statistics.
    73. Jonas Peters & Peter Bühlmann & Nicolai Meinshausen, 2016. "Causal inference by using invariant prediction: identification and confidence intervals," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 947-1012, November.
    74. Xi Chen & Ye Luo & Martin Spindler, 2019. "Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data," Papers 1912.12867, arXiv.org, revised Jan 2020.
    75. Aur'elien Ouattara & Matthieu Bult'e & Wan-Ju Lin & Philipp Scholl & Benedikt Veit & Christos Ziakas & Florian Felice & Julien Virlogeux & George Dikos, 2021. "Scalable Econometrics on Big Data -- The Logistic Regression on Spark," Papers 2106.10341, arXiv.org.
    76. Loann David Denis Desboulets, 2020. "Sparse Manifolds Graphical Modelling with Missing Values: An Application to the Commodity Futures Market," Working Papers hal-02986982, HAL.
    77. Koike, Yuta & Tanoue, Yuta, 2019. "Oracle inequalities for sign constrained generalized linear models," Econometrics and Statistics, Elsevier, vol. 11(C), pages 145-157.
    78. Saulius Jokubaitis & Dmitrij Celov & Remigijus Leipus, 2019. "Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run," Papers 1906.07992, arXiv.org, revised Oct 2020.
    79. Ben Gillen & Erik Snowberg & Leeat Yariv, 2015. "Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study," NBER Working Papers 21517, National Bureau of Economic Research, Inc.
    80. Wu, Xiaofei & Ming, Hao & Zhang, Zhimin & Cui, Zhenyu, 2024. "Multi-block alternating direction method of multipliers for ultrahigh dimensional quantile fused regression," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    81. Xiaofei Wu & Rongmei Liang & Hu Yang, 2022. "Penalized and constrained LAD estimation in fixed and high dimension," Statistical Papers, Springer, vol. 63(1), pages 53-95, February.
    82. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Inference for high-dimensional sparse econometric models," CeMMAP working papers CWP41/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    83. Zhang Haixiang & Zheng Yinan & Yoon Grace & Zhang Zhou & Gao Tao & Joyce Brian & Zhang Wei & Schwartz Joel & Vokonas Pantel & Colicino Elena & Baccarelli Andrea & Hou Lifang & Liu Lei, 2017. "Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(3), pages 159-171, August.
    84. Eric Gautier & Alexandre B, Tsybakov, 2013. "Pivotal Estimation in High-Dimensional Regression via Linear Programming," Working Papers 2013-40, Center for Research in Economics and Statistics.
    85. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011. "Estimation of treatment effects with high-dimensional controls," CeMMAP working papers CWP42/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    86. Sermpinis, Georgios & Tsoukas, Serafeim & Zhang, Ping, 2018. "Modelling market implied ratings using LASSO variable selection techniques," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 19-35.
    87. Wanling Xie & Hu Yang, 2023. "Group sparse recovery via group square-root elastic net and the iterative multivariate thresholding-based algorithm," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(3), pages 469-507, September.
    88. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2018. "Deep Neural Networks for Estimation and Inference," Papers 1809.09953, arXiv.org, revised Sep 2019.
    89. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Demand Estimation with Machine Learning and Model Combination," NBER Working Papers 20955, National Bureau of Economic Research, Inc.
    90. Jana Janková & Sara Geer, 2017. "Honest confidence regions and optimality in high-dimensional precision matrix estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 143-162, March.
    91. Pun, Chi Seng & Hadimaja, Matthew Zakharia, 2021. "A self-calibrated direct approach to precision matrix estimation and linear discriminant analysis in high dimensions," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    92. Kieran Elmes & Astra Heywood & Zhiyi Huang & Alex Gavryushkin, 2022. "A fast lasso-based method for inferring higher-order interactions," PLOS Computational Biology, Public Library of Science, vol. 18(12), pages 1-23, December.
    93. Quoc Tran-Dinh, 2017. "Adaptive smoothing algorithms for nonsmooth composite convex minimization," Computational Optimization and Applications, Springer, vol. 66(3), pages 425-451, April.
    94. Fan, Jianqing & Feng, Yang & Xia, Lucy, 2020. "A projection-based conditional dependence measure with applications to high-dimensional undirected graphical models," Journal of Econometrics, Elsevier, vol. 218(1), pages 119-139.
    95. Kock, Anders Bredahl & Callot, Laurent, 2015. "Oracle inequalities for high dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 186(2), pages 325-344.
    96. Barbara Guardabascio & Filippo Moauro & Luke Mosley, 2024. "Indirect estimation of the monthly transport turnover indicator in Italy," Empirical Economics, Springer, vol. 67(2), pages 531-566, August.
    97. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
    98. Alexander Jaax & Annabelle Mourougane & Frederic Gonzales, 2024. "Nowcasting services trade for the G7 economies," The World Economy, Wiley Blackwell, vol. 47(4), pages 1336-1386, April.
    99. Zanhua Yin, 2020. "Variable selection for sparse logistic regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(7), pages 821-836, October.
    100. Wang, Lie, 2013. "The L1 penalized LAD estimator for high dimensional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 135-151.
    101. Alexis Derumigny, 2017. "Improved bounds for Square-Root Lasso and Square-Root Slope," Working Papers 2017-53, Center for Research in Economics and Statistics.
    102. Tianxi Cai & T. Tony Cai & Zijian Guo, 2021. "Optimal statistical inference for individualized treatment effects in high‐dimensional models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 669-719, September.

  56. Victor Chernozhukov & Iván Fernández-Val & Alfred Galichon, 2010. "Rearranging Edgeworth–Cornish–Fisher expansions," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 42(2), pages 419-435, February.
    See citations under working paper version above.
  57. Victor Chernozhukov & Roberto Rigobon & Thomas M. Stoker, 2010. "Set identification and sensitivity analysis with Tobin regressors," Quantitative Economics, Econometric Society, vol. 1(2), pages 255-277, November.

    Cited by:

    1. Yohanes Maria Vianey Mudayen & Lincolin Arsyad & Rimawan Pradiptyo & Sekar Utami Setiastuti, 2025. "Does Public Debt Encourage Economic Growth? An Application of Quantile Regressions to Panel Data for Developing Countries," Economies, MDPI, vol. 13(4), pages 1-31, April.
    2. Chalak, Karim & Kim, Daniel, 2020. "Measurement error in multiple equations: Tobin’s q and corporate investment, saving, and debt," Journal of Econometrics, Elsevier, vol. 214(2), pages 413-432.
    3. Kazi Musa & Kazi Sohag & Jamaliah Said & Farha Ghapar & Norli Ali, 2023. "Public Debt, Governance, and Growth in Developing Countries: An Application of Quantile via Moments," Mathematics, MDPI, vol. 11(3), pages 1-13, January.
    4. Karim Chalak, 2012. "Identification of Average Random Coefficients under Magnitude and Sign Restrictions on Confounding," Boston College Working Papers in Economics 816, Boston College Department of Economics.
    5. Juan Carlos Escanciano & Lin Zhu, 2013. "Set inferences and sensitivity analysis in semiparametric conditionally identified models," CeMMAP working papers 55/13, Institute for Fiscal Studies.
    6. Chalak, Karim, 2024. "Nonparametric Gini-Frisch bounds," Journal of Econometrics, Elsevier, vol. 238(1).
    7. Travis A. Smith, 2017. "Do School Food Programs Improve Child Dietary Quality?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(2), pages 339-356.
    8. Semenova, Vira, 2023. "Debiased machine learning of set-identified linear models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1725-1746.
    9. Semenova, Vira, 2025. "Generalized Lee bounds," Journal of Econometrics, Elsevier, vol. 251(C).
    10. Kazi Musa & Marijn Janssen & Jamaliah Said & Nor Balkish Zakaria & Naila Erum, 2025. "The Impact of Public Debt and Quality of Governance on Economic Growth in High-Income Countries," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 2817-2843, March.

