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Yu-Chin Hsu

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. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.

    Cited by:

    1. Kajal Lahiri & Cheng Yang, 2023. "A tale of two recession-derivative indicators," Empirical Economics, Springer, vol. 65(2), pages 925-947, August.

  2. Qingliang Fan & Yu-Chin Hsu & Robert P. Lieli & Yichong Zhang, 2019. "Estimation of Conditional Average Treatment Effects with High-Dimensional Data," Papers 1908.02399, arXiv.org, revised Jul 2021.

    Cited by:

    1. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
    2. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    4. Michael C. Knaus, 2020. "Double Machine Learning based Program Evaluation under Unconfoundedness," Papers 2003.03191, arXiv.org, revised Jun 2022.
    5. Arthur Charpentier & Emmanuel Flachaire & Ewen Gallic, 2023. "Optimal Transport for Counterfactual Estimation: A Method for Causal Inference," Papers 2301.07755, arXiv.org.
    6. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
    7. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Apr 2024.
    8. Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
    9. 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.
    10. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    11. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    12. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    13. Zimmert, Franziska & Zimmert, Michael, 2020. "Paid parental leave and maternal reemployment: Do part-time subsidies help or harm?," Economics Working Paper Series 2002, University of St. Gallen, School of Economics and Political Science.
    14. 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.
    15. Claudia Noack & Tomasz Olma & Christoph Rothe, 2021. "Flexible Covariate Adjustments in Regression Discontinuity Designs," Papers 2107.07942, arXiv.org, revised May 2023.
    16. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
    17. Huang, W. & Linton, O. & Zhang, Z., 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Cambridge Working Papers in Economics 2113, Faculty of Economics, University of Cambridge.
    18. 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.
    19. Adam Baybutt & Manu Navjeevan, 2023. "Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls," Papers 2301.06283, arXiv.org.

  3. Yoichi Arai & Yu-Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2019. "Testing identifying assumptions in fuzzy regression discontinuity designs," CeMMAP working papers CWP10/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    Cited by:

    1. Abdulkadiroglu, Atila & Angrist, Joshua & Narita, Yusuke & Pathak, Parag A., 2019. "Breaking Ties: Regression Discontinuity Design Meets Market Design," IZA Discussion Papers 12205, Institute of Labor Economics (IZA).
    2. Takuya Ishihara & Masayuki Sawada, 2020. "Manipulation-Robust Regression Discontinuity Designs," Papers 2009.07551, arXiv.org, revised Oct 2023.
    3. Colubi, Ana & Ramos-Guajardo, Ana Belén, 2023. "Fuzzy sets and (fuzzy) random sets in Econometrics and Statistics," Econometrics and Statistics, Elsevier, vol. 26(C), pages 84-98.
    4. Matias D. Cattaneo & Rocio Titiunik & Gonzalo Vazquez-Bare, 2019. "The Regression Discontinuity Design," Papers 1906.04242, arXiv.org, revised Jun 2020.
    5. Joshua D. Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path From Cause to Effect," Econometrica, Econometric Society, vol. 90(6), pages 2509-2539, November.
    6. Acerenza, Santiago & Bartalotti, Otávio & Kedagni, Desire, 2021. "Testing Identifying Assumptions in Bivariate Probit Models," ISU General Staff Papers 202103290700001124, Iowa State University, Department of Economics.
    7. Yingying DONG & Ying-Ying LEE & Michael GOU, 2019. "Regression Discontinuity Designs with a Continuous Treatment," Discussion papers 19058, Research Institute of Economy, Trade and Industry (RIETI).
    8. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.
    9. Mario Fiorini & Katrien Stevens, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1475-1526, December.
    10. Matias D. Cattaneo & Luke Keele & Rocio Titiunik, 2023. "A Guide to Regression Discontinuity Designs in Medical Applications," Papers 2302.07413, arXiv.org, revised May 2023.

  4. Hsu, Yu-Chin & Huber, Martin & Lee, Ying-Ying & Pipoz, Layal, 2018. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," FSES Working Papers 495, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.

    Cited by:

    1. Yu-Chin Hsu & Martin Huber & Ying-Ying Lee & Chu-An Liu, 2021. "Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data," Papers 2106.04237, arXiv.org, revised Aug 2022.
    2. Huber, Martin & Solovyeva, Anna, 2018. "Direct and indirect effects under sample selection and outcome attrition," FSES Working Papers 496, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    3. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    5. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP72/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Stefan Tubbicke, 2020. "Entropy Balancing for Continuous Treatments," Papers 2001.06281, arXiv.org, revised May 2020.
    7. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    8. Huang, W. & Linton, O. & Zhang, Z., 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Cambridge Working Papers in Economics 2113, Faculty of Economics, University of Cambridge.
    9. Numair Sani & Yizhen Xu & AmirEmad Ghassami & Ilya Shpitser, 2021. "Multiply Robust Causal Mediation Analysis with Continuous Treatments," Papers 2105.09254, arXiv.org, revised Feb 2024.
    10. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.

  5. Robert Pal Lieli & Yu-Chin Hsu, 2018. "Using the Area Under an Estimated ROC Curve to Test the Adequacy of Binary Predictors," CEU Working Papers 2018_1, Department of Economics, Central European University.

    Cited by:

    1. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
    2. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
    3. Christiansen, Charlotte & Eriksen, Jonas N. & Møller, Stig V., 2019. "Negative house price co-movements and US recessions," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 382-394.
    4. Saldarriaga, Miguel, 2018. "Credit Booms in Commodity Exporters," Working Papers 2018-008, Banco Central de Reserva del Perú.
    5. Robert P. Lieli & Yu-Chin Hsu, 2019. "Using the area under an estimated ROC curve to test the adequacy of binary predictors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 31(1), pages 100-130, January.

  6. Hsu, Yu-Chin & Huber, Martin & Lai, Tsung Chih, 2017. "Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting," FSES Working Papers 482, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.

    Cited by:

    1. Wunsch, Conny & Strobl, Renate, 2018. "Identification of Causal Mechanisms Based on Between-Subject Double Randomization Designs," IZA Discussion Papers 11626, Institute of Labor Economics (IZA).
    2. Yu-Chin Hsu & Martin Huber & Yu-Min Yen, 2023. "Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning," Papers 2307.01049, arXiv.org.
    3. Hsu, Yu-Chin & Huber, Martin & Lee, Ying-Ying & Pipoz, Layal, 2018. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," FSES Working Papers 495, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.

  7. Yu-Chin Hsu & Hsiou-Wei Lin & Kendro Vincent, 2017. "Do Cross-Sectional Stock Return Predictors Pass the Test without Data-Snooping Bias?," IEAS Working Paper : academic research 17-A003, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.

  8. Yu-Chin Hsu, 2016. "Multiplier Bootstrap for Empirical Processes," IEAS Working Paper : academic research 16-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    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. Yoichi Arai & Yu-Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2021. "Testing identifying assumptions in fuzzy regression discontinuity designs," CeMMAP working papers CWP16/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Hsu, Yu-Chin & Shen, Shu, 2019. "Testing treatment effect heterogeneity in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 208(2), pages 468-486.

