IDEAS home Printed from https://ideas.repec.org/f/c/par625.html

Timothy Armstrong

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.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. 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.

    Mentioned in:

    1. A Simple Adjustment for Bandwidth Snooping (REStud 2018) in ReplicationWiki ()

Working papers

  1. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..

    Cited by:

    1. Philipp Ketz & Adam McCloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Papers 2109.08222, arXiv.org.
    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. Soonwoo Kwon & Liyang Sun, 2025. "Estimating Treatment Effects Under Bounded Heterogeneity," Papers 2510.05454, arXiv.org.
    4. Timothy B. Armstrong & Martin Weidner & Andrei Zeleneev, 2024. "Robust estimation and inference in panels with interactive fixed effects," CeMMAP working papers 28/24, Institute for Fiscal Studies.

  2. Timothy B. Armstrong & Michal Koles'ar & Mikkel Plagborg-M{o}ller, 2020. "Robust Empirical Bayes Confidence Intervals," Papers 2004.03448, arXiv.org, revised May 2022.

    Cited by:

    1. Evan T.R. Rosenman & Guillaume Basse & Art B. Owen & Mike Baiocchi, 2023. "Combining observational and experimental datasets using shrinkage estimators," Biometrics, The International Biometric Society, vol. 79(4), pages 2961-2973, December.
    2. Simon Freyaldenhoven & Christian Hansen, 2025. "(Visualizing) Plausible Treatment Effect Paths," Papers 2505.12014, arXiv.org.
    3. Isaiah Andrews & Toru Kitagawa & Adam McCloskey, 2020. "Inference on winners," CeMMAP working papers CWP43/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Walters, Christopher, 2024. "Empirical Bayes methods in labor economics," Handbook of Labor Economics,, Elsevier.
    5. JoonHo Lee & Daihe Sui, 2025. "Fully Bayesian Inference for Meta-Analytic Deconvolution Using Efron’s Log-Spline Prior," Mathematics, MDPI, vol. 13(16), pages 1-49, August.
    6. Easterly, William & Pennings, Steven, 2025. "Leader value added: Assessing the growth contribution of individual national leaders," Journal of Development Economics, Elsevier, vol. 175(C).
    7. Jaan Masso & Amaresh K Tiwari, 2024. "Retracted: Estimating Production Function And Productivity Impact Of Export Persistence In Presence Of Market Imperfections," University of Tartu - Faculty of Economics and Business Administration Working Paper Series 150, Faculty of Economics and Business Administration, University of Tartu (Estonia).
    8. Boot, Tom, 2023. "Joint inference based on Stein-type averaging estimators in the linear regression model," Journal of Econometrics, Elsevier, vol. 235(2), pages 1542-1563.

  3. Timothy B. Armstrong & Michal Koles'ar, 2018. "Sensitivity Analysis using Approximate Moment Condition Models," Papers 1808.07387, arXiv.org, revised Jul 2020.

    Cited by:

    1. Hahn, Jinyong & Hausman, Jerry & Kim, Jeonghwan, 2021. "A small sigma approach to certain problems in errors-in-variables models," Economics Letters, Elsevier, vol. 208(C).
    2. Hwang, Jungbin & Kang, Byunghoon & Lee, Seojeong, 2022. "A doubly corrected robust variance estimator for linear GMM," Journal of Econometrics, Elsevier, vol. 229(2), pages 276-298.
    3. Timothy B. Armstrong & Patrick Kline & Liyang Sun, 2023. "Adapting to Misspecification," Papers 2305.14265, arXiv.org, revised Sep 2025.
    4. Gregor Steiner & Jeremie Houssineau & Mark F. J. Steel, 2025. "Possibilistic Instrumental Variable Regression," Papers 2511.16029, arXiv.org, revised Jan 2026.
    5. 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.
    6. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    7. Aristotelis Epanomeritakis & Davide Viviano, 2025. "Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence," Papers 2510.23434, arXiv.org, revised Dec 2025.
    8. Thomas H. Jørgensen, 2021. "Sensitivity to Calibrated Parameters," CEBI working paper series 20-14, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    9. Alessandro Casini & Adam McCloskey, 2025. "Identification, Estimation and Inference in High-Frequency Event Study Regressions," CEIS Research Paper 608, Tor Vergata University, CEIS, revised 28 Jul 2025.
    10. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2020. "On the Informativeness of Descriptive Statistics for Structural Estimates," Working Papers 2020-06, Brown University, Department of Economics.
    11. Keisuke Hirano & Jack R. Porter, 2023. "Asymptotic Representations for Sequential Decisions, Adaptive Experiments, and Batched Bandits," Papers 2302.03117, arXiv.org, revised Feb 2025.
    12. Maximilian Blesch & Philipp Eisenhauer, 2023. "Robust Decision-Making under Risk and Ambiguity," Rationality and Competition Discussion Paper Series 463, CRC TRR 190 Rationality and Competition.
    13. Roy Allen & John Rehbeck, 2020. "Counterfactual and Welfare Analysis with an Approximate Model," Papers 2009.03379, arXiv.org.
    14. Stefano DellaVigna & Jörg Heining & Johannes F. Schmieder & Simon Trenkle, 2020. "Evidence on Job Search Models from a Survey of Unemployed Workers in Germany," NBER Working Papers 27037, National Bureau of Economic Research, Inc.
    15. David Kang & Seojeong Lee, 2025. "Misspecification-Robust Asymptotic and Bootstrap Inference for Nonsmooth GMM," Working Papers 423284005, Lancaster University Management School, Economics Department.
    16. Zhaonan Qu & Yongchan Kwon, 2024. "Distributionally Robust Instrumental Variables Estimation," Papers 2410.15634, arXiv.org, revised Dec 2024.
    17. Timothy B. Armstrong & Michal Kolesár, 2020. "Sensitivity Analysis using Approximate Moment Condition Models," Working Papers 2020-28, Princeton University. Economics Department..
    18. 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.
    19. Philipp Eisenhauer & Lena Janys & Christopher Walsh & Janós Gabler, 2023. "Structural Models for Policy-Making," CRC TR 224 Discussion Paper Series crctr224_2023_484, University of Bonn and University of Mannheim, Germany.
    20. Bo Honoré & Thomas Jørgensen & Áureo de Paula, 2020. "The informativeness of estimation moments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 797-813, November.
    21. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
    22. Stéphane Bonhomme, 2020. "A Comment on: “On the Informativeness of Descriptive Statistics for Structural Estimates” by Isaiah Andrews, Matthew Gentzkow, and Jesse M. Shapiro," Econometrica, Econometric Society, vol. 88(6), pages 2259-2264, November.
    23. Chen, Xiaohong & Hansen, Lars Peter & Hansen, Peter G., 2024. "Robust inference for moment condition models without rational expectations," Journal of Econometrics, Elsevier, vol. 243(1).
    24. 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.
    25. Sun, Zhenting & Wüthrich, Kaspar, 2025. "Pairwise valid instruments," Journal of Econometrics, Elsevier, vol. 250(C).
    26. Ertian Chen, 2025. "Robust Structural Estimation under Misspecified Latent-State Dynamics," Papers 2510.22347, arXiv.org, revised Nov 2025.
    27. Byunghoon Kang, 2018. "Higher Order Approximation of IV Estimators with Invalid Instruments," Working Papers 257105320, Lancaster University Management School, Economics Department.
    28. Raffaella Giacomini & Toru Kitagawa & Harald Uhlig, 2019. "Estimation Under Ambiguity," CeMMAP working papers CWP24/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    29. Xin Liu, 2024. "Averaging Estimation for Instrumental Variables Quantile Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(5), pages 1290-1312, October.
    30. Stéphane Bonhomme & Martin Weidner, 2022. "Minimizing sensitivity to model misspecification," Quantitative Economics, Econometric Society, vol. 13(3), pages 907-954, July.
    31. Naoya Sueishi, 2022. "A Misuse of Specification Tests," Papers 2211.11915, arXiv.org, revised Sep 2025.
    32. Isaiah Andrews & Matthew Gentzkow & Jesse M. Shapiro, 2020. "Transparency in Structural Research," NBER Working Papers 26631, National Bureau of Economic Research, Inc.
    33. Timothy Christensen & Benjamin Connault, 2023. "Counterfactual Sensitivity and Robustness," Econometrica, Econometric Society, vol. 91(1), pages 263-298, January.