  58. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    See citations under working paper version above.
  59. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.

    Cited by:

    1. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    2. Kaplan, David M. & Sun, Yixiao, 2012. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," University of California at San Diego, Economics Working Paper Series qt888657tp, Department of Economics, UC San Diego.
    3. Laffers, Lukas, 2013. "Identification in Models with Discrete Variables," Discussion Paper Series in Economics 1/2013, Norwegian School of Economics, Department of Economics.
    4. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    5. David M. Kaplan, 2013. "IDEAL Inference on Conditional Quantiles via Interpolated Duals of Exact Analytic L-statistics," Working Papers 1316, Department of Economics, University of Missouri.
    6. Chung, Ray S.W. & So, Mike K.P. & Chu, Amanda M.Y. & Chan, Thomas W.C., 2020. "Regularization of Bayesian quasi-likelihoods constructed from complex estimating functions," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    7. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    8. Jun, Sung Jae, 2008. "Weak identification robust tests in an instrumental quantile model," Journal of Econometrics, Elsevier, vol. 144(1), pages 118-138, May.
    9. Luger, Richard, 2012. "Finite-sample bootstrap inference in GARCH models with heavy-tailed innovations," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3198-3211.
    10. David M. Kaplan & Matt Goldman, 2013. "IDEAL Quantile Inference via Interpolated Duals of Exact Analytic L-statistics," Working Papers 1315, Department of Economics, University of Missouri.
    11. David M. Kaplan & Matt Goldman, 2015. "Nonparametric inference on conditional quantile differences and linear combinations, using L-statistics," Working Papers 1503, Department of Economics, University of Missouri.
    12. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP46/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Victor Chernozhukov & Christian Hansen, 2013. "Quantile models with endogeneity," CeMMAP working papers CWP25/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. David Kang & Seojeong Lee, 2025. "Misspecification-Robust Asymptotic and Bootstrap Inference for Nonsmooth GMM," Working Papers 423284005, Lancaster University Management School, Economics Department.
    15. Biying Yu & Junyi Zhang & Akimasa Fujiwara, 2016. "Who rebounds in the private transport sector? A comparative analysis between Beijing and Tokyo," Environment and Planning B, , vol. 43(3), pages 561-579, May.
    16. Victor Chernozhukov & Christian Hansen & Kaspar Wuthrich, 2020. "Instrumental Variable Quantile Regression," Papers 2009.00436, arXiv.org.
    17. Zhongjun Qu & Jungmo Yoon, 2011. "Nonparametric Estimation and Inference on Conditional Quantile Processes," Boston University - Department of Economics - Working Papers Series WP2011-059, Boston University - Department of Economics.
    18. Adam Rosen & Takuya Ura, 2020. "Finite Sample Inference for the Maximum Score Estimand," CeMMAP working papers CWP22/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Elise Coudin & Jean-Marie Dufour, 2010. "Finite and Large Sample Distribution-Free Inference in Median Regressions with Instrumental Variables," Working Papers 2010-56, Center for Research in Economics and Statistics.
    20. David M. Kaplan, 2020. "sivqr: Smoothed IV quantile regression," Working Papers 2009, Department of Economics, University of Missouri.
    21. David M. Kaplan, 2023. "Smoothed instrumental variables quantile regression," Papers 2310.09013, arXiv.org.
    22. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    23. Kaspar W thrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
    24. Otsu, Taisuke, 2008. "Conditional empirical likelihood estimation and inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 142(1), pages 508-538, January.
    25. Alexis Derumigny & Lucas Girard & Yannick Guyonvarch, 2025. "Can we have it all? Non-asymptotically valid and asymptotically exact confidence intervals for expectations and linear regressions," Papers 2507.16776, arXiv.org, revised Jul 2025.
    26. Hiroaki Kaido & Kaspar Wüthrich, 2018. "Decentralization estimators for instrumental variable quantile regression models," CeMMAP working papers CWP72/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    27. Zhongjun Qu & Jungmo Yoon, 2015. "Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs," Boston University - Department of Economics - Working Papers Series wp2015-009, Boston University - Department of Economics.
    28. Purevdorj Tuvaandorj, 2021. "Robust Permutation Tests in Linear Instrumental Variables Regression," Papers 2111.13774, arXiv.org, revised Jul 2024.
    29. Joel L. Horowitz, 2017. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers 46/17, Institute for Fiscal Studies.
    30. Joel L. Horowitz, 2018. "Non-Asymptotic Inference in Instrumental Variables Estimation," Papers 1809.03600, arXiv.org.
    31. Horowitz, Joel L., 2021. "Bounding the difference between true and nominal rejection probabilities in tests of hypotheses about instrumental variables models," Journal of Econometrics, Elsevier, vol. 222(2), pages 1057-1082.
    32. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    33. Su, Liangjun & Hoshino, Tadao, 2016. "Sieve instrumental variable quantile regression estimation of functional coefficient models," Journal of Econometrics, Elsevier, vol. 191(1), pages 231-254.
    34. Luciano de Castro & Antonio F. Galvao & David M. Kaplan, 2017. "Smoothed instrumental variables quantile regression, with estimation of quantile Euler equations," Working Papers 1710, Department of Economics, University of Missouri, revised 28 Feb 2018.
    35. Marina Dias & Demian Pouzo, 2021. "Inference for multi-valued heterogeneous treatment effects when the number of treated units is small," Papers 2105.10965, arXiv.org.
    36. Joel L. Horowitz, 2018. "Non-asymptotic inference in instrumental variables estimation," CeMMAP working papers CWP52/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    37. Gossner, Olivier & Schlag, Karl H., 2013. "Finite-sample exact tests for linear regressions with bounded dependent variables," Journal of Econometrics, Elsevier, vol. 177(1), pages 75-84.
    38. Isaiah Andrews & Anna Mikusheva, 2022. "Optimal Decision Rules for Weak GMM," Econometrica, Econometric Society, vol. 90(2), pages 715-748, March.

  60. V. Chernozhukov & I. Fernández-Val & A. Galichon, 2009. "Improving point and interval estimators of monotone functions by rearrangement," Biometrika, Biometrika Trust, vol. 96(3), pages 559-575.
    See citations under working paper version above.
  61. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Admissible Invariant Similar Tests For Instrumental Variables Regression," Econometric Theory, Cambridge University Press, vol. 25(3), pages 806-818, June.