  9. Yu-Chin Hsu & Chu-An Liu & Xiaoxia Shi, 2016. "Testing Generalized Regression Monotonicity," IEAS Working Paper : academic research 16-A009, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Yu-Chin Hsu & Martin Huber & Ying-Ying Lee & Chu-An Liu, 2021. "Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data," Papers 2106.04237, arXiv.org, revised Aug 2022.
    2. Kedagni, Desire & Li, Lixiong & Mourifie, Ismael, 2021. "Discordant Relaxations of Misspecified Models," ISU General Staff Papers 202107280700001131, Iowa State University, Department of Economics.
    3. Ismael Mourifie & Marc Henry & Romuald Meango, 2017. "Sharp bounds and testability of a Roy model of STEM major choices," Papers 1709.09284, arXiv.org, revised Nov 2019.
    4. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.
    5. Matthew A. Masten & Alexandre Poirier, 2018. "Interpreting Quantile Independence," Papers 1804.10957, arXiv.org.
    6. Yoici Arai & Taisuke Otsu & Mengshan Xu, 2022. "GLS under monotone heteroskedasticity," STICERD - Econometrics Paper Series 625, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    7. Chetverikov, Denis & Wilhelm, Daniel & Kim, Dongwoo, 2021. "An Adaptive Test Of Stochastic Monotonicity," Econometric Theory, Cambridge University Press, vol. 37(3), pages 495-536, June.
    8. Yu‐Chin Hsu & Shu Shen, 2021. "Testing monotonicity of conditional treatment effects under regression discontinuity designs," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 346-366, April.

  10. Yu-Chin Hsu & Shu Shen, 2016. "Testing for Treatment Effect Heterogeneity in Regression Discontinuity Design," IEAS Working Paper : academic research 16-A005, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Yoichi Arai & Yu-Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2021. "Testing identifying assumptions in fuzzy regression discontinuity designs," CeMMAP working papers CWP16/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Köppl-Turyna, Monika & Kantorowicz, Jarosław, 2017. "Disentangling fiscal effects of local constitutions," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168163, Verein für Socialpolitik / German Economic Association.
    3. Yu-Chin Hsu & Chung-Ming Kuan & Giorgio Teng-Yu Lo, 2017. "Quantile Treatment Effects in Regression Discontinuity Designs with Covariates," IEAS Working Paper : academic research 17-A009, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    4. Maria Paula Gerardino & Stephan Litschig & Dina Pomeranz, 2017. "Distortion by Audit: Evidence from Public Procurement," NBER Working Papers 23978, National Bureau of Economic Research, Inc.

  11. Yu-Chin Hsu & Robert P. Lieli & Tsung-Chih Lai, 2015. "Estimation and Inference for Distribution Functions and Quantile Functions in Endogenous Treatment Effect Models," IEAS Working Paper : academic research 15-A003, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    2. Chernozhukov, Victor & Fernández-Val, Iván & Melly, Blaise & Wüthrich, Kaspar, 2020. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," University of California at San Diego, Economics Working Paper Series qt5zm6m9rq, Department of Economics, UC San Diego.
    3. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.
    4. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    5. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.

  12. Yu-Chin Hsu & Kamhon Kan & Tsung-Chih Lai, 2015. "Distribution and Quantile Structural Functions in Treatment Effect Models: Application to Smoking Effects on Wages," IEAS Working Paper : academic research 15-A001, Institute of Economics, Academia Sinica, Taipei, Taiwan, revised Apr 2016.

    Cited by:

    1. Maasoumi, Esfandiar & Wang, Le, 2017. "What can we learn about the racial gap in the presence of sample selection?," Journal of Econometrics, Elsevier, vol. 199(2), pages 117-130.

  13. Garry F. Barrett & Stephen G. Donald & Yu-Chin Hsu, 2015. "Consistent Tests for Poverty Dominance Relations," IEAS Working Paper : academic research 15-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Tahsin Mehdi, 2020. "Testing for Stochastic Dominance up to a Common Relative Poverty Line," Econometrics, MDPI, vol. 8(1), pages 1-9, February.
    2. Greg Kaplan & Gianni La Cava & Tahlee Stone, 2018. "Household Economic Inequality in Australia," The Economic Record, The Economic Society of Australia, vol. 94(305), pages 117-134, June.
    3. Edwin Fourrier-Nicolai & Michel Lubrano, 2017. "Bayesian Inference for TIP curves: An Application to Child Poverty in Germany," AMSE Working Papers 1710, Aix-Marseille School of Economics, France.
    4. Naouel Chtioui & Mohamed Ayadi, 2018. "Rank-based poverty measures and poverty ordering with an application to Tunisia," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 17(2), pages 117-139, July.
    5. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2020. "Bayesian assessment of Lorenz and stochastic dominance," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(2), pages 767-799, May.
    6. Mariateresa Ciommi & Chiara Gigliarano & Francesco M. Chelli, 2021. "Incidence, intensity and inequality of poverty in Italy," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 75(4), pages 31-41, October-D.
    7. David Lander & David Gunawan & William E. Griffiths & Duangkamon Chotikapanich, 2016. "Bayesian Assessment of Lorenz and Stochastic Dominance Using a Mixture of Gamma Densities," Department of Economics - Working Papers Series 2023, The University of Melbourne.

  14. Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2014. "Inverse Probability Weighted Estimation of Local Average Treatment Effects: A Higher Order MSE Expansion," IEAS Working Paper : academic research 14-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan, revised Aug 2014.

    Cited by:

    1. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    2. Sloczynski, Tymon & Uysal, Derya & Wooldridge, Jeffrey M., 2022. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," IZA Discussion Papers 15241, Institute of Labor Economics (IZA).
    3. 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.
    4. Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2012. "Estimating Conditional Average Treatment Effects," CEU Working Papers 2012_16, Department of Economics, Central European University, revised 20 Jul 2012.
    5. Hsu Yu-Chin & Huber Martin & Lai Tsung-Chih, 2019. "Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-20, January.
    6. Tymon S{l}oczy'nski & S. Derya Uysal & Jeffrey M. Wooldridge, 2022. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," Papers 2204.07672, arXiv.org, revised Feb 2024.
    7. Tesfaye, Wondimagegn & Tirivayi, Nyasha, 2016. "The effect of improved storage innovations on food security and welfare in Ethiopia," MERIT Working Papers 2016-063, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    8. Tesfaye, Wondimagegn & Tirivayi, Nyasha, 2018. "The impacts of postharvest storage innovations on food security and welfare in Ethiopia," Food Policy, Elsevier, vol. 75(C), pages 52-67.
    9. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    10. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.

  15. Yi-Ting Chen & Yu-Chin Hsu & Hung-Jen Wang, 2014. "A Stochastic Frontier Model with an Effect Stochastic Frontier Models with Endogenous Selection," IEAS Working Paper : academic research 14-A006, Institute of Economics, Academia Sinica, Taipei, Taiwan, revised Sep 2015.

    Cited by:

    1. Mattsson, Pontus, 2019. "The impact of labour subsidies on total factor productivity and profit per employee," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 325-341.

  16. Tsung-Hsun Lu & Yi-Chi Chen & Yu-Chin Hsu, 2014. "Trend Definition or Holding Strategy: What Determines the Profitability of Candlestick Technical Trading Strategies?," IEAS Working Paper : academic research 14-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan, revised Jul 2015.

    Cited by:

    1. Kevin Rink, 2023. "The predictive ability of technical trading rules: an empirical analysis of developed and emerging equity markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 403-456, December.
    2. Shangkun Deng & Zhihao Su & Yanmei Ren & Haoran Yu & Yingke Zhu & Chenyang Wei, 2022. "Can Japanese Candlestick Patterns be Profitable on the Component Stocks of the SSE50 Index?," SAGE Open, , vol. 12(3), pages 21582440221, August.

  17. Wei-Ming Lee & Yu-Chin Hsu & Chung-Ming Kuan, 2014. "Robust Hypothesis Tests for M-Estimators with Possibly Non-differentiable Estimating Functions," IEAS Working Paper : academic research 14-A004, Institute of Economics, Academia Sinica, Taipei, Taiwan, revised Oct 2014.

    Cited by:

    1. Wei-Ming Lee & Chung-Ming Kuan & Yu-Chin Hsu, 2014. "Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix," IEAS Working Paper : academic research 14-A001, Institute of Economics, Academia Sinica, Taipei, Taiwan.