  4. Timothy B. Armstrong, 2018. "Adaptation Bounds for Confidence Bands under Self-Similarity," Cowles Foundation Discussion Papers 2146, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Timothy B. Armstrong, 2018. "Adaptation Bounds for Confidence Bands under Self-Similarity," Cowles Foundation Discussion Papers 2146R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2019.

  5. Timothy B. Armstrong, 2017. "On the Choice of Test Statistic for Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1960R2, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Chen, Le-Yu & Lee, Sokbae, 2019. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," Journal of Econometrics, Elsevier, vol. 210(2), pages 482-497.
    2. Evan K. Rose & Yotam Shem-Tov, 2021. "On Recoding Ordered Treatments as Binary Indicators," Papers 2111.12258, arXiv.org, revised Mar 2024.
    3. Andres Aradillas-Lopez & Adam Rosen, 2021. "Inference in ordered response games with complete information," CeMMAP working papers CWP37/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Yuehao Bai & Shunzhuang Huang & Max Tabord-Meehan, 2024. "Sharp Testable Implications of Encouragement Designs," Papers 2411.09808, arXiv.org, revised Nov 2025.
    5. Zheng Fang, 2021. "A Unifying Framework for Testing Shape Restrictions," Papers 2107.12494, arXiv.org, revised Aug 2021.
    6. Armstrong, Timothy B. & Chan, Hock Peng, 2016. "Multiscale adaptive inference on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 194(1), pages 24-43.

  6. Timothy B. Armstrong & Michal Koles'ar, 2017. "Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness," Papers 1712.04594, arXiv.org, revised Jan 2021.

    Cited by:

    1. Zhexiao Lin & Peng Ding & Fang Han, 2023. "Estimation Based on Nearest Neighbor Matching: From Density Ratio to Average Treatment Effect," Econometrica, Econometric Society, vol. 91(6), pages 2187-2217, November.
    2. Cl'ement de Chaisemartin, 2021. "Trading-off Bias and Variance When the Size of the Treatment Effect is Bounded," Papers 2105.08766, arXiv.org, revised Nov 2025.
    3. Laurent Davezies & Xavier D'Haultf{oe}uille & Louise Laage, 2021. "Identification and Estimation of Average Causal Effects in Fixed Effects Logit Models," Papers 2105.00879, arXiv.org, revised Dec 2024.
    4. Zichen Deng & Maarten Lindeboom, 2021. "Early-life Famine Exposure, Hunger Recall and Later-life Health," Tinbergen Institute Discussion Papers 21-054/V, Tinbergen Institute.
    5. Zichen Deng & Maarten Lindeboom, 2021. "Early-life Famine Exposure, Hunger Recall and Later-life Health," Papers 2021-04, Centre for Health Economics, Monash University.
    6. Zichen Deng & Maarten Lindeboom, 2022. "Early‐life famine exposure, hunger recall, and later‐life health," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 771-787, June.
    7. Deng, Zichen & Lindeboom, Maarten, 2021. "Early-Life Famine Exposure, Hunger Recall and Later-Life Health," IZA Discussion Papers 14487, IZA Network @ LISER.
    8. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2025. "Double Robust Bayesian Inference on Average Treatment Effects," Econometrica, Econometric Society, vol. 93(2), pages 539-568, March.
    9. Huiming Zhang & Haoyu Wei & Guang Cheng, 2023. "Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm," Papers 2303.07287, arXiv.org, revised Jan 2024.
    10. Soonwoo Kwon & Liyang Sun, 2025. "Estimating Treatment Effects Under Bounded Heterogeneity," Papers 2510.05454, arXiv.org.
    11. 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.
    12. Guanyu LU & Kenta TANAKA & Toshi H. ARIMURA, 2025. "The Impact of Emissions Trading Systems on Manufacturing Installation Productivity: Evidence from Japan," Discussion papers 25063, Research Institute of Economy, Trade and Industry (RIETI).
    13. Jing Kong, 2025. "On the Asymptotics of the Minimax Linear Estimator," Papers 2510.16661, arXiv.org.
    14. Michael C. Knaus, 2024. "Treatment Effect Estimators as Weighted Outcomes," Papers 2411.11559, arXiv.org, revised Dec 2024.
    15. Junho Choi, 2025. "On the role of the design phase in a linear regression," Papers 2509.01861, arXiv.org.
    16. Timothy B. Armstrong & Michal Koles'ar, 2018. "Sensitivity Analysis using Approximate Moment Condition Models," Papers 1808.07387, arXiv.org, revised Jul 2020.
    17. Max Cytrynbaum, 2021. "Optimal Stratification of Survey Experiments," Papers 2111.08157, arXiv.org, revised Aug 2023.
    18. Xie, Haitian, 2024. "Nonlinear and nonseparable structural functions in regression discontinuity designs with a continuous treatment," Journal of Econometrics, Elsevier, vol. 242(1).
    19. Kline, Patrick, 2024. "Firm wage effects," Handbook of Labor Economics,, Elsevier.
    20. Claudia Noack & Christoph Rothe, 2024. "Bias‐Aware Inference in Fuzzy Regression Discontinuity Designs," Econometrica, Econometric Society, vol. 92(3), pages 687-711, May.
    21. 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.
    22. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Mar 2025.
    23. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    24. Jacob Dorn, 2025. "How Much Weak Overlap Can Doubly Robust T-Statistics Handle?," Papers 2504.13273, arXiv.org, revised Apr 2025.
    25. Sokbae Lee & Martin Weidner, 2021. "Bounding Treatment Effects by Pooling Limited Information across Observations," Papers 2111.05243, arXiv.org, revised Feb 2026.
    26. Timothy B. Armstrong & Michal Koles'ar & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Papers 2012.14823, arXiv.org, revised Aug 2023.
    27. Pengzhou Wu & Kenji Fukumizu, 2021. "$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap," Papers 2110.05225, arXiv.org.
    28. Ferman, Bruno, 2021. "Matching estimators with few treated and many control observations," Journal of Econometrics, Elsevier, vol. 225(2), pages 295-307.
    29. 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.
    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. Christoph Breunig & Ruixuan Liu & Zhengfei Yu, 2022. "Double Robust Bayesian Inference on Average Treatment Effects," Papers 2211.16298, arXiv.org, revised Feb 2025.

  7. Timothy B. Armstrong & Michal Koles�r, 2016. "Simple and Honest Confidence Intervals in Nonparametric Regression," Cowles Foundation Discussion Papers 2044R2, Cowles Foundation for Research in Economics, Yale University, revised Mar 2018.

    Cited by:

    1. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    2. Carolina Caetano & Gregorio Caetano & Leonard Goff & Eric Nielsen, 2025. "Identification of Causal Effects with a Bunching Design," Papers 2507.05210, arXiv.org.
    3. Kamila Cygan-Rehm & Matthias Westphal, 2025. "School Starting Age and the Gender Pay Gap over the Life Cycle," CESifo Working Paper Series 12274, CESifo.
    4. Yang He & Otávio Bartalotti, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 211-231.
    5. Andra Hiriscau, 2024. "The Effect of Paid Maternity Leave on Fertility and Mothers’ Labor Force Participation," Journal of Labor Research, Springer, vol. 45(3), pages 350-384, September.
    6. Giuseppe Rose & Desiré De Luca, 2024. "Health Concerns And Consumption Expectations During Covid-19: Evidence From A Fuzzy Regression Discontinuity Design," Working Papers 202401, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    7. Matias D. Cattaneo & Rocio Titiunik, 2021. "Regression Discontinuity Designs," Papers 2108.09400, arXiv.org, revised Feb 2022.
    8. Mueller, Clemens, 2023. "Reacting to Early Failure in University: Evidence from a Regression Discontinuity Design," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277620, Verein für Socialpolitik / German Economic Association.
    9. Ioana Marinescu & Sofia Triantafillou & Konrad Kording, 2022. "Regression discontinuity threshold optimization," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-19, November.
    10. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org, revised Mar 2025.
    11. Matti Sarvimäki & Hanna Pesola, 2022. "Intergenerational Spillovers of Integration Policies: Evidence from Finland’s Integration Plans," RFBerlin Discussion Paper Series 2212, ROCKWOOL Foundation Berlin (RFBerlin).
    12. Santiago Acerenza, 2024. "Partial Identification of Marginal Treatment Effects with Discrete Instruments and Misreported Treatment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 74-100, February.
    13. Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
    14. Pesola, Hanna Onerva & Sarvimäki, Matti, 2022. "Intergenerational Spillovers of Integration Policies: Evidence from Finland's Integration Plans," IZA Discussion Papers 15310, IZA Network @ LISER.
    15. Xie, Haitian, 2024. "Nonlinear and nonseparable structural functions in regression discontinuity designs with a continuous treatment," Journal of Econometrics, Elsevier, vol. 242(1).
    16. Guastavino, Carlos & Miranda, Alvaro & Montero, Rodrigo, 2021. "Rank effect in bureaucrat recruitment," European Journal of Political Economy, Elsevier, vol. 68(C).
    17. Kengo Kato & Yuya Sasaki & Takuya Ura, 2021. "Robust inference in deconvolution," Quantitative Economics, Econometric Society, vol. 12(1), pages 109-142, January.
    18. Claudia Noack & Christoph Rothe, 2024. "Bias‐Aware Inference in Fuzzy Regression Discontinuity Designs," Econometrica, Econometric Society, vol. 92(3), pages 687-711, May.
    19. Bugni, Federico A. & Canay, Ivan A., 2021. "Testing continuity of a density via g-order statistics in the regression discontinuity design," Journal of Econometrics, Elsevier, vol. 221(1), pages 138-159.
    20. Patrizia Ordine & Giuseppe Rose, 2019. "Early entry, age-at-test, and schooling attainment: evidence from Italian primary schools," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 36(3), pages 761-784, October.
    21. Tomasz Olma, 2021. "Nonparametric Estimation of Truncated Conditional Expectation Functions," Papers 2109.06150, arXiv.org.
    22. 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.
    23. David Figlio & Krzysztof Karbownik & Umut Özek & David N. Figlio, 2023. "Sibling Spillovers May Enhance the Efficacy of Targeted School Policies," CESifo Working Paper Series 10526, CESifo.
    24. Yingying Dong & Michal Kolesár, 2023. "When Can We Ignore Measurement Error in the Running Variable?," Working Papers 2022-13, Princeton University. Economics Department..
    25. Samantha E. Clark & Ruth Etzioni & Jerry Radich & Zachary Marcum & Anirban Basu, 2023. "The price elasticity of Gleevec in patients with Chronic Myeloid Leukemia enrolled in Medicare Part D: Evidence from a regression discontinuity design," Papers 2305.06076, arXiv.org.
    26. Zincenko, Federico, 2024. "Estimation and inference of seller’s expected revenue in first-price auctions," Journal of Econometrics, Elsevier, vol. 241(1).
    27. 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.
    28. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.
    29. Dean Eckles & Nikolaos Ignatiadis & Stefan Wager & Han Wu, 2020. "Noise-Induced Randomization in Regression Discontinuity Designs," Papers 2004.09458, arXiv.org, revised Mar 2025.
    30. Cowan, Benjamin & Jones, Todd R. & Swigert, Jeffrey, 2024. "Parental and Student Time Use Around the Academic Year," Journal of Economic Behavior & Organization, Elsevier, vol. 224(C), pages 66-110.
    31. Sasaki, Yuya & Wang, Yulong, 2024. "On uniform confidence intervals for the tail index and the extreme quantile," Journal of Econometrics, Elsevier, vol. 244(1).
    32. Nibourel, Chloe & Folkestad, Mattias, 2025. "Starting young: How age limits shape political participation," European Journal of Political Economy, Elsevier, vol. 87(C).
    33. Stéphane Bonhomme & Martin Weidner, 2022. "Minimizing sensitivity to model misspecification," Quantitative Economics, Econometric Society, vol. 13(3), pages 907-954, July.
    34. Freyberger, Joachim & Rai, Yoshiyasu, 2018. "Uniform confidence bands: Characterization and optimality," Journal of Econometrics, Elsevier, vol. 204(1), pages 119-130.
    35. Fang, Guanfu & Miao, Liya, 2024. "Expanding boundaries: The Impact of kindergarten availability on women's employment in China," Labour Economics, Elsevier, vol. 88(C).
    36. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Simultaneous Inference for Local Structural Parameters with Random Forests," Papers 2405.07860, arXiv.org, revised Sep 2024.
    37. Federico Crippa, 2024. "Manipulation Test for Multidimensional RDD," Papers 2402.10836, arXiv.org, revised Jun 2024.
    38. Yingying Dong & Michal Kolesár, 2023. "When can we ignore measurement error in the running variable?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 735-750, August.
    39. Stuart Lane, 2025. "The moment is here: a generalised class of estimators for fuzzy regression discontinuity designs," Papers 2511.03424, arXiv.org.
    40. Martti Kaila, 2024. "How Do People React to Income-Based Fines? Evidence from Speeding Tickets Discontinuities," CESifo Working Paper Series 11064, CESifo.

  8. Timothy B. Armstrong & Michal Koles�r, 2016. "Optimal Inference in a Class of Regression Models," Cowles Foundation Discussion Papers 2043R, Cowles Foundation for Research in Economics, Yale University, revised May 2017.

    Cited by:

    1. Philipp Ketz & Adam McCloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Papers 2109.08222, arXiv.org.
    2. Gregory Fletcher Cox, 2024. "A Simple and Adaptive Confidence Interval when Nuisance Parameters Satisfy an Inequality," Papers 2409.09962, arXiv.org.
    3. Michael P. Leung, 2023. "Cluster-Randomized Trials with Cross-Cluster Interference," Papers 2310.18836, arXiv.org, revised Oct 2025.
    4. Yusuke Narita & Kohei Yata, 2021. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Cowles Foundation Discussion Papers 2283, Cowles Foundation for Research in Economics, Yale University.
    5. Evan T.R. Rosenman & Guillaume Basse & Art B. Owen & Mike Baiocchi, 2023. "Combining observational and experimental datasets using shrinkage estimators," Biometrics, The International Biometric Society, vol. 79(4), pages 2961-2973, December.
    6. Paul Goldsmith-Pinkham & Karen Jiang & Zirui Song & Jacob Wallace, 2022. "Measuring Changes in Disparity Gaps: An Application to Health Insurance," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 356-360, May.
    7. Karthik Muralidharan & Mauricio Romero & Kaspar Wüthrich, 2020. "Factorial Designs, Model Selection, and (Incorrect) Inference in Randomized Experiments," CESifo Working Paper Series 8137, CESifo.
    8. Kamila Cygan-Rehm & Matthias Westphal, 2025. "School Starting Age and the Gender Pay Gap over the Life Cycle," CESifo Working Paper Series 12274, CESifo.
    9. Timothy B. Armstrong & Michal Kolesár & Mikkel Plagborg-Møller, 2022. "Robust Empirical Bayes Confidence Intervals," Working Papers 2022-27, Princeton University. Economics Department..
    10. Timothy B. Armstrong & Patrick Kline & Liyang Sun, 2023. "Adapting to Misspecification," Papers 2305.14265, arXiv.org, revised Sep 2025.
    11. 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.
    12. Feng, Jin & Song, Hong & Wang, Zhen, 2020. "The elderly's response to a patient cost-sharing policy in health insurance: Evidence from China," Journal of Economic Behavior & Organization, Elsevier, vol. 169(C), pages 189-207.
    13. Ben Deaner & Soonwoo Kwon, 2025. "Extrapolation in Regression Discontinuity Design Using Comonotonicity," Papers 2507.00289, arXiv.org.
    14. Yang He & Otávio Bartalotti, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 211-231.
    15. Rahul Singh & Moses Stewart, 2025. "Placebo Discontinuity Design," Papers 2507.12693, arXiv.org.
    16. 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.
    17. Christina Korting & Carl Lieberman & Jordan Matsudaira & Zhuan Pei & Yi Shen, 2021. "Visual Inference and Graphical Representation in Regression Discontinuity Designs," Papers 2112.03096, arXiv.org, revised Jan 2023.
    18. 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.
    19. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    20. Aristotelis Epanomeritakis & Davide Viviano, 2025. "Learning What to Learn: Experimental Design when Combining Experimental with Observational Evidence," Papers 2510.23434, arXiv.org, revised Dec 2025.
    21. Giuseppe Rose & Desiré De Luca, 2024. "Health Concerns And Consumption Expectations During Covid-19: Evidence From A Fuzzy Regression Discontinuity Design," Working Papers 202401, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    22. Timothy B. Armstrong & Michal Koles'r, 2018. "Sensitivity Analysis using Approximate Moment Condition Models," Cowles Foundation Discussion Papers 2158R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2019.
    23. Yoici Arai & Taisuke Otsu & Myung Hwan Seo, 2022. "Regression discontinuity design with potentially many covariates," STICERD - Econometrics Paper Series 626, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    24. Atı̇la Abdulkadı̇roğlu & Joshua D. Angrist & Yusuke Narita & Parag Pathak, 2022. "Breaking Ties: Regression Discontinuity Design Meets Market Design," Econometrica, Econometric Society, vol. 90(1), pages 117-151, January.
    25. Soonwoo Kwon & Liyang Sun, 2025. "Estimating Treatment Effects Under Bounded Heterogeneity," Papers 2510.05454, arXiv.org.
    26. Susan Athey & Stefan Wager, 2017. "Policy Learning with Observational Data," Papers 1702.02896, arXiv.org, revised Sep 2020.
    27. Jing Kong, 2025. "On the Asymptotics of the Minimax Linear Estimator," Papers 2510.16661, arXiv.org.
    28. Bertanha, Marinho & Moreira, Marcelo J., 2020. "Impossible inference in econometrics: Theory and applications," Journal of Econometrics, Elsevier, vol. 218(2), pages 247-270.
    29. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    30. 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.
    31. Bai, Yuehao, 2023. "Why randomize? Minimax optimality under permutation invariance," Journal of Econometrics, Elsevier, vol. 232(2), pages 565-575.
    32. 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.
    33. Xie, Haitian, 2024. "Nonlinear and nonseparable structural functions in regression discontinuity designs with a continuous treatment," Journal of Econometrics, Elsevier, vol. 242(1).
    34. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    35. Kline, Patrick, 2024. "Firm wage effects," Handbook of Labor Economics,, Elsevier.
    36. Claudia Noack & Christoph Rothe, 2024. "Bias‐Aware Inference in Fuzzy Regression Discontinuity Designs," Econometrica, Econometric Society, vol. 92(3), pages 687-711, May.
    37. Bugni, Federico A. & Canay, Ivan A., 2021. "Testing continuity of a density via g-order statistics in the regression discontinuity design," Journal of Econometrics, Elsevier, vol. 221(1), pages 138-159.
    38. Chenchuan (Mark) Li & Ulrich K. Müller, 2020. "Linear Regression with Many Controls of Limited Explanatory Power," Working Papers 2020-57, Princeton University. Economics Department..
    39. Yiqi Liu & Yuan Qi, 2023. "Using Forests in Multivariate Regression Discontinuity Designs," Papers 2303.11721, arXiv.org, revised Jun 2025.
    40. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," CEI Working Paper Series 2021-05, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    41. Jing Kong, 2025. "Causal Inference in High-Dimensional Generalized Linear Models with Binary Outcomes," Papers 2510.16669, arXiv.org.
    42. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.
    43. Kohei Yata, 2021. "Optimal Decision Rules Under Partial Identification," Papers 2111.04926, arXiv.org, revised Mar 2025.
    44. Federico A. Bugni & Ivan A. Canay & Deborah Kim, 2025. "Testing Conditional Stochastic Dominance at Target Points," Papers 2503.14747, arXiv.org, revised Nov 2025.
    45. José Luis Montiel Olea & Mikkel Plagborg‐Møller, 2021. "Local Projection Inference Is Simpler and More Robust Than You Think," Econometrica, Econometric Society, vol. 89(4), pages 1789-1823, July.
    46. Timothy B. Armstrong & Martin Weidner & Andrei Zeleneev, 2024. "Robust estimation and inference in panels with interactive fixed effects," CeMMAP working papers 28/24, Institute for Fiscal Studies.
    47. Xiao Huang & Zhaoguo Zhan, 2020. "Local Composite Quantile Regression for Regression Discontinuity," Papers 2009.03716, arXiv.org, revised Oct 2021.
    48. Narita, Yusuke & Yata, Kohei, 2022. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Discussion Paper Series 730, Institute of Economic Research, Hitotsubashi University.
    49. Huynh, Nhan, 2023. "Unemployment beta and the cross-section of stock returns: Evidence from Australia," International Review of Financial Analysis, Elsevier, vol. 86(C).
    50. Sasaki, Yuya & Wang, Yulong, 2024. "On uniform confidence intervals for the tail index and the extreme quantile," Journal of Econometrics, Elsevier, vol. 244(1).
    51. Walter Beckert & Daniel Kaliski, 2019. "Honest inference for discrete outcomes," CeMMAP working papers CWP67/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    52. Taneesha Datta, 2025. "Political representation and judicial outcomes: Eidence from India," CSAE Working Paper Series 2025-11, Centre for the Study of African Economies, University of Oxford.
    53. Tuvaandorj, Purevdorj, 2020. "Regression discontinuity designs, white noise models, and minimax," Journal of Econometrics, Elsevier, vol. 218(2), pages 587-608.
    54. Yingying Dong & Michal Kolesár, 2023. "When can we ignore measurement error in the running variable?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 735-750, August.

  9. Isaiah Andrews & Timothy B. Armstrong, 2015. "Unbiased Instrumental Variables Estimation under Known First-Stage Sign," Cowles Foundation Discussion Papers 1984R5, Cowles Foundation for Research in Economics, Yale University, revised Nov 2016.

    Cited by:

    1. Tetsuya Kaji, 2019. "Theory of Weak Identification in Semiparametric Models," Papers 1908.10478, arXiv.org, revised Aug 2020.
    2. Karthik Rajkumar, 2019. "Ridge regularization for Mean Squared Error Reduction in Regression with Weak Instruments," Papers 1904.08580, arXiv.org.
    3. Angrist, Joshua & Kolesár, Michal, 2024. "One instrument to rule them all: The bias and coverage of just-ID IV," Journal of Econometrics, Elsevier, vol. 240(2).
    4. Timothy Derdenger & Vineet Kumar, 2019. "Estimating dynamic discrete choice models with aggregate data: Properties of the inclusive value approximation," Quantitative Marketing and Economics (QME), Springer, vol. 17(4), pages 359-384, December.
    5. Matthew C. Harding & Jerry Hausman & Christopher Palmer, 2015. "Finite sample bias corrected IV estimation for weak and many instruments," CeMMAP working papers CWP41/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Sebastián Amador, 2022. "Hysteresis, endogenous growth, and monetary policy," Working Papers 348, University of California, Davis, Department of Economics.
    7. Roach, Travis & Nath, Saheli, 2023. "Counties with More Vietnam Veterans Have Higher Suicide Rates," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 53(01), April.
    8. Müller, Ulrich K. & Wang, Yulong, 2019. "Nearly weighted risk minimal unbiased estimation," Journal of Econometrics, Elsevier, vol. 209(1), pages 18-34.
    9. Khan, Umair & Khalid, Umair & Farooq, Fatima, 2021. "Endogeneity Quagmire Empirical Evidence from Telecommunication Industry of Pakistan," Journal of Accounting and Finance in Emerging Economies, CSRC Publishing, Center for Sustainability Research and Consultancy Pakistan, vol. 7(4), pages 955-967, December.
    10. Bunkanwanicha, Pramuan & Di Giuli, Alberta & Salvade, Federica, 2022. "Bank CEO careers after bailouts: The effects of management turnover on bank risk," Journal of Financial Intermediation, Elsevier, vol. 52(C).
    11. 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.
    12. Callaway, Brantly & Karami, Sonia, 2023. "Treatment effects in interactive fixed effects models with a small number of time periods," Journal of Econometrics, Elsevier, vol. 233(1), pages 184-208.
    13. Brassiolo, Pablo & Estrada, Ricardo & Fajardo, Gustavo & Vargas, Juan, 2021. "Self-Selection into corruption: Evidence from the lab," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 799-812.
    14. David M. Kaplan, 2019. "Unbiased Estimation as a Public Good," Working Papers 1911, Department of Economics, University of Missouri.
    15. Matthew C. Harding & Jerry Hausman & Christopher Palmer, 2015. "Finite sample bias corrected IV estimation for weak and many instruments," CeMMAP working papers 41/15, Institute for Fiscal Studies.
    16. Michael Keane & Timothy Neal, 2021. "A Practical Guide to Weak Instruments," Discussion Papers 2021-05b, School of Economics, The University of New South Wales.
    17. Tetsuya Kaji, 2021. "Theory of Weak Identification in Semiparametric Models," Econometrica, Econometric Society, vol. 89(2), pages 733-763, March.
    18. Abadie, Alberto & Gu, Jiaying & Shen, Shu, 2024. "Instrumental variable estimation with first-stage heterogeneity," Journal of Econometrics, Elsevier, vol. 240(2).