    Cited by:

    1. Tetsuya Kaji, 2019. "Theory of Weak Identification in Semiparametric Models," Papers 1908.10478, arXiv.org, revised Aug 2020.
    2. Antoine, Bertille & Lavergne, Pascal, 2023. "Identification-robust nonparametric inference in a linear IV model," Journal of Econometrics, Elsevier, vol. 235(1), pages 1-24.
    3. Donald W. K. Andrews & Patrik Guggenberger, 2015. "Identification- and Singularity-Robust Inference for Moment Condition," Cowles Foundation Discussion Papers 1978R2, Cowles Foundation for Research in Economics, Yale University, revised Jan 2019.
    4. Régis Barnichon & Geert Mesters, 2019. "Identifying modern macro equations with old shocks," Economics Working Papers 1659, Department of Economics and Business, Universitat Pompeu Fabra.
    5. Marmer, Vadim & Yu, Zhengfei, 2015. "Efficient Inference in the Classical IV Regression Model with Weak Identification: Asymptotic Power Against Arbitrarily Large Deviations from the Null Hypothesis," Microeconomics.ca working papers vadim_marmer-2015-17, Vancouver School of Economics, revised 02 Sep 2015.
    6. Bertille Antoine & Pascal Lavergne, 2020. "Identification-Robust Nonparametric Interference in a Linear IV Model," Discussion Papers dp20-03, Department of Economics, Simon Fraser University.
    7. Kleibergen, Frank, 2021. "Efficient size correct subset inference in homoskedastic linear instrumental variables regression," Journal of Econometrics, Elsevier, vol. 221(1), pages 78-96.
    8. Mills, Benjamin & Moreira, Marcelo J. & Vilela, Lucas P., 2014. "Tests based on t-statistics for IV regression with weak instruments," Journal of Econometrics, Elsevier, vol. 182(2), pages 351-363.
    9. Dong Jin Lee, 2020. "Optimal tests for parameter breaking process in conditional quantile models," The Japanese Economic Review, Springer, vol. 71(3), pages 479-510, July.
    10. Moreira, Humberto Ataíde & Moreira, Marcelo J., 2013. "Contributions to the Theory of Optimal Tests," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 747, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    11. Giuseppe Cavaliere & Zeng-Hua Lu & Anders Rahbek & Yuhong Yang, 2021. "MinP Score Tests with an Inequality Constrained Parameter Space," Papers 2107.06089, arXiv.org.
    12. Aragón, Edilean Kleber da Silva Bejarano & Galvão, Ana Beatriz, 2023. "Shock-based inference on the Phillips curve with the cost channel," Economic Modelling, Elsevier, vol. 126(C).

  62. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.

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    1. João Santos Silva, 2019. "Quantile regression: Basics and recent advances," London Stata Conference 2019 27, Stata Users Group.
    2. Raghuram G. Rajan & Rodney Ramcharan, 2009. "Land and Credit: A Study of the Political Economy of Banking in the United States in the Early 20th Century," NBER Working Papers 15083, National Bureau of Economic Research, Inc.
    3. Galvao, Antonio F. & Montes-Rojas, Gabriel, 2015. "On the equivalence of instrumental variables estimators for linear models," Economics Letters, Elsevier, vol. 134(C), pages 13-15.
    4. Xu, Xiu & Wang, Weining & Shin, Yongcheol, 2020. "Dynamic Spatial Network Quantile Autoregression," IRTG 1792 Discussion Papers 2020-024, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    5. Chen, Songnian, 2018. "Sequential estimation of censored quantile regression models," Journal of Econometrics, Elsevier, vol. 207(1), pages 30-52.
    6. Michael L. Polemis, 2020. "A note on the estimation of competition-productivity nexus: a panel quantile approach," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(4), pages 663-676, December.
    7. Galina Besstremyannaya, 2014. "The adverse effects of value-based purchasing in health care: dynamic quantile regression with endogeneity," Discussion Papers 14-006, Stanford Institute for Economic Policy Research.
    8. Tae-Hwan Kim & Christophe Muller, 2020. "Inconsistency transmission and variance reduction in two-stage quantile regression," Post-Print hal-02084505, HAL.
    9. Mauricio S. de Carvalho & Patrícia C. Melo & Bruno T. Rocha & Isabel Proença & João de Abreu e Silva, 2025. "Market potential, road accessibility, and firm births: evidence from twenty years of road investment," Working Papers REM 2025/0364, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    10. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    11. Ozan Bakis & Nurhan Davutyan & Haluk Levent & Sezgin Polat, 2010. "External Returns to Higher Education in Turkey," Working Papers 517, Economic Research Forum, revised 04 Jan 2010.
    12. Erik Figueiredo & Luiz Renato Lima & Gianluca Orefice, 2016. "Migration and Regional Trade Agreements: A (New) Gravity Estimation," Review of International Economics, Wiley Blackwell, vol. 24(1), pages 99-125, February.
    13. Cizek, P., 2009. "Generalized Methods of Trimmed Moments," Other publications TiSEM 46607f30-95c0-430a-8ef9-2, Tilburg University, School of Economics and Management.
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    15. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
    16. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
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    25. M. Fort, 2012. "Unconditional and Conditional Quantile Treatment Effect: Identification Strategies and Interpretations," Working Papers wp857, Dipartimento Scienze Economiche, Universita' di Bologna.
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    28. Valizadeh, Pourya & Smith, Travis A., "undated". "Distributional Impacts of WIC on Dietary Quality of Children: Evidence from a Regression Discontinuity Design," 2018 Annual Meeting, August 5-7, Washington, D.C. 273904, Agricultural and Applied Economics Association.
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    37. Tommaso Gabrieli & Antonio F. Galvao, Jr. & Antonio F. Galvao, Jr., 2010. "Who Benefits from Reducing the Cost of Formality? Quantile Regression Discontinuity Analysis," Real Estate & Planning Working Papers rep-wp2010-11, Henley Business School, University of Reading.
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    40. Chunbei Wang & Le Wang, 2011. "Language Skills and the Earnings Distribution Among Child Immigrants," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 50(2), pages 297-322, April.
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    44. Laurent-Fabrice Ambassa & Honoré Bidiasse, 2025. "Gender Norms and Female Labor Supply in Sub-Saharan Africa: A Dynamic Panel Quantile Regression Analysis," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(2), pages 9512-9535, June.
    45. Jinping Zhang & Qiuru Lu & Li Guan & Xiaoying Wang, 2021. "Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China," Energies, MDPI, vol. 14(2), pages 1-17, January.
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    52. Andrés Rodríguez-Pose & Alexandra Sotiriou, 2021. "Trading with richer and poorer countries: trade integration and regional inequality in Greece," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 67(3), pages 697-725, December.
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    93. Jim Been & Susann Rohwedder & Michael Hurd, 2021. "Households’ joint consumption spending and home production responses to retirement in the US," Review of Economics of the Household, Springer, vol. 19(4), pages 959-985, December.
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    Cited by:

    1. Andini, Corrado, 2017. "Tertiary Education for All and Wage Inequality: Policy Insights from Quantile Regression," IZA Policy Papers 132, Institute of Labor Economics (IZA).
    2. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    3. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. WenShwo Fang & Stephen M. Miller & Chih-Chuan Yeh, 2009. "Does a Threshold Inflation Rate Exist? Quantile Inferences for Inflation and Its Variability," Working Papers 0921, University of Nevada, Las Vegas , Department of Economics, revised Dec 2009.
    5. Jean-Jacques Forneron, 2019. "Detecting Identification Failure in Moment Condition Models," Papers 1907.13093, arXiv.org, revised Oct 2023.
    6. Graham, Bryan S. & Hahn, Jinyong & Poirier, Alexandre & Powell, James L., 2018. "A quantile correlated random coefficients panel data model," Journal of Econometrics, Elsevier, vol. 206(2), pages 305-335.
    7. Santiago Budría, 2011. "Are Educational Mismatches Responsible for the ‘Inequality Increasing Effect’ of Education?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 102(3), pages 409-437, July.
    8. Marilena Furno, 2020. "Returns to Education and Gender Wage Gap Across Quantiles in Italy," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(2), pages 145-169, June.
    9. Le-Yu Chen & Yu-Min Yen, 2021. "Estimations of the Local Conditional Tail Average Treatment Effect," Papers 2109.08793, arXiv.org, revised May 2024.
    10. Arabsheibani, Reza & Staneva, Anita, 2012. "Returns to Education in Russia: Where There Is Risky Sexual Behaviour There Is Also an Instrument," IZA Discussion Papers 6726, IZA Network @ LISER.
    11. Domenico Depalo & Raffaela Giordano, 2011. "The public-private pay gap: a robust quantile approach," Temi di discussione (Economic working papers) 824, Bank of Italy, Economic Research and International Relations Area.
    12. Wehby George L. & McCarthy Ann Marie & Castilla Eduardo & Murray Jeffrey C., 2011. "The Impact of Household Investments on Early Child Neurodevelopment and on Racial and Socioeconomic Developmental Gaps: Evidence from South America," Forum for Health Economics & Policy, De Gruyter, vol. 14(1), pages 1-60, December.
    13. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    14. Wehby, George L. & Murray, Jeffrey C. & Wilcox, Allen & Lie, Rolv T., 2012. "Smoking and body weight: Evidence using genetic instruments," Economics & Human Biology, Elsevier, vol. 10(2), pages 113-126.
    15. Kaspar W thrich, 2014. "A Comparison of two Quantile Models with Endogeneity," Diskussionsschriften dp1408, Universitaet Bern, Departement Volkswirtschaft.
    16. Wang, Le, 2011. "How Does Education Affect the Earnings Distribution in Urban China?," IZA Discussion Papers 6173, IZA Network @ LISER.
    17. George L. Wehby & Jeffrey C. Murray & Eduardo E. Castilla & Jorge S. Lopez‐Camelo & Robert L. Ohsfeldt, 2009. "Quantile effects of prenatal care utilization on birth weight in Argentina," Health Economics, John Wiley & Sons, Ltd., vol. 18(11), pages 1307-1321, November.
    18. Morra, Wayne & Hearn, Gail & Buck, Andrew J., 2009. "The market for bushmeat: Colobus Satanas on Bioko Island," Ecological Economics, Elsevier, vol. 68(10), pages 2619-2626, August.
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    20. Oliver Himmler, 2009. "The Effects of School Competition on Academic Achievement and Grading Standards," CESifo Working Paper Series 2676, CESifo.
    21. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
    22. Luis Fernando Gamboa & Mauricio Rodr�guez Acosta & Andr�s Garc�a Suaza, 2013. "Differences in motivations and academic achievement," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 78, pages 9-44.

  65. Chernozhukov, Victor & Imbens, Guido W. & Newey, Whitney K., 2007. "Instrumental variable estimation of nonseparable models," Journal of Econometrics, Elsevier, vol. 139(1), pages 4-14, July.

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    1. Depalo, D.;, 2019. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health, Econometrics and Data Group (HEDG) Working Papers 19/13, HEDG, c/o Department of Economics, University of York.
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    3. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
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    6. Xiaohong Chen & Demian Pouzo, 2009. "Efficient estimation of semiparametric conditional moment models with possibly nonsmooth residuals," CeMMAP working papers CWP20/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Joel L. Horowitz & Sokbae (Simon) Lee, 2010. "Uniform confidence bands for functions estimated nonparametrically with instrumental variables," CeMMAP working papers CWP19/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Andrew Chesher, 2007. "Endogeneity and discrete outcomes," CeMMAP working papers CWP05/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Sasaki, Yuya, 2015. "What Do Quantile Regressions Identify For General Structural Functions?," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1102-1116, October.
    10. Xiaohong Chen & Markus Reiss, 2007. "On Rate Optimality for Ill-posed Inverse Problems in Econometrics," Cowles Foundation Discussion Papers 1626, Cowles Foundation for Research in Economics, Yale University.
    11. Ben Deaner & Soonwoo Kwon, 2025. "Extrapolation in Regression Discontinuity Design Using Comonotonicity," Papers 2507.00289, arXiv.org.
    12. Chernozhukov, Victor & Hansen, Christian & Jansson, Michael, 2009. "Finite sample inference for quantile regression models," Journal of Econometrics, Elsevier, vol. 152(2), pages 93-103, October.
    13. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    14. Fusejima, Koki, 2024. "Identification of multi-valued treatment effects with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 238(1).
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    25. Victor Chernozhukov & Christian Hansen, 2013. "Quantile models with endogeneity," CeMMAP working papers CWP25/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    26. Senay Sokullu, 2012. "Nonparametric Analysis of Two-Sided Markets," Bristol Economics Discussion Papers 12/628, School of Economics, University of Bristol, UK.
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    37. Suqin Ge & João Macieira, 2024. "Unobserved Worker Quality and Inter‐Industry Wage Differentials," Journal of Industrial Economics, Wiley Blackwell, vol. 72(1), pages 459-515, March.
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    39. Jia-Young Michael Fu & Joel L. Horowitz & Matthias Parey, 2015. "Testing exogeneity in nonparametric instrumental variables identified by conditional quantile restrictions," CeMMAP working papers CWP68/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    40. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Post-Print hal-02618513, HAL.
    41. Xiaohong Chen & Yingyao Hu, 2006. "Identification and Inference of Nonlinear Models Using Two Samples with Arbitrary Measurement Errors," Cowles Foundation Discussion Papers 1590, Cowles Foundation for Research in Economics, Yale University.
    42. Ta-Wei Huang & Eva Ascarza, 2024. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals," Marketing Science, INFORMS, vol. 43(4), pages 863-884, July.
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    44. Dunker , Fabian & Florens, Jean-Pierre & Hohage, Thorsten & Johannes, Jan & Mammen, Enno, 2013. "Iterative estimation of solutions to noisy nonlinear operator equations in nonparametric instrumental regression," LIDAM Discussion Papers ISBA 2013041, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    45. Tae-Hwan Kim, & Christophe Muller, 2012. "Bias Transmission and Variance Reduction in Two-Stage Quantile Regression," AMSE Working Papers 1221, Aix-Marseille School of Economics, France.
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    47. Feng, Junlong, 2024. "Matching points: Supplementing instruments with covariates in triangular models," Journal of Econometrics, Elsevier, vol. 238(1).
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    53. Joel L. Horowitz, 2013. "Adaptive nonparametric instrumental variables estimation: empirical choice of the regularization parameter," CeMMAP working papers 30/13, Institute for Fiscal Studies.
    54. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
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    86. Undral Byambadalai & Tomu Hirata & Tatsushi Oka & Shota Yasui, 2025. "Beyond the Average: Distributional Causal Inference under Imperfect Compliance," Papers 2509.15594, arXiv.org, revised Oct 2025.
    87. Breunig, Christoph, 2016. "Specification testing in nonparametric instrumental quantile regression," SFB 649 Discussion Papers 2016-032, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    88. Ian Crawford & Matthew Polisson, 2015. "Demand analysis with partially observed prices," IFS Working Papers W15/16, Institute for Fiscal Studies.
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    90. Koki Fusejima, 2020. "Identification of multi-valued treatment effects with unobserved heterogeneity," Papers 2010.04385, arXiv.org, revised Apr 2023.
    91. Joachim Freyberger & Joel L. Horowitz, 2013. "Identification and shape restrictions in nonparametric instrumental variables estimation," CeMMAP working papers CWP31/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    92. Jinyong Hahn & Geert Ridder, 2011. "Conditional Moment Restrictions and Triangular Simultaneous Equations," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 683-689, May.
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    94. Giovanni Forchini, 2014. "A General Result on Observational Equivalence in a Class of Nonparametric Structural Equations Models," School of Economics Discussion Papers 0114, School of Economics, University of Surrey.
    95. Arthur Lewbel, 2018. "The Identification Zoo - Meanings of Identification in Econometrics," Boston College Working Papers in Economics 957, Boston College Department of Economics, revised 14 Dec 2019.
    96. Maximilian Kasy, 2014. "Instrumental Variables with Unrestricted Heterogeneity and Continuous Treatment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(4), pages 1614-1636.
    97. Victor Chernozhukov & Juan Carlos Escanciano & Hidehiko Ichimura & Whitney K. Newey & James M. Robins, 2018. "Locally robust semiparametric estimation," CeMMAP working papers CWP30/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    98. Centorrino, Samuele & Fève, Frédérique & Florens, Jean-Pierre, 2025. "Iterative estimation of nonparametric regressions with continuous endogenous variables and discrete instruments," Journal of Econometrics, Elsevier, vol. 247(C).
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    100. Kaspar W thrich, 2015. "Semiparametric estimation of quantile treatment effects with endogeneity," Diskussionsschriften dp1509, Universitaet Bern, Departement Volkswirtschaft.
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    Cited by:

    1. Hiroaki Kaido, 2014. "Asymptotically efficient estimation of weighted average derivatives with an interval censored variable," CeMMAP working papers 03/14, Institute for Fiscal Studies.
    2. Kurisu, Daisuke & Otsu, Taisuke, 2026. "Empirical likelihood for manifolds," LSE Research Online Documents on Economics 128293, London School of Economics and Political Science, LSE Library.
    3. Andres Aradillas-Lopez & Adam Rosen, 2013. "Inference in ordered response games with complete information," CeMMAP working papers 33/13, Institute for Fiscal Studies.
    4. Christopher P. Chambers & Federico Echenique & Nicolas Lambert, 2019. "Recovering Preferences from Finite Data," Papers 1909.05457, arXiv.org, revised Oct 2020.
    5. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2016. "Testing many moment inequalities," CeMMAP working papers CWP42/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Ulrich K. Müller & Andriy Norets, 2016. "Coverage Inducing Priors in Nonstandard Inference Problems," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1233-1241, July.
    7. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    8. Menzel, Konrad, 2014. "Consistent estimation with many moment inequalities," Journal of Econometrics, Elsevier, vol. 182(2), pages 329-350.
    9. Kilian, Lutz & Inoue, Atsushi, 2020. "The Role of the Prior in Estimating VAR Models with Sign Restrictions," CEPR Discussion Papers 15545, C.E.P.R. Discussion Papers.
    10. Kate Ho & Adam M. Rosen, 2015. "Partial Identification in Applied Research: Benefits and Challenges," NBER Working Papers 21641, National Bureau of Economic Research, Inc.
    11. Susanne M. Schennach, 2012. "Measurement error in nonlinear models - a review," CeMMAP working papers 41/12, Institute for Fiscal Studies.
    12. Donald W.K. Andrews & Sukjin Han, 2008. "Invalidity of the Bootstrap and the m Out of n Bootstrap for Interval Endpoints Defined by Moment Inequalities," Cowles Foundation Discussion Papers 1671, Cowles Foundation for Research in Economics, Yale University.
    13. Federico Zincenko, 2016. "Nonparametric Estimation of First-Price Auctions with Risk-Averse Bidders," Working Paper 5855, Department of Economics, University of Pittsburgh.
    14. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers CWP43/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Taisuke Otsu & Martin Pesendorfer & Yuya Sasaki & Yuya Takahashi, 2020. "Estimation of (static or dynamic) games under equilibrium multiplicity," STICERD - Econometrics Paper Series 611, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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    18. Federico A. Bugni & Ivan A. Canay & Xiaoxia Shi, 2014. "Inference for functions of partially identified parameters in moment inequality models," CeMMAP working papers CWP05/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Thomas G. Wollmann, 2018. "Trucks without Bailouts: Equilibrium Product Characteristics for Commercial Vehicles," American Economic Review, American Economic Association, vol. 108(6), pages 1364-1406, June.
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    21. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
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    25. Ghanem, Dalia, 2017. "Testing identifying assumptions in nonseparable panel data models," Journal of Econometrics, Elsevier, vol. 197(2), pages 202-217.
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    30. Vira Semenova, 2023. "Debiased Machine Learning of Aggregated Intersection Bounds and Other Causal Parameters," Papers 2303.00982, arXiv.org, revised May 2025.
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    41. Otsu, Taisuke, 2010. "On Bahadur efficiency of empirical likelihood," Journal of Econometrics, Elsevier, vol. 157(2), pages 248-256, August.
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    43. Andrew Chesher & Adam Rosen, 2016. "Characterizations of identified sets delivered by structural econometric models," CeMMAP working papers CWP44/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    44. Toru Kitagawa, 2011. "Inference and decision for set identified parameters using posterior lower and upper probabilities," CeMMAP working papers CWP16/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    45. Susanne M. Schennach, 2013. "Entropic Latent Variable Integration via Simulation," CeMMAP working papers CWP32/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    46. Donald W.K. Andrews & Soonwoo Kwon, 2019. "Inference in Moment Inequality Models That Is Robust to Spurious Precision under Model Misspecification," Cowles Foundation Discussion Papers 2184, Cowles Foundation for Research in Economics, Yale University, revised Oct 2019.
    47. Orville Mondal & Rui Wang, 2024. "Partial Identification of Binary Choice Models with Misreported Outcomes," Papers 2401.17137, arXiv.org.
    48. Aviv Nevo & Adam Rosen, 2008. "Identification with imperfect instruments," CeMMAP working papers CWP16/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    49. Ivan A. Canay & Azeem M. Shaikh, 2016. "Practical and theoretical advances in inference for partially identified models," CeMMAP working papers CWP05/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    50. Taisuke Otsu & Myung Hwan Seo, 2014. "Asymptotics for maximum score method under general conditions," STICERD - Econometrics Paper Series 571, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    51. Mika Meitz, 2024. "Statistical inference for generative adversarial networks and other minimax problems," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(3), pages 1323-1356, September.
    52. Andrew Chesher & Adam Rosen & Konrad Smolinski, 2011. "An instrumental variable model of multiple discrete choice," CeMMAP working papers CWP39/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    53. Shengjie Hong & Yu-Chin Hsu & Yuanyuan Wan, 2023. "Subvector inference for Varying Coefficient Models with Partial Identification," Working Papers tecipa-756, University of Toronto, Department of Economics.
    54. Khai Xiang Chiong & Matthew Shum, 2019. "Random Projection Estimation of Discrete-Choice Models with Large Choice Sets," Management Science, INFORMS, vol. 65(1), pages 256-271, January.
    55. Mourifié, Ismael & Wan, Yuanyuan, 2025. "Layered policy analysis in program evaluation using the marginal treatment effect," Journal of Econometrics, Elsevier, vol. 251(C).
    56. Anderson, Gordon & Linton, Oliver & Whang, Yoon-Jae, 2009. "Nonparametric estimation of a polarization measure," LSE Research Online Documents on Economics 25378, London School of Economics and Political Science, LSE Library.
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    58. Nail Kashaev, 2022. "Estimation of Parametric Binary Outcome Models with Degenerate Pure Choice-Based Data with Application to COVID-19-Positive Tests from British Columbia," University of Western Ontario, Departmental Research Report Series 20225, University of Western Ontario, Department of Economics.
    59. Ferman, Bruno & Pinto, Cristine, 2016. "Revisiting the Synthetic Control Estimator," MPRA Paper 73982, University Library of Munich, Germany.
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    61. Adams-Prassl, Abigail, 2019. "Mutually Consistent Revealed Preference Demand Predictions," CEPR Discussion Papers 13580, C.E.P.R. Discussion Papers.
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    66. Paul L. E. Grieco, 2014. "Discrete games with flexible information structures: an application to local grocery markets," RAND Journal of Economics, RAND Corporation, vol. 45(2), pages 303-340, June.
    67. Coroneo, Laura & Corradi, Valentina & Santos Monterio, Paulo, "undated". "Testing for optimal monetary policy via moment inequalities," Economic Research Papers 270654, University of Warwick - Department of Economics.
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    69. Raj Chetty, 2009. "Bounds on Elasticities with Optimization Frictions: A Synthesis of Micro and Macro Evidence on Labor Supply," NBER Working Papers 15616, National Bureau of Economic Research, Inc.
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    72. Victor Chernozhukov & Emre Kocatulum & Konrad Menzel, 2012. "Inference on sets in finance," CeMMAP working papers 04/12, Institute for Fiscal Studies.
    73. Tuba Tunçel, 2024. "Should We Prevent Off-Label Drug Prescriptions? Empirical Evidence from France," Post-Print hal-04758877, HAL.
    74. Lina Zhang & David T. Frazier & D. S. Poskitt & Xueyan Zhao, 2020. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Papers 2009.02642, arXiv.org, revised Sep 2022.