  18. Wei-Ming Lee & Chung-Ming Kuan & Yu-Chin Hsu, 2014. "Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix," IEAS Working Paper : academic research 14-A001, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Wei-Ming Lee & Chung-Ming Kuan & Yu-Chin Hsu, 2014. "Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix," IEAS Working Paper : academic research 14-A001, Institute of Economics, Academia Sinica, Taipei, Taiwan.

  19. Yu-Chin Hsu & Xiaoxia Shi, 2013. "Model Selection Tests for Conditional Moment Inequality Models," IEAS Working Paper : academic research 13-A004, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Susanne M. Schennach & Daniel Wilhelm, 2016. "A simple parametric model selection test," CeMMAP working papers CWP30/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Shi, Xiaoxia, 2015. "Model selection tests for moment inequality models," Journal of Econometrics, Elsevier, vol. 187(1), pages 1-17.

  20. Yu-Chin Hsu & Chung-Ming Kuan & Meng-Feng Yen, 2013. "A Generalized Stepwise Procedure with Improved Power for Multiple Inequalities Testing," IEAS Working Paper : academic research 13-A001, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Hubert Dichtl, 2020. "Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?," Review of Financial Economics, John Wiley & Sons, vol. 38(2), pages 352-378, April.
    2. Yu-Chin Hsu & Hsiou-Wei Lin & Kendro Vincent, 2017. "Do Cross-Sectional Stock Return Predictors Pass the Test without Data-Snooping Bias?," IEAS Working Paper : academic research 17-A003, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    3. Baur, Dirk G. & Dichtl, Hubert & Drobetz, Wolfgang & Wendt, Viktoria-Sophie, 2020. "Investing in gold – Market timing or buy-and-hold?," International Review of Financial Analysis, Elsevier, vol. 71(C).
    4. Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie, 2021. "Data snooping in equity premium prediction," International Journal of Forecasting, Elsevier, vol. 37(1), pages 72-94.
    5. Hubert Dichtl & Wolfgang Drobetz & Viktoria‐Sophie Wendt, 2021. "How to build a factor portfolio: Does the allocation strategy matter?," European Financial Management, European Financial Management Association, vol. 27(1), pages 20-58, January.
    6. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
    7. Chiang, Mi-Hsiu & Chiu, Hsin-Yu & Kuo, Wei-Yu, 2021. "Predictive ability of similarity-based futures trading strategies," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    8. Minyou Fan & Youwei Li & Ming Liao & Jiadong Liu, 2022. "A reexamination of factor momentum: How strong is it?," The Financial Review, Eastern Finance Association, vol. 57(3), pages 585-615, August.
    9. Hsu, Po-Hsuan & Han, Qiheng & Wu, Wensheng & Cao, Zhiguang, 2018. "Asset allocation strategies, data snooping, and the 1 / N rule," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 257-269.
    10. Vincent, Kendro & Hsu, Yu-Chin & Lin, Hsiou-Wei, 2021. "Investment styles and the multiple testing of cross-sectional stock return predictability," Journal of Financial Markets, Elsevier, vol. 56(C).
    11. Yang, Junmin & Cao, Zhiguang & Han, Qiheng & Wang, Qiyu, 2019. "Tactical asset allocation on technical trading rules and data snooping," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).

  21. Yu-Chin Hsu, 2013. "Consistent Tests for Conditional Treatment Effects," IEAS Working Paper : academic research 13-A003, Institute of Economics, Academia Sinica, Taipei, Taiwan, revised Sep 2015.

    Cited by:

    1. Yu-Chin Hsu & Martin Huber & Ying-Ying Lee & Chu-An Liu, 2021. "Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data," Papers 2106.04237, arXiv.org, revised Aug 2022.
    2. Pedro H. C. Sant’Anna, 2021. "Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 816-832, July.
    3. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
    4. Hsu Yu-Chin & Huber Martin & Lai Tsung-Chih, 2019. "Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-20, January.
    5. Hsu, Yu-Chin & Shen, Shu, 2019. "Testing treatment effect heterogeneity in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 208(2), pages 468-486.
    6. 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.
    7. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    8. Yu‐Chin Hsu & Shu Shen, 2021. "Testing monotonicity of conditional treatment effects under regression discontinuity designs," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 346-366, April.
    9. Shi, Chengchun & Lu, Wenbin & Song, Rui, 2019. "A sparse random projection-based test for overall qualitative treatment effects," LSE Research Online Documents on Economics 102107, London School of Economics and Political Science, LSE Library.
    10. Sungwon Lee, 2024. "Partial identification and inference for conditional distributions of treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 107-127, January.

  22. Stephen G. Donald & Yu-Chin Hsu, 2012. "Estimation and Inference for Distribution Functions and Quantile Functions in Treatment Effect Models," IEAS Working Paper : academic research 12-A016, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2021. "Estimating Partially Conditional Quantile Treatment Effects," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202103, University of Kansas, Department of Economics, revised Jan 2021.
    2. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    3. Cécile Couharde & Rémi Generoso, 2024. "Assessing the Impact of National Air Quality Standards on Agricultural Land Values: Insights from Corn and Soybean Regions," EconomiX Working Papers 2024-9, University of Paris Nanterre, EconomiX.
    4. Ferreira,Francisco H. G. & Firpo,Sergio & Galvao,Antonio F., 2017. "Estimation and inference for actual and counterfactual growth incidence curves," Policy Research Working Paper Series 7933, The World Bank.
    5. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2019. "A Unified Framework for Efficient Estimation of General Treatment Models," CeMMAP working papers CWP64/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2019. "Calibration estimation of semiparametric copula models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 85-109.
    7. Chernozhukov, Victor & Fernández-Val, Iván & Melly, Blaise & Wüthrich, Kaspar, 2020. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," University of California at San Diego, Economics Working Paper Series qt5zm6m9rq, Department of Economics, UC San Diego.
    8. Yu-Chin Hsu & Martin Huber & Yu-Min Yen, 2023. "Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning," Papers 2307.01049, arXiv.org.
    9. Pedro H. C. Sant’Anna, 2021. "Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 816-832, July.
    10. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    11. Ai, Chunrong & Linton, Oliver & Zhang, Zheng, 2022. "Estimation and inference for the counterfactual distribution and quantile functions in continuous treatment models," Journal of Econometrics, Elsevier, vol. 228(1), pages 39-61.
    12. Brantly Callaway & Tong Li, 2019. "Quantile treatment effects in difference in differences models with panel data," Quantitative Economics, Econometric Society, vol. 10(4), pages 1579-1618, November.
    13. Ying-Ying Lee, 2015. "Efficient propensity score regression estimators of multi-valued treatment effects for the treated," Economics Series Working Papers 738, University of Oxford, Department of Economics.
    14. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    15. 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.
    16. Pedro H. C. Sant'Anna & Xiaojun Song, 2016. "Specification Tests for the Propensity Score," Papers 1611.06217, arXiv.org, revised Feb 2019.
    17. 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.
    18. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2021. "A Nonparametric Test for Testing Heterogeneity in Conditional Quantile Treatment Effects," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202117, University of Kansas, Department of Economics, revised Aug 2021.
    19. Hsu Yu-Chin & Huber Martin & Lai Tsung-Chih, 2019. "Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-20, January.
    20. Sungwon Lee, 2020. "Identification and Confidence Regions for Treatment Effect and its Distribution under Stochastic Dominance," Working Papers 2011, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    21. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.
    22. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods," Papers 1803.09015, arXiv.org, revised Dec 2020.
    23. García, A., 2016. "Oaxaca-Blinder Type Counterfactual Decomposition Methods for Duration Outcomes," Documentos de Trabajo 14186, Universidad del Rosario.
    24. Tenglong Li & Jordan Lawson, 2021. "A generalized bootstrap procedure of the standard error and confidence interval estimation for inverse probability of treatment weighting," Papers 2109.00171, arXiv.org.
    25. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    26. Caselli, Francesca & Wingender, Philippe, 2021. "Heterogeneous effects of fiscal rules: The Maastricht fiscal criterion and the counterfactual distribution of government deficits✰," European Economic Review, Elsevier, vol. 136(C).
    27. Shengfang Tang & Zongwu Cai & Ying Fang & Ming Lin, 2020. "A New Quantile Treatment Effect Model for Studying Smoking Effect on Birth Weight During Mother's Pregnancy," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202003, University of Kansas, Department of Economics, revised Feb 2020.
    28. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    29. Huang, W. & Linton, O. & Zhang, Z., 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Cambridge Working Papers in Economics 2113, Faculty of Economics, University of Cambridge.
    30. Francesca Caselli & Mr. Philippe Wingender, 2018. "Bunching at 3 Percent: The Maastricht Fiscal Criterion and Government Deficits," IMF Working Papers 2018/182, International Monetary Fund.
    31. Yingjie Feng, 2020. "Causal Inference in Possibly Nonlinear Factor Models," Papers 2008.13651, arXiv.org, revised Oct 2021.
    32. Pedro H. C. Sant'Anna, 2016. "Program Evaluation with Right-Censored Data," Papers 1604.02642, arXiv.org.
    33. Brantly Callaway & Weige Huang, 2020. "Distributional Effects of a Continuous Treatment with an Application on Intergenerational Mobility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 808-842, August.
    34. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2020. "Inferences for Partially Conditional Quantile Treatment Effect Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202005, University of Kansas, Department of Economics, revised Feb 2020.
    35. Sungwon Lee, 2024. "Partial identification and inference for conditional distributions of treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 107-127, January.
    36. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

