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Ye Luo

Personal Details

First Name:Ye
Middle Name:
Last Name:Luo
Suffix:
RePEc Short-ID:plu554
[This author has chosen not to make the email address public]
https://www.hkubs.hku.hk/people/ye-luo/

Affiliation

(50%) Faculty of Business and Economics
University of Hong Kong

Pokfulam, Hong Kong
http://www.fbe.hku.hk/
RePEc:edi:fbhkuhk (more details at EDIRC)

(50%) School of Economics
University of Queensland

Brisbane, Australia
https://economics.uq.edu.au/
RePEc:edi:decuqau (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Qiang Chen & Tianyang Han & Jin Li & Ye Luo & Yuxiao Wu & Xiaowei Zhang & Tuo Zhou, 2025. "Can AI Master Econometrics? Evidence from Econometrics AI Agent on Expert-Level Tasks," Papers 2506.00856, arXiv.org.
  2. Hanzhe Li & Jin Li & Ye Luo & Xiaowei Zhang, 2024. "AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors," Papers 2410.01114, arXiv.org.
  3. Jin Li & Ye Luo & Xiaowei Zhang, 2024. "Seesaw Experimentation: A/B Tests with Spillovers," Papers 2411.02085, arXiv.org, revised Jan 2025.
  4. Jin Li & Ye Luo & Xiaowei Zhang, 2021. "Dynamic Selection in Algorithmic Decision-making," Papers 2108.12547, arXiv.org, revised Sep 2023.
  5. Jin Li & Ye Luo & Zigan Wang & Xiaowei Zhang, 2021. "Asymptotic Theory for IV-Based Reinforcement Learning with Potential Endogeneity," Papers 2103.04021, arXiv.org, revised Dec 2024.
  6. Jerry A. Hausman & Haoyang Liu & Ye Luo & Christopher Palmer, 2019. "Errors in the Dependent Variable of Quantile Regression Models," NBER Working Papers 25819, National Bureau of Economic Research, Inc.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. Luo, Ye & Spindler, Martin, 2017. "L2-Boosting for Economic Applications," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168194, Verein für Socialpolitik / German Economic Association.
  12. Ye Luo & Martin Spindler, 2017. "$L_2$Boosting for Economic Applications," Papers 1702.03244, arXiv.org.
  13. Ye Luo & Martin Spindler & Jannis Kuck, 2016. "High-Dimensional $L_2$Boosting: Rate of Convergence," Papers 1602.08927, arXiv.org, revised Jul 2022.
  14. 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.

Articles

  1. Ye Luo, 2024. "Special issue on machine learning and artificial intelligence in business and economics," International Studies of Economics, John Wiley & Sons, vol. 19(4), pages 470-471, December.
  2. 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.
  3. 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.
  4. Luo, Ye & Spindler, Martin & Bach, Philipp, 2019. "Dynamic Pricing mit Künstlicher Intelligenz - Fallstudie aus dem Ride-Sharing-Markt," Marketing Review St.Gallen, Universität St.Gallen, Institut für Marketing und Customer Insight, vol. 36(5), pages 48-54.
  5. Runmin Shi & Faming Liang & Qifan Song & Ye Luo & Malay Ghosh, 2018. "A Blockwise Consistency Method for Parameter Estimation of Complex Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 179-223, December.
  6. Faming Liang & Bochao Jia & Jingnan Xue & Qizhai Li & Ye Luo, 2018. "An imputation–regularized optimization algorithm for high dimensional missing data problems and beyond," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(5), pages 899-926, November.
  7. 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.
  8. Ye Luo & Hai Wang, 2017. "Core Determining Class and Inequality Selection," American Economic Review, American Economic Association, vol. 107(5), pages 274-277, May.
  9. Ye Luo & Martin Spindler, 2017. "L2-Boosting for Economic Applications," American Economic Review, American Economic Association, vol. 107(5), pages 270-273, May.

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.

Working papers

  1. Jin Li & Ye Luo & Xiaowei Zhang, 2021. "Dynamic Selection in Algorithmic Decision-making," Papers 2108.12547, arXiv.org, revised Sep 2023.

    Cited by:

    1. Riccardo Della Vecchia & Debabrota Basu, 2023. "Online Instrumental Variable Regression: Regret Analysis and Bandit Feedback," Working Papers hal-03831210, HAL.

  2. Jerry A. Hausman & Haoyang Liu & Ye Luo & Christopher Palmer, 2019. "Errors in the Dependent Variable of Quantile Regression Models," NBER Working Papers 25819, National Bureau of Economic Research, Inc.