  10. Timothy B. Armstrong & Michal Kolesar, 2014. "A Simple Adjustment for Bandwidth Snooping," Cowles Foundation Discussion Papers 1961R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2015.

    Cited by:

    1. Marianna Nitti & Marco Ventura, 2025. "rdlasso: Regression Discontinuity with High-Dimensional Data," Working Papers in Public Economics 265, Department of Economics and Law, Sapienza University of Rome.
    2. Kato, Kengo & Sasaki, Yuya, 2019. "Uniform confidence bands for nonparametric errors-in-variables regression," Journal of Econometrics, Elsevier, vol. 213(2), pages 516-555.
    3. Susanne M. Schennach, 2015. "A bias bound approach to nonparametric inference," CeMMAP working papers CWP71/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Eduardo Schirmer Finn & Eduardo Horta, 2024. "Convolution Mode Regression," Papers 2412.05736, arXiv.org.
    5. Kato, Kengo & Sasaki, Yuya, 2018. "Uniform confidence bands in deconvolution with unknown error distribution," Journal of Econometrics, Elsevier, vol. 207(1), pages 129-161.
    6. 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.
    7. Xu, Ke-Li, 2017. "Regression discontinuity with categorical outcomes," Journal of Econometrics, Elsevier, vol. 201(1), pages 1-18.
    8. Chen, Heng & Fan, Yanqin, 2019. "Identification and wavelet estimation of weighted ATE under discontinuous and kink incentive assignment mechanisms," Journal of Econometrics, Elsevier, vol. 212(2), pages 476-502.
    9. Jacob Dorn, 2025. "How Much Weak Overlap Can Doubly Robust T-Statistics Handle?," Papers 2504.13273, arXiv.org, revised Apr 2025.
    10. Xu, Ke-Li, 2018. "A semi-nonparametric estimator of regression discontinuity design with discrete duration outcomes," Journal of Econometrics, Elsevier, vol. 206(1), pages 258-278.
    11. Sasaki, Yuya & Wang, Yulong, 2024. "On uniform confidence intervals for the tail index and the extreme quantile," Journal of Econometrics, Elsevier, vol. 244(1).
    12. Jun Ma & Zhengfei Yu, 2020. "Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs," Papers 2008.09263, arXiv.org, revised May 2024.

  11. Timothy B. Armstrong, 2014. "A Note on Minimax Testing and Confidence Intervals in Moment Inequality Models," Cowles Foundation Discussion Papers 1975, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Armstrong, Timothy B., 2018. "On the choice of test statistic for conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 203(2), pages 241-255.

  12. Timothy B. Armstrong, 2014. "Adaptive Testing on a Regression Function at a Point," Cowles Foundation Discussion Papers 1957, Cowles Foundation for Research in Economics, Yale University, revised Oct 2014.

    Cited by:

    1. Philipp Ketz & Adam McCloskey, 2021. "Short and Simple Confidence Intervals when the Directions of Some Effects are Known," Papers 2109.08222, arXiv.org.
    2. Koohyun Kwon & Soonwoo Kwon, 2020. "Inference in Regression Discontinuity Designs under Monotonicity," Papers 2011.14216, arXiv.org.
    3. Byunghoon Kang, 2017. "Inference in Nonparametric Series Estimation with Data-Dependent Undersmoothing," Working Papers 170712442, Lancaster University Management School, Economics Department.
    4. Victor Chernozhukov & Whitney K. Newey & Andres Santos, 2015. "Constrained conditional moment restriction models," CeMMAP working papers CWP59/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Susanne M. Schennach, 2015. "A bias bound approach to nonparametric inference," CeMMAP working papers CWP71/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Piet Groeneboom & Geurt Jongbloed, 2024. "Confidence intervals in monotone regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(4), pages 1749-1781, December.
    7. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    8. Babii, Andrii & Kumar, Rohit, 2023. "Isotonic regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 234(2), pages 371-393.
    9. Koohyun Kwon & Soonwoo Kwon, 2020. "Adaptive Inference in Multivariate Nonparametric Regression Models Under Monotonicity," Papers 2011.14219, arXiv.org.
    10. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    11. 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.
    12. Patrick Kline & Evan K Rose & Christopher R Walters, 2023. "Systemic Discrimination Among Large U.S. Employers," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(4), pages 1963-2036.
    13. Timothy B. Armstrong & Michal Koles�r, 2016. "Optimal Inference in a Class of Regression Models," Cowles Foundation Discussion Papers 2043, Cowles Foundation for Research in Economics, Yale University.
    14. 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.
    15. Patrick Kline & Christopher Walters, 2019. "Audits as Evidence: Experiments, Ensembles, and Enforcement," Papers 1907.06622, arXiv.org, revised Jul 2019.

  13. Timothy B. Armstrong & Shu Shen, 2013. "Inference on Optimal Treatment Assignments," Cowles Foundation Discussion Papers 1927R, Cowles Foundation for Research in Economics, Yale University, revised Apr 2014.

    Cited by:

    1. Toru Kitagawa & Aleksey Tetenov, 2015. "Who should be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Carlo Alberto Notebooks 402, Collegio Carlo Alberto.
    2. Davide Viviano & Jess Rudder, 2020. "Policy design in experiments with unknown interference," Papers 2011.08174, arXiv.org, revised May 2024.
    3. Davide Viviano, 2019. "Policy Targeting under Network Interference," Papers 1906.10258, arXiv.org, revised Apr 2024.
    4. Lehrer, Steven F. & Pohl, R. Vincent & Song, Kyungchul, 2019. "Multiple testing and the distributional effects of accountability incentives in education," Ruhr Economic Papers 799, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    5. Juliano Assunção & Robert McMillan & Joshua Murphy & Eduardo Souza-Rodrigues, 2019. "Optimal Environmental Targeting in the Amazon Rainforest," NBER Working Papers 25636, National Bureau of Economic Research, Inc.
    6. Eric Mbakop & Max Tabord‐Meehan, 2021. "Model Selection for Treatment Choice: Penalized Welfare Maximization," Econometrica, Econometric Society, vol. 89(2), pages 825-848, March.
    7. Yuya Sasaki & Takuya Ura, 2020. "Welfare Analysis via Marginal Treatment Effects," Papers 2012.07624, arXiv.org.
    8. Shosei Sakaguchi, 2021. "Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints," Papers 2106.05031, arXiv.org, revised Aug 2024.
    9. Toru Kitagawa & Aleksey Tetenov, 2015. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers 10/15, Institute for Fiscal Studies.
    10. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
    11. Undral Byambadalai, 2022. "Identification and Inference for Welfare Gains without Unconfoundedness," Papers 2207.04314, arXiv.org.
    12. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.