  23. Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2012. "Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT," IEAS Working Paper : academic research 12-A017, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Yu-Chin Hsu & Martin Huber & Ying-Ying Lee & Chu-An Liu, 2021. "Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data," Papers 2106.04237, arXiv.org, revised Aug 2022.
    2. Bedoya Arguelles,Guadalupe & Bittarello,Luca & Davis,Jonathan Martin Villars & Mittag,Nikolas Karl & Bedoya Arguelles,Guadalupe & Bittarello,Luca & Davis,Jonathan Martin Villars & Mittag,Nikolas Karl, 2017. "Distributional impact analysis: toolkit and illustrations of impacts beyond the average treatment effect," Policy Research Working Paper Series 8139, The World Bank.
    3. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    4. Black, Dan A. & Joo, Joonhwi & LaLonde, Robert & Smith, Jeffrey A. & Taylor, Evan J., 2022. "Simple Tests for Selection: Learning More from Instrumental Variables," Labour Economics, Elsevier, vol. 79(C).
    5. Gerry H. Makepeace & Michael J. Peel, 2013. "Combining information from Heckman and matching estimators: testing and controlling for hidden bias," Economics Bulletin, AccessEcon, vol. 33(3), pages 2422-2436.
    6. Marianna Endresz & Peter Harasztosi & Robert P. Lieli, 2015. "The Impact of the Magyar Nemzeti Bank's Funding for Growth Scheme on Firm Level Investment," MNB Working Papers 2015/2, Magyar Nemzeti Bank (Central Bank of Hungary).
    7. Sloczynski, Tymon, 2021. "When Should We (Not) Interpret Linear IV Estimands as LATE?," IZA Discussion Papers 14349, Institute of Labor Economics (IZA).
    8. Huber, Martin, 2013. "A simple test for the ignorability of non-compliance in experiments," Economics Working Paper Series 1312, University of St. Gallen, School of Economics and Political Science.
    9. Donald, Stephen G. & Hsu, Yu-Chin, 2014. "Estimation and inference for distribution functions and quantile functions in treatment effect models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 383-397.
    10. Slichter, David, 2020. "Smile: A Simple Diagnostic for Selection on Observables," MPRA Paper 99921, University Library of Munich, Germany.
    11. Zeqin Liu & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Statistical Analysis and Evaluation of Macroeconomic Policies: A Selective Review," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201904, University of Kansas, Department of Economics, revised Mar 2019.
    12. 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.
    13. Tymon Sloczynski & S. Derya Uysal & Jeffrey M. Wooldridge & Derya Uysal, 2022. "Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment," CESifo Working Paper Series 10105, CESifo.
    14. Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2012. "Estimating Conditional Average Treatment Effects," CEU Working Papers 2012_16, Department of Economics, Central European University, revised 20 Jul 2012.
    15. Hsu Yu-Chin & Huber Martin & Lai Tsung-Chih, 2019. "Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-20, January.
    16. Tymon S{l}oczy'nski, 2020. "When Should We (Not) Interpret Linear IV Estimands as LATE?," Papers 2011.06695, arXiv.org, revised Sep 2022.
    17. Fang, Ying & Tang, Shengfang & Cai, Zongwu & Lin, Ming, 2020. "An alternative test for conditional unconfoundedness using auxiliary variables," Economics Letters, Elsevier, vol. 194(C).
    18. Khalil, Umair & Yıldız, Neşe, 2022. "A test of the selection on observables assumption using a discontinuously distributed covariate," Journal of Econometrics, Elsevier, vol. 226(2), pages 423-450.
    19. Huber, Martin & Wüthrich, Kaspar, 2017. "Evaluating local average and quantile treatment effects under endogeneity based on instruments: a review," FSES Working Papers 479, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    20. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    21. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.
    22. Tymon S{l}oczy'nski & S. Derya Uysal & Jeffrey M. Wooldridge, 2022. "Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment," Papers 2208.01300, arXiv.org, revised Nov 2022.
    23. de Luna, Xavier & Johansson, Per, 2012. "Testing for nonparametric identification of causal effects in the presence of a quasi-instrument," Working Paper Series 2012:14, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    24. Huang, W. & Linton, O. & Zhang, Z., 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Cambridge Working Papers in Economics 2113, Faculty of Economics, University of Cambridge.
    25. Donald, Stephen G. & Hsu, Yu-Chin & Lieli, Robert P., 2014. "Inverse probability weighted estimation of local average treatment effects: A higher order MSE expansion," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 132-138.
    26. Martin Huber & Jannis Kueck, 2022. "Testing the identification of causal effects in observational data," Papers 2203.15890, arXiv.org, revised Jun 2023.

  24. Stephen G. Donald & Yu-Chin Hsu, 2012. "Improving the Power of Tests of Stochastic Dominance," IEAS Working Paper : academic research 12-A015, Institute of Economics, Academia Sinica, Taipei, Taiwan, revised Jun 2013.