    Cited by:

    1. Demetrescu, Matei & Hosseinkouchack, Mehdi & Rodrigues, Paulo M. M., 2023. "Tests of no cross-sectional error dependence in panel quantile regressions," Ruhr Economic Papers 1041, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. 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.
    3. Chen, Liang & Dolado, Juan J & Gonzalo, Jesus & Pan, Haozi, 2023. "Estimation of Characteristics-based Quantile Factor Models," CEPR Discussion Papers 18115, C.E.P.R. Discussion Papers.
    4. 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.
    5. Paulo M.M. Rodrigues & Matei Demetrescu, 2022. "Cross-Sectional Error Dependence in Panel Quantile Regressions," Working Papers w202213, Banco de Portugal, Economics and Research Department.
    6. Brantly Callaway & Tong Li & Irina Murtazashvili, 2021. "Distributional Effects with Two-Sided Measurement Error: An Application to Intergenerational Income Mobility," Papers 2107.09235, arXiv.org, revised Jun 2024.
    7. Zongwu Cai & Meng Shi & Yue Zhao & Wuqing Wu, 2020. "Testing Financial Hierarchy Based on A PDQ-CRE Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202011, University of Kansas, Department of Economics, revised Jul 2020.
    8. Uribe, Jorge M. & Mosquera-López, Stephania & Arenas, Oscar J., 2022. "Assessing the relationship between electricity and natural gas prices in European markets in times of distress," Energy Policy, Elsevier, vol. 166(C).

  3. 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).

  4. 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. Guido W. Imbens, 2020. "Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics," Journal of Economic Literature, American Economic Association, vol. 58(4), pages 1129-1179, December.
    4. 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.

  5. Luo, Ye & Spindler, Martin, 2017. "L2-Boosting for Economic Applications," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168194, Verein für Socialpolitik / German Economic Association.

    Cited by:

    1. 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.
    2. 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.
    3. Damian Kozbur, 2020. "Analysis of Testing‐Based Forward Model Selection," Econometrica, Econometric Society, vol. 88(5), pages 2147-2173, September.
    4. Chen, Jiafeng & Ritzwoller, David M., 2023. "Semiparametric estimation of long-term treatment effects," Journal of Econometrics, Elsevier, vol. 237(2).

  6. Ye Luo & Martin Spindler, 2017. "$L_2$Boosting for Economic Applications," Papers 1702.03244, arXiv.org.

    Cited by:

    1. Chen, Jiafeng & Ritzwoller, David M., 2023. "Semiparametric estimation of long-term treatment effects," Journal of Econometrics, Elsevier, vol. 237(2).

  7. Ye Luo & Martin Spindler & Jannis Kuck, 2016. "High-Dimensional $L_2$Boosting: Rate of Convergence," Papers 1602.08927, arXiv.org, revised Jul 2022.

    Cited by:

    1. 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.
    2. 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.
    3. Hugo Bodory & Martin Huber & Lukáš Lafférs, 2022. "Evaluating (weighted) dynamic treatment effects by double machine learning [Identification of causal effects using instrumental variables]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 628-648.
    4. Yue, Mu & Li, Jialiang & Cheng, Ming-Yen, 2019. "Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 222-234.
    5. Michela Bia & Martin Huber & Luk'av{s} Laff'ers, 2020. "Double machine learning for sample selection models," Papers 2012.00745, arXiv.org, revised Jul 2021.
    6. 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.
    7. 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.
    8. 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.
    9. Farbmacher, Helmut & Huber, Martin & Langen, Henrika & Spindler, Martin, 2020. "Causal mediation analysis with double machine learning," FSES Working Papers 515, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    10. 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.
    11. Yang, Jui-Chung & Chuang, Hui-Ching & Kuan, Chung-Ming, 2020. "Double machine learning with gradient boosting and its application to the Big N audit quality effect," Journal of Econometrics, Elsevier, vol. 216(1), pages 268-283.