  14. Timothy B. Armstrong & Hock Peng Chan, 2013. "Multiscale Adaptive Inference on Conditional Moment Inequalities," Cowles Foundation Discussion Papers 1885R, Cowles Foundation for Research in Economics, Yale University, revised Dec 2015.

    Cited by:

    1. Ariel Pakes & Jack Porter, 2024. "Moment inequalities for multinomial choice with fixed effects," Quantitative Economics, Econometric Society, vol. 15(1), pages 1-25, January.
    2. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," CeMMAP working papers 51/17, Institute for Fiscal Studies.
    3. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    4. Armstrong, Timothy B., 2018. "On the choice of test statistic for conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 203(2), pages 241-255.
    5. Timothy B. Armstrong & Michal Kolesár & Soonwoo Kwon, 2020. "Bias-Aware Inference in Regularized Regression Models," Working Papers 2020-2, Princeton University. Economics Department..
    6. Andrews, Donald W.K. & Shi, Xiaoxia, 2014. "Nonparametric inference based on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 179(1), pages 31-45.
    7. João Madeira & Nuno Palma, 2018. "Measuring Monetary Policy Deviations from the Taylor Rule," Economics Discussion Paper Series 1803, Economics, The University of Manchester.
    8. 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.
    9. Victor Chernozhukov & Wooyoung Kim & Sokbae (Simon) Lee & Adam Rosen, 2014. "Implementing intersection bounds in Stata," CeMMAP working papers CWP25/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    10. 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.
    11. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers CWP23/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Vogt, Michael & Linton, Oliver, 2020. "Multiscale clustering of nonparametric regression curves," Journal of Econometrics, Elsevier, vol. 216(1), pages 305-325.
    13. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. 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.
    15. 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.
    16. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers CWP53/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Nick Koning & Paul Bekker, 2019. "Exact Testing of Many Moment Inequalities Against Multiple Violations," Papers 1904.12775, arXiv.org, revised Jun 2020.
    18. Isaac Loh, 2024. "Inference under partial identification with minimax test statistics," Papers 2401.13057, arXiv.org, revised Apr 2024.
    19. 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.
    20. 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.
    21. Harold D. Chiang & Kengo Kato & Yuya Sasaki, 2020. "Inference for high-dimensional exchangeable arrays," Papers 2009.05150, arXiv.org, revised Jul 2021.
    22. Toru Kitagawa, 2013. "A bootstrap test for instrument validity in heterogeneous treatment effect models," CeMMAP working papers 53/13, Institute for Fiscal Studies.
    23. 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.
    24. Andrews, Donald W.K. & Shi, Xiaoxia, 2017. "Inference based on many conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 196(2), pages 275-287.
    25. Nicky L. Grant & Richard J. Smith, 2018. "GEL-Based Inference from Unconditional Moment Inequality Restrictions," Economics Discussion Paper Series 1802, Economics, The University of Manchester.
    26. Yoichi Arai & Yu‐Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2022. "Testing identifying assumptions in fuzzy regression discontinuity designs," Quantitative Economics, Econometric Society, vol. 13(1), pages 1-28, January.
    27. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    28. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers 23/18, Institute for Fiscal Studies.

Articles

  1. Timothy B. Armstrong & Michal Kolesár, 2021. "Sensitivity analysis using approximate moment condition models," Quantitative Economics, Econometric Society, vol. 12(1), pages 77-108, January.
    See citations under working paper version above.
  2. Timothy B. Armstrong & Michal Kolesár, 2021. "Finite‐Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness," Econometrica, Econometric Society, vol. 89(3), pages 1141-1177, May.
    See citations under working paper version above.
  3. Timothy B. Armstrong & Michal Kolesár, 2020. "Simple and honest confidence intervals in nonparametric regression," Quantitative Economics, Econometric Society, vol. 11(1), pages 1-39, January.
    See citations under working paper version above.
  4. Armstrong, Timothy B., 2018. "On the choice of test statistic for conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 203(2), pages 241-255.
    See citations under working paper version above.
  5. Timothy B. Armstrong & Michal Kolesár, 2018. "Optimal Inference in a Class of Regression Models," Econometrica, Econometric Society, vol. 86(2), pages 655-683, March.
    See citations under working paper version above.
  6. 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.
    See citations under working paper version above.
  7. Isaiah Andrews & Timothy B. Armstrong, 2017. "Unbiased instrumental variables estimation under known first‐stage sign," Quantitative Economics, Econometric Society, vol. 8(2), pages 479-503, July.
    See citations under working paper version above.
  8. Armstrong, Timothy B. & Chan, Hock Peng, 2016. "Multiscale adaptive inference on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 194(1), pages 24-43.
    See citations under working paper version above.
  9. Timothy B. Armstrong, 2016. "Large Market Asymptotics for Differentiated Product Demand Estimators With Economic Models of Supply," Econometrica, Econometric Society, vol. 84, pages 1961-1980, September.

    Cited by:

    1. Laura Grigolon, 2017. "Blurred boundaries: a flexible approach for segmentation applied to the car market," Department of Economics Working Papers 2017-17, McMaster University.
    2. Bartosz Olesiński, 2020. "The Analysis of the Tobacco Product Bans Using a Random Coefficients Logit Model," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(2), pages 113-144, June.
    3. Christopher T. Conlon & Julie Holland Mortimer, 2018. "Empirical Properties of Diversion Ratios," Working Papers 18-16, New York University, Leonard N. Stern School of Business, Department of Economics.
    4. Zhentong Lu & Kenichi Shimizu, 2025. "Estimating Discrete Choice Demand Models with Sparse Market-Product Shocks," Working Papers 2025-01, University of Alberta, Department of Economics.
    5. Otsu, Taisuke & Sunada, Keita, 2024. "On large market asymptotics for spatial price competition models," Economics Letters, Elsevier, vol. 234(C).
    6. Susan J. Méndez, 2018. "Parallel trade of pharmaceuticals: The Danish market for statins," Health Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 333-356, February.
    7. Pietro Tebaldi & Alexander Torgovitsky & Hanbin Yang, 2019. "Nonparametric Estimates of Demand in the California Health Insurance Exchange," NBER Working Papers 25827, National Bureau of Economic Research, Inc.
    8. Fabian Dunker & Stefan Hoderlein & Hiroaki Kaido, 2023. "Nonparametric identification of random coefficients in aggregate demand models for differentiated products," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 279-306.
    9. Otsu, Taisuke & Sunada, Keita, 2024. "On large market asymptotics for spatial price competition models," LSE Research Online Documents on Economics 120588, London School of Economics and Political Science, LSE Library.
    10. Xavier D'Haultfoeuille & Isis Durrmeyer & Philippe Février, 2017. "Automobile Prices in Market Equilibrium with Unobserved Price Discrimination," Working Papers 2017-18, Center for Research in Economics and Statistics.
    11. Masayuki Sawada & Kohei Kawaguchi, 2020. "Estimating High-Dimensional Discrete Choice Model of Differentiated Products with Random Coefficients," Papers 2004.08791, arXiv.org.
    12. Greg Lewis & Bora Ozaltun & Georgios Zervas, 2021. "Maximum Likelihood Estimation of Differentiated Products Demand Systems," Papers 2111.12397, arXiv.org.
    13. Lu, Zhentong & Shi, Xiaoxia & Tao, Jing, 2023. "Semi-nonparametric estimation of random coefficients logit model for aggregate demand," Journal of Econometrics, Elsevier, vol. 235(2), pages 2245-2265.
    14. Sukjin Han & Kyungho Lee, 2025. "Copyright and Competition: Estimating Supply and Demand with Unstructured Data," Bristol Economics Discussion Papers 25/816, School of Economics, University of Bristol, UK.
    15. Lu, Tingmingke, 2023. "Response of new car buyers to alternative energy policies: The role of vehicle use heterogeneity," Economic Modelling, Elsevier, vol. 120(C).
    16. Afonso Rodrigues, 2025. "Consumer Choice Over Shopping Baskets: A Linear Demand Approach," Papers 2511.11846, arXiv.org.
    17. Néstor Duch-Brown & Lukasz Grzybowski & André Romahn & Frank Verboven, 2022. "Evaluating the Impact of Online Market Integration-Evidence from the EU Portable PC Market," Working Papers hal-03780118, HAL.
    18. Mandys, Filip & Taneja, Shivani, 2024. "Demand for green and fossil fuel automobiles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).
    19. Duarte, Marco, 2025. "Extending fixed-point methods for equilibrium computation in markets with differentiated products," Economics Letters, Elsevier, vol. 250(C).
    20. Hugo Molina, 2026. "Buyer Alliances in Vertically Related Markets [Alliances d'acheteurs sur les marchés verticalement liés]," Working Papers hal-03340176, HAL.
    21. Gabaix, Xavier & Laibson, David & Li, Deyuan & Li, Hongyi & Resnick, Sidney & de Vries, Casper G., 2016. "The impact of competition on prices with numerous firms," Journal of Economic Theory, Elsevier, vol. 165(C), pages 1-24.
    22. Isaiah Andrews & Anna Mikusheva, 2022. "GMM is Inadmissible Under Weak Identification," Papers 2204.12462, arXiv.org, revised May 2023.
    23. 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.
    24. Pál, László & Sándor, Zsolt, 2023. "Comparing procedures for estimating random coefficient logit demand models with a special focus on obtaining global optima," International Journal of Industrial Organization, Elsevier, vol. 88(C).
    25. 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.
    26. Salanié, Bernard & Wolak, Frank, 2018. "Fast, “Robust†, and Approximately Correct: Estimating Mixed Demand Systems," CEPR Discussion Papers 13236, C.E.P.R. Discussion Papers.
    27. Sukjin Han & Kyungho Lee, 2025. "Copyright and Competition: Estimating Supply and Demand with Unstructured Data," Papers 2501.16120, arXiv.org, revised Sep 2025.
    28. Givord, Pauline & Grislain-Letrémy, Céline & Naegele, Helene, 2018. "How do fuel taxes impact new car purchases? An evaluation using French consumer-level data," Energy Economics, Elsevier, vol. 74(C), pages 76-96.
    29. Amit Gandhi & Jean-François Houde, 2019. "Measuring Substitution Patterns in Differentiated-Products Industries," NBER Working Papers 26375, National Bureau of Economic Research, Inc.
    30. Wang, Ao, 2021. "A BLP Demand Model of Product-Level Market Shares with Complementarity," The Warwick Economics Research Paper Series (TWERPS) 1351, University of Warwick, Department of Economics.
    31. Bernard Salanie & Frank A. Wolak, 2018. "Fast, "robust", and approximately correct: estimating mixed demand systems," CeMMAP working papers CWP64/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    32. Ketz, Philipp, 2019. "On asymptotic size distortions in the random coefficients logit model," Journal of Econometrics, Elsevier, vol. 212(2), pages 413-432.
    33. Kirill Borusyak & Peter Hull & Mauricio Caceres Bravo, 2025. "Estimating demand with recentered instruments," CeMMAP working papers 10/25, Institute for Fiscal Studies.
    34. Iaria, Alessandro & ,, 2020. "Identification and Estimation of Demand for Bundles," CEPR Discussion Papers 14363, C.E.P.R. Discussion Papers.
    35. Marco Duarte & Lorenzo Magnolfi & Mikkel Sølvsten & Christopher Sullivan, 2024. "Testing firm conduct," Quantitative Economics, Econometric Society, vol. 15(3), pages 571-606, July.
      • Marco Duarte & Lorenzo Magnolfi & Mikkel S{o}lvsten & Christopher Sullivan, 2023. "Testing Firm Conduct," Papers 2301.06720, arXiv.org, revised Jan 2024.
    36. Kandelhardt, Johannes, 2023. "Flexible estimation of random coefficient logit models of differentiated product demand," DICE Discussion Papers 399, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    37. Christopher Conlon & Jeff Gortmaker, 2020. "Best practices for differentiated products demand estimation with PyBLP," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 1108-1161, December.
    38. Matthew Weinberg & Gloria Sheu & Nathan Miller, 2019. "Oligopolistic Price Leadership and Mergers: An Empirical Model of the U.S. Beer Industry," 2019 Meeting Papers 1210, Society for Economic Dynamics.
    39. Molina, Hugo & Wang, Ao, 2024. "Vertical Bargaining under Uncertain Retailer Responsiveness : A Structural Approach," The Warwick Economics Research Paper Series (TWERPS) 1534, University of Warwick, Department of Economics.
    40. Steven T. Berry & Philip A. Haile, 2021. "Foundations of Demand Estimation," Cowles Foundation Discussion Papers 2301, Cowles Foundation for Research in Economics, Yale University.
    41. Amit Gandhi & Zhentong Lu & Xiaoxia Shi, 2023. "Estimating demand for differentiated products with zeroes in market share data," Quantitative Economics, Econometric Society, vol. 14(2), pages 381-418, May.
    42. Mogens Fosgerau & Julien Monardo & André de Palma, 2019. "The Inverse Product Differentiation Logit Model," Working Papers hal-02183411, HAL.

  10. Armstrong, Timothy B., 2015. "Asymptotically exact inference in conditional moment inequality models," Journal of Econometrics, Elsevier, vol. 186(1), pages 51-65.

    Cited by:

    1. Ariel Pakes & Jack Porter, 2024. "Moment inequalities for multinomial choice with fixed effects," Quantitative Economics, Econometric Society, vol. 15(1), pages 1-25, January.
    2. Le-Yu Chen & Sokbae (Simon) Lee, 2015. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," CeMMAP working papers 26/15, Institute for Fiscal Studies.
    3. Christian Bontemps & Thierry Magnac, 2017. "Set identification, moment restrictions, and inference," Post-Print hal-01575813, HAL.
    4. Li, Jia & Liao, Zhipeng & Zhou, Wenyu, 2025. "A general test for functional inequalities," Journal of Econometrics, Elsevier, vol. 251(C).
    5. Armstrong, Timothy B., 2018. "On the choice of test statistic for conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 203(2), pages 241-255.
    6. João Madeira & Nuno Palma, 2018. "Measuring Monetary Policy Deviations from the Taylor Rule," Economics Discussion Paper Series 1803, Economics, The University of Manchester.
    7. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers CWP23/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. 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.
    9. Rui Wang, 2023. "Testing and Identifying Substitution and Complementarity Patterns," Papers 2304.00678, arXiv.org.
    10. 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.
    11. 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.
    12. Andres Aradillas-Lopez & Adam Rosen, 2021. "Inference in ordered response games with complete information," CeMMAP working papers CWP37/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    13. Aradillas-López, Andrés & Gandhi, Amit & Quint, Daniel, 2016. "A simple test for moment inequality models with an application to English auctions," Journal of Econometrics, Elsevier, vol. 194(1), pages 96-115.
    14. Yuehao Bai & Shunzhuang Huang & Max Tabord-Meehan, 2024. "Sharp Testable Implications of Encouragement Designs," Papers 2411.09808, arXiv.org, revised Nov 2025.
    15. Andrews, Donald W.K. & Shi, Xiaoxia, 2017. "Inference based on many conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 196(2), pages 275-287.
    16. Nicky L. Grant & Richard J. Smith, 2018. "GEL-Based Inference from Unconditional Moment Inequality Restrictions," Economics Discussion Paper Series 1802, Economics, The University of Manchester.
    17. Lorenz Gschwent & Bjorn Hammarfelt & Martin Karlsson & Mathias Kifmann, 2024. "The Rise of Health Economics: Transforming the Landscape of Economic Research," Papers 2410.06313, arXiv.org.
    18. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers 23/18, Institute for Fiscal Studies.
    19. Breunig, Christoph, 2015. "Testing missing at random using instrumental variables," SFB 649 Discussion Papers 2015-016, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  11. Armstrong, Timothy B., 2014. "Weighted KS statistics for inference on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 181(2), pages 92-116.