    Cited by:

    1. Yu-Chin Hsu & Martin Huber & Ying-Ying Lee & Chu-An Liu, 2021. "Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data," Papers 2106.04237, arXiv.org, revised Aug 2022.
    2. Chuang, O-Chia & Kuan, Chung-Ming & Tzeng, Larry Y., 2017. "Testing for central dominance: Method and application," Journal of Econometrics, Elsevier, vol. 196(2), pages 368-378.
    3. Lok, Thomas M. & Tabri, Rami V., 2021. "An improved bootstrap test for restricted stochastic dominance," Journal of Econometrics, Elsevier, vol. 224(2), pages 371-393.
    4. Toru Kitagawa & Aleksey Tetenov, 2017. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers CWP24/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Chang, Chia-Lin & Jimenez-Martin, Juan-Angel & Maasoumi, Esfandiar & McAleer, Michael & Pérez-Amaral, Teodosio, 2019. "Choosing expected shortfall over VaR in Basel III using stochastic dominance," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 95-113.
    6. Chia-Lin Chang & Juan-Ángel Jiménez-Martín & Esfandiar Maasoumi & Michael McAleer & Teodosio Pérez-Amaral, 2015. "A Stochastic Dominance Approach to the Basel III Dilemma: Expected Shortfall or VaR?," Documentos de Trabajo del ICAE 2015-16, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    7. Yoichi Arai & Yu-Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2021. "Testing identifying assumptions in fuzzy regression discontinuity designs," CeMMAP working papers CWP16/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Hongyi Jiang & Zhenting Sun & Shiyun Hu, 2023. "A Nonparametric Test of $m$th-degree Inverse Stochastic Dominance," Papers 2306.12271, arXiv.org, revised Jul 2023.
    9. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    10. David M. Kaplan & Matt Goldman, 2018. "Comparing distributions by multiple testing across quantiles or CDF values," Working Papers 1801, Department of Economics, University of Missouri.
    11. Mariusz Górajski & Zbigniew Kuchta, 2022. "Which hallmarks of optimal monetary policy rules matter in Poland? A stochastic dominance approach," Bank i Kredyt, Narodowy Bank Polski, vol. 53(2), pages 149-182.
    12. Chia-Lin Chang & Juan-Ángel Jiménez-Martín & Esfandiar Maasoumi & Teodosio Pérez Amaral, 2014. "A Stochastic Dominance Approach to Financial Risk Management Strategies," Documentos de Trabajo del ICAE 2014-08, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, revised Apr 2014.
    13. Chung, D. & Linton, O. & Whang Y-J., 2021. "Consistent Testing for an Implication of Supermodular Dominance," Cambridge Working Papers in Economics 2134, Faculty of Economics, University of Cambridge.
    14. Garry F. Barrett & Stephen G. Donald & Yu-Chin Hsu, 2015. "Consistent Tests for Poverty Dominance Relations," IEAS Working Paper : academic research 15-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    15. David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.
    16. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2020. "Bayesian assessment of Lorenz and stochastic dominance," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(2), pages 767-799, May.
    17. beare, brendan & shi, xiaoxia, 2015. "An improved bootstrap test of density ratio ordering," MPRA Paper 74772, University Library of Munich, Germany.
    18. Brendan K. Beare & Jackson D. Clarke, 2022. "Modified Wilcoxon-Mann-Whitney tests of stochastic dominance," Papers 2210.08892, arXiv.org.
    19. Hsu, Yu-Chin & Shen, Shu, 2019. "Testing treatment effect heterogeneity in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 208(2), pages 468-486.
    20. David Lander & David Gunawan & William E. Griffiths & Duangkamon Chotikapanich, 2016. "Bayesian Assessment of Lorenz and Stochastic Dominance Using a Mixture of Gamma Densities," Department of Economics - Working Papers Series 2023, The University of Melbourne.
    21. Toru Kitagawa & Aleksey Tetenov, 2017. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers 24/17, Institute for Fiscal Studies.
    22. Yu‐Chin Hsu & Shu Shen, 2021. "Testing monotonicity of conditional treatment effects under regression discontinuity designs," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 346-366, April.
    23. Arvanitis, Stelios & Topaloglou, Nikolas, 2017. "Testing for prospect and Markowitz stochastic dominance efficiency," Journal of Econometrics, Elsevier, vol. 198(2), pages 253-270.

  25. Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2012. "Estimating Conditional Average Treatment Effects," CEU Working Papers 2012_16, Department of Economics, Central European University, revised 20 Jul 2012.

    Cited by:

    1. Sokbae Lee & Ryo Okui & Yoon†Jae Whang, 2017. "Doubly robust uniform confidence band for the conditional average treatment effect function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1207-1225, November.
    2. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
    3. Sakos, Grayson & Cerulli, Giovanni & Garbero, Alessandra, 2021. "Beyond the ATE: Idiosyncratic Effect Estimation to Uncover Distributional Impacts Results from 17 Impact Evaluations," 2021 Annual Meeting, August 1-3, Austin, Texas 314017, Agricultural and Applied Economics Association.
    4. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2021. "Estimating Partially Conditional Quantile Treatment Effects," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202103, University of Kansas, Department of Economics, revised Jan 2021.
    5. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
    6. Victor Chernozhukov & Whitney K. Newey & Rahul Singh, 2021. "A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees," Papers 2105.15197, arXiv.org, revised Oct 2022.
    7. Feng, Sanying & Kong, Kaidi & Kong, Yinfei & Li, Gaorong & Wang, Zhaoliang, 2022. "Statistical inference of heterogeneous treatment effect based on single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    8. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    9. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Papers 1810.13237, arXiv.org, revised Dec 2018.
    10. Alessandro Barbera & Áron Gereben & Marcin Wolski, 2022. "Estimating conditional treatment effects of EIB lending to SMEs in Europe," BIS Working Papers 1006, Bank for International Settlements.
    11. Niwen Zhou & Xu Guo & Lixing Zhu, 2022. "The role of propensity score structure in asymptotic efficiency of estimated conditional quantile treatment effect," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 718-743, June.
    12. Daniel Kaliski, 2023. "Identifying the impact of health insurance on subgroups with changing rates of diagnosis," Health Economics, John Wiley & Sons, Ltd., vol. 32(9), pages 2098-2112, September.
    13. Arthur Charpentier & Emmanuel Flachaire & Ewen Gallic, 2023. "Optimal Transport for Counterfactual Estimation: A Method for Causal Inference," Papers 2301.07755, arXiv.org.
    14. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
    15. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
    16. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2021. "A Nonparametric Test for Testing Heterogeneity in Conditional Quantile Treatment Effects," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202117, University of Kansas, Department of Economics, revised Aug 2021.
    17. Hsu, Yu-Chin & Huber, Martin & Lee, Ying-Ying & Pipoz, Layal, 2018. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," FSES Working Papers 495, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    18. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Apr 2024.
    19. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    20. ASAKAWA Shinsuke & OHTAKE Fumio, 2022. "Impact of COVID-19 School Closures on the Cognitive and Non-cognitive Skills of Elementary School Students," Discussion papers 22075, Research Institute of Economy, Trade and Industry (RIETI).
    21. Wichman, Casey J., 2016. "Information Provision and Consumer Behavior: A Natural Experiment in Billing Frequency," RFF Working Paper Series dp-15-35-rev, Resources for the Future.
    22. Qingliang Fan & Yu-Chin Hsu & Robert P. Lieli & Yichong Zhang, 2022. "Estimation of Conditional Average Treatment Effects With High-Dimensional Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 313-327, January.
    23. Seungyeon Cho, 2022. "The Effect of Participation in the Supplemental Nutrition Assistance Program on Food Insecurity of Children in U.S. Immigrant Households," Journal of Family and Economic Issues, Springer, vol. 43(3), pages 501-510, September.
    24. Miller, Steve, 2020. "Causal forest estimation of heterogeneous and time-varying environmental policy effects," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    25. Robson, M.; & Doran, T.; & Cookson, R.;, 2019. "Estimating and Decomposing Conditional Average Treatment Effects: The Smoking Ban in England," Health, Econometrics and Data Group (HEDG) Working Papers 19/20, HEDG, c/o Department of Economics, University of York.
    26. Yixiao Jiang, 2021. "Semiparametric Estimation of a Corporate Bond Rating Model," Econometrics, MDPI, vol. 9(2), pages 1-20, May.
    27. Shengfang Tang & Zongwu Cai & Ying Fang & Ming Lin, 2020. "A New Quantile Treatment Effect Model for Studying Smoking Effect on Birth Weight During Mother's Pregnancy," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202003, University of Kansas, Department of Economics, revised Feb 2020.
    28. Pengzhou Wu & Kenji Fukumizu, 2021. "$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap," Papers 2110.05225, arXiv.org.
    29. 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.
    30. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    31. Sebastian Calonico & Rafael Di Tella & Juan Cruz Lopez Del Valle, 2022. "Causal Inference During a Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina," NBER Working Papers 30084, National Bureau of Economic Research, Inc.
    32. Zimmert, Franziska & Zimmert, Michael, 2020. "Paid parental leave and maternal reemployment: Do part-time subsidies help or harm?," Economics Working Paper Series 2002, University of St. Gallen, School of Economics and Political Science.
    33. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    34. Gabriel Okasa & Kenneth A. Younge, 2022. "Sample Fit Reliability," Papers 2209.06631, arXiv.org.
    35. Huang, W. & Linton, O. & Zhang, Z., 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Cambridge Working Papers in Economics 2113, Faculty of Economics, University of Cambridge.
    36. 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.
    37. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.
    38. Isaac Meza & Rahul Singh, 2021. "Nested Nonparametric Instrumental Variable Regression: Long Term, Mediated, and Time Varying Treatment Effects," Papers 2112.14249, arXiv.org, revised Mar 2024.
    39. Zongwu Cai & Ying Fang & Ming Lin & Shengfang Tang, 2020. "Inferences for Partially Conditional Quantile Treatment Effect Model," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202005, University of Kansas, Department of Economics, revised Feb 2020.
    40. Lu Li & Niwen Zhou & Lixing Zhu, 2022. "Outcome regression-based estimation of conditional average treatment effect," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 987-1041, October.
    41. Sungwon Lee, 2024. "Partial identification and inference for conditional distributions of treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 107-127, January.