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    82. Larry G. Epstein & Hiroaki Kaido & Kyoungwon Seo, 2015. "Robust confidence regions for incomplete models," CeMMAP working papers CWP65/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    100. Andrew Chesher & Adam Rosen, 2019. "Generalized Instrumental Variable Models, Methods, and Applications," CeMMAP working papers CWP41/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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    113. Shinya Sugawara, 2013. "An Interval Regression Analysis for Tenures of Japanese Elder Care Workers Using Matched Employer-Employee Data," CIRJE F-Series CIRJE-F-887, CIRJE, Faculty of Economics, University of Tokyo.
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    1. Huber, Martin & Melly, Blaise, 2011. "Quantile Regression in the Presence of Sample Selection," Economics Working Paper Series 1109, University of St. Gallen, School of Economics and Political Science.
    2. Galvao, Antonio F. & Montes-Rojas, Gabriel, 2015. "On the equivalence of instrumental variables estimators for linear models," Economics Letters, Elsevier, vol. 134(C), pages 13-15.
    3. Frölich, Markus & Melly, Blaise, 2008. "Unconditional Quantile Treatment Effects under Endogeneity," IZA Discussion Papers 3288, IZA Network @ LISER.
    4. Xiaorong Yang & Jia Chen & Degui Li & Runze Li, 2023. "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure," Papers 2303.13218, arXiv.org.
    5. Javier Alejo & Nicolás Badaracco, 2015. "Counterfactual Distributions in Bivariate Models—A Conditional Quantile Approach," Econometrics, MDPI, vol. 3(4), pages 1-14, November.
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    1. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers CWP43/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Xiaohong Chen & Timothy M. Christensen & Keith O'Hara & Elie Tamer, 2016. "MCMC confidence sets for identified sets," CeMMAP working papers CWP28/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Tong Li & Xiaoyong Zheng, 2006. "Entry and competition effects in first-price auctions: theory and evidence from procurement auctions," CeMMAP working papers CWP13/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Vukina, Tomislav & Zheng, Xiaoyong & Marra, Michele & Levy, Armando, 2008. "Do farmers value the environment? Evidence from a conservation reserve program auction," International Journal of Industrial Organization, Elsevier, vol. 26(6), pages 1323-1332, November.
    5. Benjamin Poignard & Manabu Asaiz, 2020. "A Penalised OLS Framework for High-Dimensional Multivariate Stochastic Volatility Models," Discussion Papers in Economics and Business 20-02, Osaka University, Graduate School of Economics.
    6. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    7. McCrary, Justin, 2008. "Manipulation of the running variable in the regression discontinuity design: A density test," Journal of Econometrics, Elsevier, vol. 142(2), pages 698-714, February.
    8. Li, Tong, 2010. "Indirect inference in structural econometric models," Journal of Econometrics, Elsevier, vol. 157(1), pages 120-128, July.
    9. Hickman Brent R. & Hubbard Timothy P. & Sağlam Yiğit, 2012. "Structural Econometric Methods in Auctions: A Guide to the Literature," Journal of Econometric Methods, De Gruyter, vol. 1(1), pages 67-106, August.
    10. Michael Jansson, 2007. "Semiparametric Power Envelopes for Tests of the Unit Root Hypothesis," CREATES Research Papers 2007-12, Department of Economics and Business Economics, Aarhus University.
    11. Torben G. Andersen & Nicola Fusari & Viktor Todorov, 2014. "The Risk Premia Embedded in Index Options," CREATES Research Papers 2014-56, Department of Economics and Business Economics, Aarhus University.
    12. Yao Luo, 2018. "Unobserved Heterogeneity in Auctions under Restricted Stochastic Dominance," Working Papers tecipa-606, University of Toronto, Department of Economics.
    13. Qihui Chen & Zheng Fang, 2019. "Inference on Functionals under First Order Degeneracy," Papers 1901.04861, arXiv.org.
    14. Launov, Andrey & Holzner, Christian, 2005. "Search Equilibrium, Production Parameters and Social Returns to Education: Theory and Estimation," W.E.P. - Würzburg Economic Papers 64, University of Würzburg, Department of Economics.
    15. Justin McCrary, 2007. "Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test," NBER Technical Working Papers 0334, National Bureau of Economic Research, Inc.
    16. Emmanuel Guerre & Yao Luo, 2019. "Nonparametric Identification of First-Price Auction with Unobserved Competition: A Density Discontinuity Framework," Papers 1908.05476, arXiv.org, revised Dec 2024.
    17. Yanqin Fan & Hyeonseok Park, 2024. "Minimum Sliced Distance Estimation in a Class of Nonregular Econometric Models," Papers 2412.05621, arXiv.org.
    18. Patrick Bayer & Shakeeb Khan & Christopher Timmins, 2008. "Nonparametric Identification and Estimation in a Generalized Roy Model," NBER Working Papers 13949, National Bureau of Economic Research, Inc.
    19. Reiß, Markus & Schmidt-Hieber, Johannes, 2020. "Posterior contraction rates for support boundary recovery," Stochastic Processes and their Applications, Elsevier, vol. 130(11), pages 6638-6656.
    20. Li, Tong & Zheng, Xiaoyong, 2012. "Information acquisition and/or bid preparation: A structural analysis of entry and bidding in timber sale auctions," Journal of Econometrics, Elsevier, vol. 168(1), pages 29-46.
    21. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers 43/17, Institute for Fiscal Studies.
    22. Zhou, Haiming & Huang, Xianzheng, 2022. "Bayesian beta regression for bounded responses with unknown supports," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    23. Di Iorio, Francesca & Calzolari, Giorgio, 2006. "Discontinuities in indirect estimation: An application to EAR models," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 2124-2136, April.
    24. Yu, Ping, 2012. "Likelihood estimation and inference in threshold regression," Journal of Econometrics, Elsevier, vol. 167(1), pages 274-294.
    25. Joel L. Horowitz, 2018. "Bootstrap Methods in Econometrics," Papers 1809.04016, arXiv.org.
    26. Li, Tong, 2009. "Simulation based selection of competing structural econometric models," Journal of Econometrics, Elsevier, vol. 148(2), pages 114-123, February.
    27. Roberto Casarin & Antonio Peruzzi & Davide Raggi, 2025. "Multiple Equilibria and the Phillips Curve: Do Agents Always Underreact?," Working Papers 2025: 10, Department of Economics, University of Venice "Ca' Foscari".
    28. Laurent LAMY & Manasa PATNAM & Michael VISSER, 2019. "Distinguishing Incentive from Selection Effects in Auction-Determined Contracts," Working Papers 2019-15, Center for Research in Economics and Statistics.
    29. Laurini Márcio Poletti, 2013. "A Hybrid Data Cloning Maximum Likelihood Estimator for Stochastic Volatility Models," Journal of Time Series Econometrics, De Gruyter, vol. 5(2), pages 193-229, May.
    30. Xiaohong Chen & Timothy M. Christensen & Keith O'Hara & Elie Tamer, 2016. "MCMC confidence sets for identified sets," CeMMAP working papers 28/16, Institute for Fiscal Studies.
    31. Victor Chernozhukov & Ivan Fernandez-Val, 2011. "Inference for extremal conditional quantile models, with an application to market and birthweight risks," CeMMAP working papers CWP40/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    32. Yu, Ping, 2015. "Adaptive estimation of the threshold point in threshold regression," Journal of Econometrics, Elsevier, vol. 189(1), pages 83-100.
    33. Holzner, Christian & Launov, Andrey, 2010. "Search equilibrium and social and private returns to education," European Economic Review, Elsevier, vol. 54(1), pages 39-59, January.
    34. Hashimoto, Shintaro, 2021. "Predictive probability matching priors for a certain non-regular model," Statistics & Probability Letters, Elsevier, vol. 174(C).
    35. Fan, Yanqin & Shi, Xuetao, 2023. "Wald, QLR, and score tests when parameters are subject to linear inequality constraints," Journal of Econometrics, Elsevier, vol. 235(2), pages 2005-2026.
    36. Sağlam, Yiğit, 2012. "Structural Econometric Methods in Auctions: A Guide to the Literature," Working Paper Series 19224, Victoria University of Wellington, The New Zealand Institute for the Study of Competition and Regulation.
    37. Benjamin Poignard, 2020. "Asymptotic theory of the adaptive Sparse Group Lasso," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 297-328, February.
    38. Joel L. Horowitz, 2018. "Bootstrap methods in econometrics," CeMMAP working papers CWP53/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    39. Aryal, Gaurab & Grundl, Serafin & Kim, Dong-Hyuk & Zhu, Yu, 2018. "Empirical relevance of ambiguity in first-price auctions," Journal of Econometrics, Elsevier, vol. 204(2), pages 189-206.