    Cited by:

    1. Ariel Pakes & Jack Porter, 2024. "Moment inequalities for multinomial choice with fixed effects," Quantitative Economics, Econometric Society, vol. 15(1), pages 1-25, January.
    2. Xiaohong Chen & Timothy Christensen & Keith O’Hara & Elie Tamer, 2016. "MCMC Confidence sets for Identified Sets," Cowles Foundation Discussion Papers 2037R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2016.
    3. Le-Yu Chen & Sokbae (Simon) Lee, 2015. "Breaking the curse of dimensionality in conditional moment inequalities for discrete choice models," CeMMAP working papers 26/15, Institute for Fiscal Studies.
    4. 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.
    5. Christian Bontemps & Thierry Magnac, 2017. "Set identification, moment restrictions, and inference," Post-Print hal-01575813, HAL.
    6. Federico A. Bugni & Ivan A. Canay & Xiaoxia Shi, 2015. "Inference for functions of partially identified parameters in moment inequality models," CeMMAP working papers 54/15, Institute for Fiscal Studies.
    7. Armstrong, Timothy B., 2018. "On the choice of test statistic for conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 203(2), pages 241-255.
    8. Hiroaki Kaido & Yi Zhang, 2019. "Robust likelihood ratio tests for incomplete economic models," CeMMAP working papers CWP68/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Shosei Sakaguchi, 2021. "Partial Identification and Inference in Duration Models with Endogenous Censoring," Papers 2107.00928, arXiv.org.
    10. João Madeira & Nuno Palma, 2018. "Measuring Monetary Policy Deviations from the Taylor Rule," Economics Discussion Paper Series 1803, Economics, The University of Manchester.
    11. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers 43/17, Institute for Fiscal Studies.
    12. 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.
    13. Andrei Voronin, 2025. "Generalized Optimal Transport," Papers 2507.22422, arXiv.org.
    14. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers CWP23/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Bugni, Federico A. & Canay, Ivan A. & Shi, Xiaoxia, 2015. "Specification tests for partially identified models defined by moment inequalities," Journal of Econometrics, Elsevier, vol. 185(1), pages 259-282.
    16. 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.
    17. Armstrong, Timothy B., 2015. "Asymptotically exact inference in conditional moment inequality models," Journal of Econometrics, Elsevier, vol. 186(1), pages 51-65.
    18. 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.
    19. Sasaki, Yuya & Takahashi, Yuya & Xin, Yi & Hu, Yingyao, 2023. "Dynamic discrete choice models with incomplete data: Sharp identification," Journal of Econometrics, Elsevier, vol. 236(1).
    20. 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.
    21. 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.
    22. Andres Aradillas-Lopez & Adam Rosen, 2021. "Inference in ordered response games with complete information," CeMMAP working papers CWP37/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    23. Harold D. Chiang & Kengo Kato & Yuya Sasaki, 2020. "Inference for high-dimensional exchangeable arrays," Papers 2009.05150, arXiv.org, revised Jul 2021.
    24. Aradillas-López, Andrés & Gandhi, Amit & Quint, Daniel, 2016. "A simple test for moment inequality models with an application to English auctions," Journal of Econometrics, Elsevier, vol. 194(1), pages 96-115.
    25. Yuehao Bai & Shunzhuang Huang & Max Tabord-Meehan, 2024. "Sharp Testable Implications of Encouragement Designs," Papers 2411.09808, arXiv.org, revised Nov 2025.
    26. Andrews, Donald W.K. & Shi, Xiaoxia, 2017. "Inference based on many conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 196(2), pages 275-287.
    27. Nicky L. Grant & Richard J. Smith, 2018. "GEL-Based Inference from Unconditional Moment Inequality Restrictions," Economics Discussion Paper Series 1802, Economics, The University of Manchester.
    28. Armstrong, Timothy B. & Chan, Hock Peng, 2016. "Multiscale adaptive inference on conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 194(1), pages 24-43.
    29. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    30. Nicky L. Grant & Richard J. Smith, 2018. "GEL-based inference with unconditional moment inequality restrictions," CeMMAP working papers 23/18, Institute for Fiscal Studies.

  12. Armstrong, Timothy B. & Bertanha, Marinho & Hong, Han, 2014. "A fast resample method for parametric and semiparametric models," Journal of Econometrics, Elsevier, vol. 179(2), pages 128-133.

    Cited by:

    1. Shao, Zhen & Gao, Fei & Zhang, Qiang & Yang, Shan-Lin, 2015. "Multivariate statistical and similarity measure based semiparametric modeling of the probability distribution: A novel approach to the case study of mid-long term electricity consumption forecasting in China," Applied Energy, Elsevier, vol. 156(C), pages 502-518.
    2. Aristide Houndetoungan & Abdoul Haki Maoude, 2024. "Inference for Two-Stage Extremum Estimators," Papers 2402.05030, arXiv.org, revised Nov 2024.
    3. Jean-Jacques Forneron, 2022. "Estimation and Inference by Stochastic Optimization," Papers 2205.03254, arXiv.org.
    4. Forneron, Jean-Jacques, 2024. "Estimation and inference by stochastic optimization," Journal of Econometrics, Elsevier, vol. 238(2).
    5. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn, 2014. "Asymptotic efficiency of semiparametric two-step GMM," CeMMAP working papers CWP28/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Jinyong Hahn & Zhipeng Liao, 2021. "Bootstrap Standard Error Estimates and Inference," Econometrica, Econometric Society, vol. 89(4), pages 1963-1977, July.
    7. Chen, Xiaohong & Liao, Zhipeng, 2015. "Sieve semiparametric two-step GMM under weak dependence," Journal of Econometrics, Elsevier, vol. 189(1), pages 163-186.
    8. Antonia Antweiler & Joachim Freyberger, 2025. "Flexible estimation of skill formation models," Papers 2507.18995, arXiv.org.
    9. Jean-Jacques Forneron & Serena Ng, 2020. "Inference by Stochastic Optimization: A Free-Lunch Bootstrap," Papers 2004.09627, arXiv.org, revised Sep 2020.
    10. Hong, Han & Li, Weiming & Wang, Boyu, 2015. "Estimation of dynamic discrete models from time aggregated data," Journal of Econometrics, Elsevier, vol. 188(2), pages 435-446.
    11. Davide La Vecchia & Alban Moor & Olivier Scaillet, 2020. "A Higher-Order Correct Fast Moving-Average Bootstrap for Dependent Data," Papers 2001.04867, arXiv.org, revised Jan 2022.

  13. Timothy B. Armstrong, 2013. "Bounds in auctions with unobserved heterogeneity," Quantitative Economics, Econometric Society, vol. 4(3), pages 377-415, November.

    Cited by:

    1. Yusuke Matsuki, 2016. "A Distribution-Free Test of Monotonicity with an Application to Auctions," Working Papers e110, Tokyo Center for Economic Research.
    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. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    4. Luo, Yao, 2020. "Unobserved heterogeneity in auctions under restricted stochastic dominance," Journal of Econometrics, Elsevier, vol. 216(2), pages 354-374.
    5. Yan, Zhen & Han, Fei & Wang, Holly & Shen, Yun & Zhou, Jiehong, 2024. "Purchasing habits, age effects and Chinese consumers' willingness to pay for chilled pork: Evidence from a random Nth-price auction experiment," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 68(02), January.
    6. Andrew Chesher & Adam Rosen, 2017. "Incomplete English auction models with heterogeneity," CeMMAP working papers CWP27/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Joachim Freyberger & Bradley J. Larsen, 2022. "Identification in ascending auctions, with an application to digital rights management," Quantitative Economics, Econometric Society, vol. 13(2), pages 505-543, May.
    8. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Andrew Chesher & Adam Rosen, 2015. "Identification of the distribution of valuations in an incomplete model of English auctions," CeMMAP working papers 30/15, Institute for Fiscal Studies.
    10. Giovanni Compiani & Phil Haile & Marcelo Sant'Anna, 2018. "Common values, unobserved heterogeneity, and endogenous entry in U.S. offshore oil lease auctions," CeMMAP working papers CWP37/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Yao Luo & Yuanyuan Wan, 2018. "Integrated-Quantile-Based Estimation for First-Price Auction Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 173-180, January.

Software components

    Sorry, no citations of software components recorded.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.