  26. Joseph Haslag & Yu-Chin Hsu, 2012. "Cyclical Co-movement between Output, the Price Level, and Inflation," Working Papers 1203, Department of Economics, University of Missouri.

    Cited by:

    1. Joseph Haslag & William Brock, 2014. "On Understanding the Cyclical Behavior of the Price Level and Inflation," Working Papers 1404, Department of Economics, University of Missouri, revised 01 Jul 2014.
    2. Guglielmo Maria Caporale & Gloria Claudio-Quiroga & Luis Alberiko Gil-Alana, 2022. "The relationship between prices and output in the UK and the US," SN Business & Economics, Springer, vol. 2(6), pages 1-13, June.
    3. Brock, William A. & Haslag, Joseph H., 2016. "A tale of two correlations: Evidence and theory regarding the phase shift between the price level and output," Journal of Economic Dynamics and Control, Elsevier, vol. 67(C), pages 40-57.
    4. Joseph H. Haslag & Xue Li, 2017. "On Phase Shifts in a New Keynesian Model Economy," Working Papers 1703, Department of Economics, University of Missouri.
    5. Michal Andrle & Jan Bruha & Mr. Serhat Solmaz, 2013. "Inflation and Output Comovement in the Euro Area: Love at Second Sight?," IMF Working Papers 2013/192, International Monetary Fund.
    6. Antonakakis, Nikolaos & Gupta, Rangan & Tiwari, Aviral K., 2017. "The time-varying correlation between output and prices in the United States over the period 1800–2014," Economic Systems, Elsevier, vol. 41(1), pages 98-108.

  27. Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2010. "Inverse Propensity Score Weighted Estimation of Local Average Treatment Effects and a Test of the Unconfoundedness Assumption," CEU Working Papers 2012_9, Department of Economics, Central European University, revised 11 Aug 2010.

    Cited by:

    1. Frölich, Markus & Melly, Blaise, 2008. "Identification of Treatment Effects on the Treated with One-Sided Non-Compliance," IZA Discussion Papers 3671, Institute of Labor Economics (IZA).

  28. Yu-Chin Hsu & Chung-Ming Kuan, 2006. "Change-Point Estimation of Nonstationary I(d) Processes," IEAS Working Paper : academic research 06-A007, Institute of Economics, Academia Sinica, Taipei, Taiwan.

    Cited by:

    1. Chang, Seong Yeon, 2021. "Estimation of a level shift in panel data with fractionally integrated errors," Economics Letters, Elsevier, vol. 206(C).
    2. Zongwu Cai & Seong Yeon Chang, 2018. "A New Test In A Predictive Regression with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201811, University of Kansas, Department of Economics, revised Dec 2018.
    3. Seong Yeon Chang & Pierre Perron, 2014. "Inference on a Structural Break in Trend with Fractionally Integrated Errors," Boston University - Department of Economics - Working Papers Series wp2015-011, Boston University - Department of Economics, revised 20 Sep 2015.
    4. Giorgio Canarella & Stephen M. Miller, 2016. "Inflation Persistence and Structural Breaks: The Experience of Inflation Targeting Countries and the US," Working papers 2016-11, University of Connecticut, Department of Economics.
    5. Badi H. Baltagi & Chihwa Kao & Long Liu, 2017. "Estimation and identification of change points in panel models with nonstationary or stationary regressors and error term," Econometric Reviews, Taylor & Francis Journals, vol. 36(1-3), pages 85-102, March.
    6. Daiqing Xi & Tianxiao Pang, 2021. "Estimating multiple breaks in mean sequentially with fractionally integrated errors," Statistical Papers, Springer, vol. 62(1), pages 451-494, February.

Articles

  1. Qingliang Fan & Yu-Chin Hsu & Robert P. Lieli & Yichong Zhang, 2022. "Estimation of Conditional Average Treatment Effects With High-Dimensional Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 313-327, January.
    See citations under working paper version above.
  2. Yu-Chin Hsu & Tsung-Chih Lai & Robert P. Lieli, 2022. "Counterfactual Treatment Effects: Estimation and Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 240-255, January.

    Cited by:

    1. Tsung-Chih Lai & Jiun-Hua Su, 2023. "Counterfactual Copula and Its Application to the Effects of College Education on Intergenerational Mobility," Papers 2303.06658, arXiv.org.
    2. Arthur Charpentier & Emmanuel Flachaire & Ewen Gallic, 2023. "Optimal Transport for Counterfactual Estimation: A Method for Causal Inference," Papers 2301.07755, arXiv.org.
    3. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.

  3. 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.
    See citations under working paper version above.
  4. Yu‐Chin Hsu & Shu Shen, 2021. "Testing monotonicity of conditional treatment effects under regression discontinuity designs," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 346-366, April.

    Cited by:

    1. David Wuepper & Robert Finger, 2023. "Regression discontinuity designs in agricultural and environmental economics," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(1), pages 1-28.

  5. Yu-Chin Hsu & Kamhon Kan & Tsung-Chih Lai, 2021. "Quantile structural treatment effects: application to smoking wage penalty and its determinants," Econometric Reviews, Taylor & Francis Journals, vol. 40(2), pages 128-147, February.

    Cited by:

    1. Myoung‐jae Lee & Jin‐young Choi, 2022. "Finding mover–stayer quantile difference due to unobservables using quantile selection corrections," Bulletin of Economic Research, Wiley Blackwell, vol. 74(3), pages 704-721, July.

  6. Jason Abrevaya & Yu-Chin Hsu, 2021. "Partial effects in non-linear panel data models with correlated random effects," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 519-535.

    Cited by:

    1. Aguirregabiria, Victor & Carro, Jesus, 2021. "Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models," CEPR Discussion Papers 16354, C.E.P.R. Discussion Papers.
    2. Victor Aguirregabiria, 2022. "Dynamic demand for differentiated products with fixed-effects unobserved heterogeneity," Working Papers tecipa-723, University of Toronto, Department of Economics.
    3. Cavit Pakel & Martin Weidner, 2023. "Bounds on Average Effects in Discrete Choice Panel Data Models," Papers 2309.09299, arXiv.org, revised Dec 2023.
    4. 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 Dec 2023.