  71. Victor Chernozhukov & Christian Hansen, 2004. "The Effects of 401(K) Participation on the Wealth Distribution: An Instrumental Quantile Regression Analysis," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 735-751, August.

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    1. Horowitz, Joel L. & Lee, Sokbae, 2009. "Testing a parametric quantile-regression model with an endogenous explanatory variable against a nonparametric alternative," Journal of Econometrics, Elsevier, vol. 152(2), pages 141-152, October.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2017. "Double/Debiased Machine Learning for Treatment and Structural Parameters," NBER Working Papers 23564, National Bureau of Economic Research, Inc.
    3. Javier Alejo & Antonio F Galvao & Gabriel Montes-Rojas, 2023. "A first-stage representation for instrumental variables quantile regression," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 350-377.
    4. Kaspar Wuthrich & Ying Zhu, 2019. "Omitted variable bias of Lasso-based inference methods: A finite sample analysis," Papers 1903.08704, arXiv.org, revised Sep 2021.
    5. Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
    6. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Sanjay B. Singh & Talal H. Alsabhan & Reem Alshagri, 2024. "Evaluating the Policy Implications of Renewable Portfolio Standards on Grid Reliability in the United States: An Instrumental Variable Quantile Analysis," Energies, MDPI, vol. 17(23), pages 1-22, December.
    8. Wehby, George L. & Courtemanche, Charles J., 2012. "The heterogeneity of the cigarette price effect on body mass index," Journal of Health Economics, Elsevier, vol. 31(5), pages 719-729.
    9. Victor Chernozhukov & Christian B. Hansen & Lingwei Kong & Weining Wang, 2025. "Plausible GMM: A Quasi-Bayesian Approach," Bristol Economics Discussion Papers 25/817, School of Economics, University of Bristol, UK.
    10. Susumu Imai & Hajime Katayama & Kala Krishna, 2008. "A Quantile-based Test of Protection for Sale Model," NBER Working Papers 13900, National Bureau of Economic Research, Inc.
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    97. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.
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  74. Len Umantsev & Victor Chernozhukov, 2001. "Conditional value-at-risk: Aspects of modeling and estimation," Empirical Economics, Springer, vol. 26(1), pages 271-292.