  8. 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. Daniel Goller, 2020. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Papers 2008.07165, arXiv.org.
    2. Matthew A. Masten & Alexandre Poirier & Muyang Ren, 2025. "A General Approach to Relaxing Unconfoundedness," Papers 2501.15400, arXiv.org.
    3. 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.
    4. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods," Papers 1803.09015, arXiv.org, revised Dec 2020.
    5. 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.
    6. 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).
    7. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    8. 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).
    9. 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.
    10. Christian Hansen & Damian Kozbur & Sanjog Misra, 2016. "Targeted undersmoothing," ECON - Working Papers 282, Department of Economics - University of Zurich, revised Apr 2018.
    11. 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.
    12. 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.
    13. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, Institute of Labor Economics (IZA).
    14. Sookyo Jeong & Hongseok Namkoong, 2020. "Assessing External Validity Over Worst-case Subpopulations," Papers 2007.02411, arXiv.org, revised Feb 2022.
    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. Victor Chernozhukov & Ivan Fernandez-Val & Whitney K. Newey, 2017. "Nonseparable multinomial choice models in cross-section and panel data," CeMMAP working papers 33/17, Institute for Fiscal Studies.
    19. Bin Xiong & Qi Sui, 2025. "The effect of digital economy on rural workforce occupation transformation ability: Evidence from China," Palgrave 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, Institute of Labor Economics (IZA).
    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.
    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. 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.
    35. Olivier Deschenes & Christopher Malloy & Gavin G. McDonald, 2023. "Causal Effects of Renewable Portfolio Standards on Renewable Investments and Generation: The Role of Heterogeneity and Dynamics," NBER Working Papers 31568, National Bureau of Economic Research, Inc.
    36. 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.
    37. Yuehao Bai, 2022. "Optimality of Matched-Pair Designs in Randomized Controlled Trials," Papers 2206.07845, arXiv.org.
    38. 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.
    39. 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".
    40. Pullabhotla, Hemant K. & Souza, Mateus, 2022. "Air pollution from agricultural fires increases hypertension risk," Journal of Environmental Economics and Management, Elsevier, vol. 115(C).
    41. 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).
    42. 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.
    43. Ricardo Masini & Marcelo Medeiros, 2025. "Balancing Flexibility and Interpretability: A Conditional Linear Model Estimation via Random Forest," Papers 2502.13438, arXiv.org.
    44. Bernhard Boockmann & Martin Kroczek & Natalie Laub, 2023. "Tightening access to early retirement: who can adapt?," IAW Discussion Papers 142, Institut für Angewandte Wirtschaftsforschung (IAW).
    45. 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.
    46. 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.
    47. Kai Feng & Han Hong, 2024. "Statistical Inference of Optimal Allocations I: Regularities and their Implications," Papers 2403.18248, arXiv.org, revised Apr 2024.

Articles

  1. 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.

    Cited by:

    1. Ziwei Mei & Peter C. B. Phillips & Zhentao Shi, 2024. "The boosted Hodrick‐Prescott filter is more general than you might think," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(7), pages 1260-1281, November.
    2. Wei, Waverly & Zhou, Yuqing & Zheng, Zeyu & Wang, Jingshen, 2024. "Inference on the best policies with many covariates," Journal of Econometrics, Elsevier, vol. 239(2).

  2. 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.
    See citations under working paper version above.
  3. Faming Liang & Bochao Jia & Jingnan Xue & Qizhai Li & Ye Luo, 2018. "An imputation–regularized optimization algorithm for high dimensional missing data problems and beyond," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(5), pages 899-926, November.

    Cited by:

    1. Byrd, Michael & Nghiem, Linh H. & McGee, Monnie, 2021. "Bayesian regularization of Gaussian graphical models with measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    2. Jingxuan Luo & Lili Yue & Gaorong Li, 2023. "Overview of High-Dimensional Measurement Error Regression Models," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    3. Runmin Shi & Faming Liang & Qifan Song & Ye Luo & Malay Ghosh, 2018. "A Blockwise Consistency Method for Parameter Estimation of Complex Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 179-223, December.

  4. 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.
  5. Ye Luo & Hai Wang, 2017. "Core Determining Class and Inequality Selection," American Economic Review, American Economic Association, vol. 107(5), pages 274-277, May.

    Cited by:

    1. Lixiong Li & Marc Henry, 2022. "Finite Sample Inference in Incomplete Models," Papers 2204.00473, arXiv.org, revised Apr 2024.
    2. Hiroaki Kaido & Yi Zhang, 2023. "Applications of Choquet expected utility to hypothesis testing with incompleteness," The Japanese Economic Review, Springer, vol. 74(4), pages 551-572, October.

  6. Ye Luo & Martin Spindler, 2017. "L2-Boosting for Economic Applications," American Economic Review, American Economic Association, vol. 107(5), pages 270-273, May.

    Cited by:

    1. 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.
    2. 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.
    3. Damian Kozbur, 2020. "Analysis of Testing‐Based Forward Model Selection," Econometrica, Econometric Society, vol. 88(5), pages 2147-2173, September.
    4. Chen, Jiafeng & Ritzwoller, David M., 2023. "Semiparametric estimation of long-term treatment effects," Journal of Econometrics, Elsevier, vol. 237(2).

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 11 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (8) 2017-10-01 2017-10-08 2018-01-22 2018-10-01 2019-05-20 2020-01-13 2021-03-22 2021-09-06. Author is listed
  2. NEP-BIG: Big Data (6) 2017-10-01 2017-10-01 2017-10-08 2018-01-22 2020-01-13 2021-03-22. Author is listed
  3. NEP-ORE: Operations Research (3) 2017-10-08 2019-05-20 2021-03-22
  4. NEP-CMP: Computational Economics (2) 2017-10-01 2020-01-13
  5. NEP-AIN: Artificial Intelligence (1) 2024-11-11
  6. NEP-BEC: Business Economics (1) 2019-05-20
  7. NEP-CTA: Contract Theory and Applications (1) 2017-10-22
  8. NEP-ISF: Islamic Finance (1) 2021-09-06
  9. NEP-LMA: Labor Markets - Supply, Demand, and Wages (1) 2019-05-20

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