  7. Hsu, Yu-Chin & Shiu, Ji-Liang, 2021. "Nonlinear Panel Data Models With Distribution-Free Correlated Random Effects," Econometric Theory, Cambridge University Press, vol. 37(6), pages 1075-1099, December.

    Cited by:

    1. Jie Wei & Yonghui Zhang, 2022. "Panel Probit Models with Time‐Varying Individual Effects: Reestimating the Effects of Fertility on Female Labour Participation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 799-829, August.
    2. Cäcilia Lipowski & Ralf A. Wilke & Bertrand Koebel, 2022. "Fertility, economic incentives and individual heterogeneity: Register data‐based evidence from France and Germany," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 515-546, December.
    3. Zubanov, Nick & Shakina, Elena, 2023. "Performance Costs and Benefits of Collective Turnover: A Theory-Driven Measurement Framework and Applications," IZA Discussion Papers 16413, Institute of Labor Economics (IZA).

  8. Vincent, Kendro & Hsu, Yu-Chin & Lin, Hsiou-Wei, 2021. "Investment styles and the multiple testing of cross-sectional stock return predictability," Journal of Financial Markets, Elsevier, vol. 56(C).

    Cited by:

    1. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).

  9. Martin Huber & Yu‐Chin Hsu & Ying‐Ying Lee & Layal Lettry, 2020. "Direct and indirect effects of continuous treatments based on generalized propensity score weighting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 814-840, November.
    See citations under working paper version above.
  10. Yi-Ting Chen & Yu-Chin Hsu & Hung-Jen Wang, 2020. "A Stochastic Frontier Model with Endogenous Treatment Status and Mediator," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 243-256, April.

    Cited by:

    1. Samuele Centorrino & Maria P'erez-Urdiales & Boris Bravo-Ureta & Alan J. Wall, 2023. "Binary Endogenous Treatment in Stochastic Frontier Models with an Application to Soil Conservation in El Salvador," Papers 2312.13939, arXiv.org.
    2. Salm, Martin & Siflinger, Bettina & Xie, Mingjia, 2021. "The Effect of Retirement on Mental Health: Indirect Treatment Effects and Causal Mediation," Other publications TiSEM e28efa7f-8219-437c-a26d-2, Tilburg University, School of Economics and Management.
    3. Parmeter, Christopher F. & Simar, Léopold & Van Keilegom, Ingrid & Zelenyuk, Valentin, 2021. "Inference in the Nonparametric Stochastic Frontier Model," LIDAM Discussion Papers ISBA 2021029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Yitayew, Asresu & Abdulai, Awudu & Yigezu, Yigezu A., 2023. "The effects of advisory services and technology channeling on farm yields and technical efficiency of wheat farmers in Ethiopia," Food Policy, Elsevier, vol. 116(C).
    5. Le-Yu Chen & Yu-Min Yen, 2021. "Estimations of the Conditional Tail Average Treatment Effect," Papers 2109.08793, arXiv.org, revised Sep 2021.
    6. Mohammed, Sadick & Abdulai, Awudu, 2021. "Extension Participation and Improved Technology Adoption: Impact on Efficiency and Welfare of Farmers in Ghana," 2021 Conference, August 17-31, 2021, Virtual 315362, International Association of Agricultural Economists.
    7. Huang, W. & Linton, O. & Zhang, Z., 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Cambridge Working Papers in Economics 2113, Faculty of Economics, University of Cambridge.

  11. Hsu, Yu-Chin & Liu, Chu-An & Shi, Xiaoxia, 2019. "Testing Generalized Regression Monotonicity," Econometric Theory, Cambridge University Press, vol. 35(6), pages 1146-1200, December.
    See citations under working paper version above.
  12. Robert P. Lieli & Yu-Chin Hsu, 2019. "Using the area under an estimated ROC curve to test the adequacy of binary predictors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 31(1), pages 100-130, January.
    See citations under working paper version above.
  13. Hsu, Yu-Chin & Shen, Shu, 2019. "Testing treatment effect heterogeneity in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 208(2), pages 468-486.

    Cited by:

    1. Matias D. Cattaneo & Rocio Titiunik & Gonzalo Vazquez-Bare, 2019. "The Regression Discontinuity Design," Papers 1906.04242, arXiv.org, revised Jun 2020.
    2. Yoichi Arai & Yu-Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2021. "Testing identifying assumptions in fuzzy regression discontinuity designs," CeMMAP working papers CWP16/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Matilde Cappelletti & Leonardo M. Giuffrida & Gabriele Rovigatti & Leonardo Maria Giuffrida, 2022. "Procuring Survival," CESifo Working Paper Series 10124, CESifo.
    4. Chen, Wei-Lin & Lin, Ming-Jen & Yang, Tzu-Ting, 2023. "Curriculum and national identity: Evidence from the 1997 curriculum reform in Taiwan," Journal of Development Economics, Elsevier, vol. 163(C).
    5. Pedro Forquesato, 2022. "Who Benefits from Political Connections in Brazilian Municipalities," Papers 2204.09450, arXiv.org.
    6. David Wuepper & Robert Finger, 2023. "Regression discontinuity designs in agricultural and environmental economics," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(1), pages 1-28.
    7. Likai Chen & Georg Keilbar & Liangjun Su & Weining Wang, 2023. "Tests for Many Treatment Effects in Regression Discontinuity Panel Data Models," Papers 2312.01162, arXiv.org.
    8. 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.
    9. Bansak, Kirk & Nowacki, Tobias, 2022. "Effect Heterogeneity and Causal Attribution in Regression Discontinuity Designs," SocArXiv vj34m, Center for Open Science.
    10. Matias D. Cattaneo & Luke Keele & Rocio Titiunik, 2021. "Covariate Adjustment in Regression Discontinuity Designs," Papers 2110.08410, arXiv.org, revised Aug 2022.
    11. Yu‐Chin Hsu & Shu Shen, 2021. "Testing monotonicity of conditional treatment effects under regression discontinuity designs," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 346-366, April.

  14. Hsu Yu-Chin & Huber Martin & Lai Tsung-Chih, 2019. "Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-20, January.
    See citations under working paper version above.
  15. Chiang, Harold D. & Hsu, Yu-Chin & Sasaki, Yuya, 2019. "Robust uniform inference for quantile treatment effects in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 211(2), pages 589-618.

    Cited by:

    1. Matias D. Cattaneo & Rocio Titiunik & Gonzalo Vazquez-Bare, 2019. "The Regression Discontinuity Design," Papers 1906.04242, arXiv.org, revised Jun 2020.
    2. Yoichi Arai & Yu-Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2021. "Testing identifying assumptions in fuzzy regression discontinuity designs," CeMMAP working papers CWP16/21, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Yang He & Otávio Bartalotti, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals [Using Maimonides’ rule to estimate the effect of class size on scholastic achievemen," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 211-231.
    4. Mauricio Villamizar‐Villegas & Freddy A. Pinzon‐Puerto & Maria Alejandra Ruiz‐Sanchez, 2022. "A comprehensive history of regression discontinuity designs: An empirical survey of the last 60 years," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 1130-1178, September.
    5. Jun Ma & Zhengfei Yu, 2020. "Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs," Papers 2008.09263, arXiv.org, revised May 2022.
    6. Yingying DONG & Ying-Ying LEE & Michael GOU, 2019. "Regression Discontinuity Designs with a Continuous Treatment," Discussion papers 19058, Research Institute of Economy, Trade and Industry (RIETI).
    7. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.