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    1. Roman V. Ivanov, 2018. "A Credit-Risk Valuation under the Variance-Gamma Asset Return," Risks, MDPI, vol. 6(2), pages 1-25, May.
    2. Victor Chernozhukov, 2005. "Extremal quantile regression," Papers math/0505639, arXiv.org.
    3. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org, revised Oct 2019.
    4. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    5. So Yeon Chun & Alexander Shapiro & Stan Uryasev, 2012. "Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics," Operations Research, INFORMS, vol. 60(4), pages 739-756, August.
    6. Aldasoro, Iñaki & Gambacorta, Leonardo & Giudici, Paolo & Leach, Thomas, 2022. "The drivers of cyber risk," Journal of Financial Stability, Elsevier, vol. 60(C).
    7. Tobias Adrian & Markus K. Brunnermeier, 2008. "CoVaR," Staff Reports 348, Federal Reserve Bank of New York.
      • Tobias Adrian & Markus K. Brunnermeier, 2016. "CoVaR," American Economic Review, American Economic Association, vol. 106(7), pages 1705-1741, July.
      • Tobias Adrian & Markus K. Brunnermeier, 2011. "CoVaR," NBER Working Papers 17454, National Bureau of Economic Research, Inc.
    8. López-Espinosa, Germán & Moreno, Antonio & Rubia, Antonio & Valderrama, Laura, 2015. "Systemic risk and asymmetric responses in the financial industry," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 471-485.
    9. Oka, Tatsushi & Qu, Zhongjun, 2011. "Estimating structural changes in regression quantiles," Journal of Econometrics, Elsevier, vol. 162(2), pages 248-267, June.
    10. Tibor Szendrei, 2025. "Crossing penalised CAViaR," Papers 2501.10564, arXiv.org.
    11. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2017. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Working Papers 2017-10, University of Tasmania, Tasmanian School of Business and Economics.
    12. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.
    13. Akio Hattori & Kentaro Kikuchi & Fuminori Niwa & Yoshihiko Uchida, 2014. "A Survey of Systemic Risk Measures: Methodology and Application to the Japanese Market," IMES Discussion Paper Series 14-E-03, Institute for Monetary and Economic Studies, Bank of Japan.
    14. Li, Ming-Yuan Leon & Miu, Peter, 2010. "A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 818-833, September.
    15. Zhijie Xiao, 2009. "Quantile Cointegrating Regression," Boston College Working Papers in Economics 708, Boston College Department of Economics.
    16. Benjamin Hamidi & Emmanuel Jurczenko & Bertrand Maillet, 2009. "D'un multiple conditionnel en assurance de portefeuille : CAViaR pour les gestionnaires ?," Post-Print halshs-00389773, HAL.
    17. Dingshi Tian & Zongwu Cai & Ying Fang, 2018. "Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201807, University of Kansas, Department of Economics, revised Oct 2018.
    18. Angelica Gianfreda & Derek Bunn, 2018. "A Stochastic Latent Moment Model for Electricity Price Formation," BEMPS - Bozen Economics & Management Paper Series BEMPS46, Faculty of Economics and Management at the Free University of Bozen.
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    21. Zongwu Cai & Xian Wang, 2013. "Nonparametric Methods for Estimating Conditional VaR and Expected Shortfall," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
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    23. Clancy, Daragh & Gabriele, Carmine & Žigraiová, Diana, 2022. "Sovereign bond market spillovers from crisis-time developments in Greece," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
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    41. Chen, Zhao & Cheng, Vivian Xinyi & Liu, Xu, 2024. "Hypothesis testing on high dimensional quantile regression," Journal of Econometrics, Elsevier, vol. 238(1).
    42. DeRossi, G. & Harvey, A., 2006. "Time-Varying Quantiles," Cambridge Working Papers in Economics 0649, Faculty of Economics, University of Cambridge.
    43. Chi Ming Wong & Lei Lam Olivia Ting, 2016. "A Quantile Regression Approach to the Multiple Period Value at Risk Estimation," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 12(1), pages 1-35, February.
    44. Ewa Ratuszny, 2015. "Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 129-156.
    45. Cappiello, Lorenzo & Manganelli, Simone & Gérard, Bruno, 2005. "Measuring comovements by regression quantiles," Working Paper Series 501, European Central Bank.
    46. Yuya Sasaki & Yulong Wang, 2019. "Fixed-k Inference for Conditional Extremal Quantiles," Papers 1909.00294, arXiv.org, revised Jul 2020.
    47. Victor Chernozhukov & Han Hong, 2023. "An MCMC Approach to Classical Estimation," Papers 2301.07782, arXiv.org.
    48. Chao, Shih-Kang & Härdle, Wolfgang Karl & Wang, Weining, 2012. "Quantile regression in risk calibration," SFB 649 Discussion Papers 2012-006, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    49. López-Espinosa, Germán & Rubia, Antonio & Valderrama, Laura & Antón, Miguel, 2013. "Good for one, bad for all: Determinants of individual versus systemic risk," Journal of Financial Stability, Elsevier, vol. 9(3), pages 287-299.
    50. Schaumburg, Julia, 2010. "Predicting extreme VaR: Nonparametric quantile regression with refinements from extreme value theory," SFB 649 Discussion Papers 2010-009, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    51. Kostov, Philip & Patton, Myles & Moss, Joan E. & McErlean, Seamus, 2005. "Does Gibrat's Law Hold Amongst Dairy Farmers in Northern Ireland?," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24775, European Association of Agricultural Economists.
    52. Raffaella Giacomini & Ivana Komunjer, 2003. "Evaluation and Combination of Conditional Quantile Forecasts," Boston College Working Papers in Economics 571, Boston College Department of Economics.
    53. Shah, Attaullah & Shah, Hamid Ali & Smith, Jason M. & Labianca, Giuseppe (Joe), 2017. "Judicial efficiency and capital structure: An international study," Journal of Corporate Finance, Elsevier, vol. 44(C), pages 255-274.
    54. Dong Jin Lee, 2020. "Optimal tests for parameter breaking process in conditional quantile models," The Japanese Economic Review, Springer, vol. 71(3), pages 479-510, July.
    55. White, Halbert & Kim, Tae-Hwan & Manganelli, Simone, 2015. "VAR for VaR: Measuring tail dependence using multivariate regression quantiles," Journal of Econometrics, Elsevier, vol. 187(1), pages 169-188.
    56. Victor Chernozhukov & Ivan Fernandez-Val, 2011. "Inference for extremal conditional quantile models, with an application to market and birthweight risks," CeMMAP working papers CWP40/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    57. Denis Chetverikov & Yukun Liu & Aleh Tsyvinski, 2022. "Weighted-average quantile regression," Papers 2203.03032, arXiv.org.
    58. Shaobo Li & Ben Sherwood, 2025. "Quantile Predictions for Equity Premium using Penalized Quantile Regression with Consistent Variable Selection across Multiple Quantiles," Papers 2505.16019, arXiv.org.
    59. Ilias Chronopoulos & Aristeidis Raftapostolos & George Kapetanios, 2024. "Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression," Journal of Financial Econometrics, Oxford University Press, vol. 22(3), pages 636-669.
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    66. Khalifa, Maha & Othman, Hakim Ben & Hussainey, Khaled, 2018. "The effect of ex ante and ex post conservatism on the cost of equity capital: A quantile regression approach for MENA countries," Research in International Business and Finance, Elsevier, vol. 44(C), pages 239-255.
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Software components

  1. Victor Chernozhukov & Ivan Fernandez-Val & Sukjin Han & Amanda Kowalski, 2012. "CQIV: Stata module to perform censored quantile instrumental variables regression," Statistical Software Components S457478, Boston College Department of Economics, revised 25 Sep 2019.

    Cited by:

    Sorry, no citations of software components recorded.

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