  16. Yu‐Chin Hsu, 2017. "Consistent tests for conditional treatment effects," Econometrics Journal, Royal Economic Society, vol. 20(1), pages 1-22, February.
    See citations under working paper version above.
  17. Yu‐Chin Hsu & Xiaoxia Shi, 2017. "Model‐selection tests for conditional moment restriction models," Econometrics Journal, Royal Economic Society, vol. 20(1), pages 52-85, February.

    Cited by:

    1. Liu, Tuo & Lee, Lung-fei, 2019. "A likelihood ratio test for spatial model selection," Journal of Econometrics, Elsevier, vol. 213(2), pages 434-458.
    2. Gonzalo, Jesús & Pitarakis, Jean-Yves, 2020. "Out of sample predictability in predictive regressions with many predictor candidates," UC3M Working papers. Economics 31554, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Qingliang Fan & Zijian Guo & Ziwei Mei, 2022. "A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates," Papers 2205.00171, arXiv.org, revised Mar 2023.
    4. Brück, Florian & Fermanian, Jean-David & Min, Aleksey, 2023. "A corrected Clarke test for model selection and beyond," Journal of Econometrics, Elsevier, vol. 235(1), pages 105-132.
    5. Tadao Hoshino & Takahide Yanagi, 2018. "Treatment Effect Models with Strategic Interaction in Treatment Decisions," Papers 1810.08350, arXiv.org, revised Feb 2023.
    6. Francesco Bravo, 2022. "Misspecified semiparametric model selection with weakly dependent observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 558-586, July.
    7. Rami V. Tabri & Christopher D. Walker, 2020. "Inference for Moment Inequalities: A Constrained Moment Selection Procedure," Papers 2008.09021, arXiv.org, revised Aug 2020.
    8. Ye Yang & Osman Dogan & Suleyman Taspinar & Fei Jin, 2023. "A Review of Cross-Sectional Matrix Exponential Spatial Models," Papers 2311.14813, arXiv.org.

  18. Stephen G. Donald & Yu-Chin Hsu, 2016. "Improving the Power of Tests of Stochastic Dominance," Econometric Reviews, Taylor & Francis Journals, vol. 35(4), pages 553-585, April.
    See citations under working paper version above.
  19. Barrett, Garry F. & Donald, Stephen G. & Hsu, Yu-Chin, 2016. "Consistent tests for poverty dominance relations," Journal of Econometrics, Elsevier, vol. 191(2), pages 360-373.
    See citations under working paper version above.
  20. Wei‐Ming Lee & Yu‐Chin Hsu & Chung‐Ming Kuan, 2015. "Robust hypothesis tests for M‐estimators with possibly non‐differentiable estimating functions," Econometrics Journal, Royal Economic Society, vol. 18(1), pages 95-116, February.
    See citations under working paper version above.
  21. Jason Abrevaya & Yu-Chin Hsu & Robert P. Lieli, 2015. "Estimating Conditional Average Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 485-505, October.
    See citations under working paper version above.
  22. Lu, Tsung-Hsun & Chen, Yi-Chi & Hsu, Yu-Chin, 2015. "Trend definition or holding strategy: What determines the profitability of candlestick charting?," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 172-183.

    Cited by:

    1. Krzysztof Piasecki & Anna Łyczkowska-Hanćkowiak, 2019. "Representation of Japanese Candlesticks by Oriented Fuzzy Numbers," Econometrics, MDPI, vol. 8(1), pages 1-24, December.
    2. Huadong Chang & Guozhi An, 2019. "Will History Repeat Itself? Empirical Research on A-Share Candlesticks in China Based on Matching Method," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 9(5), pages 1-8.
    3. Tsung-Hsun Lu & Yung-Ming Shiu, 2016. "Can 1-day candlestick patterns be profitable on the 30 component stocks of the DJIA?," Applied Economics, Taylor & Francis Journals, vol. 48(35), pages 3345-3354, July.
    4. Piyapas Tharavanij & Vasan Siraprapasiri & Kittichai Rajchamaha, 2017. "Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand," SAGE Open, , vol. 7(4), pages 21582440177, October.
    5. Chen, Shi & Bao, Si & Zhou, Yu, 2016. "The predictive power of Japanese candlestick charting in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 148-165.
    6. Chiang, Mi-Hsiu & Chiu, Hsin-Yu & Kuo, Wei-Yu, 2021. "Predictive ability of similarity-based futures trading strategies," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    7. Shangkun Deng & Zhihao Su & Yanmei Ren & Haoran Yu & Yingke Zhu & Chenyang Wei, 2022. "Can Japanese Candlestick Patterns be Profitable on the Component Stocks of the SSE50 Index?," SAGE Open, , vol. 12(3), pages 21582440221, August.

  23. Donald, Stephen G. & Hsu, Yu-Chin & Lieli, Robert P., 2014. "Inverse probability weighted estimation of local average treatment effects: A higher order MSE expansion," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 132-138.
    See citations under working paper version above.
  24. Donald, Stephen G. & Hsu, Yu-Chin, 2014. "Estimation and inference for distribution functions and quantile functions in treatment effect models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 383-397.
    See citations under working paper version above.
  25. Lee, Wei-Ming & Kuan, Chung-Ming & Hsu, Yu-Chin, 2014. "Testing over-identifying restrictions without consistent estimation of the asymptotic covariance matrix," Journal of Econometrics, Elsevier, vol. 181(2), pages 181-193.
    See citations under working paper version above.
  26. Stephen G. Donald & Yu-Chin Hsu & Robert P. Lieli, 2014. "Testing the Unconfoundedness Assumption via Inverse Probability Weighted Estimators of (L)ATT," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 395-415, July.
    See citations under working paper version above.
  27. Yu-Chin Hsu & Chung-Ming Kuan & Meng-Feng Yen, 2014. "A Generalized Stepwise Procedure with Improved Power for Multiple Inequalities Testing," Journal of Financial Econometrics, Oxford University Press, vol. 12(4), pages 730-755.
    See citations under working paper version above.
  28. Stephen G. Donald & Yu‐Chin Hsu & Garry F. Barrett, 2012. "Incorporating covariates in the measurement of welfare and inequality: methods and applications," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 1-30, February.

    Cited by:

    1. Frank A. Cowell & Emmanuel Flachaire, 2015. "Statistical Methods for Distributional Analysis," Post-Print hal-01457398, HAL.
    2. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    3. Goldman, Matt & Kaplan, David M., 2017. "Fractional order statistic approximation for nonparametric conditional quantile inference," Journal of Econometrics, Elsevier, vol. 196(2), pages 331-346.
    4. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    5. Donald, Stephen G. & Hsu, Yu-Chin, 2014. "Estimation and inference for distribution functions and quantile functions in treatment effect models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 383-397.
    6. Chiang, Harold D. & Hsu, Yu-Chin & Sasaki, Yuya, 2019. "Robust uniform inference for quantile treatment effects in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 211(2), pages 589-618.
    7. Ying-Ying Lee, 2015. "Efficient propensity score regression estimators of multi-valued treatment effects for the treated," Economics Series Working Papers 738, University of Oxford, Department of Economics.
    8. Margherita Gerolimetto & Stefano Magrini, 2018. "Inference for inequality measures: a review," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 72(2), pages 75-85, April-Jun.
    9. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    10. Jing Dai & Stefan Sperlich & Walter Zucchini, 2016. "A Simple Method for Predicting Distributions by Means of Covariates with Examples from Poverty and Health Economics," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 152(I), pages 49-80, March.
    11. Ng, Pin & Wong, Wing-Keung & Xiao, Zhijie, 2017. "Stochastic dominance via quantile regression with applications to investigate arbitrage opportunity and market efficiency," European Journal of Operational Research, Elsevier, vol. 261(2), pages 666-678.
    12. Mario Fiorini & Katrien Stevens, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1475-1526, December.
    13. Christopher J. Bennett, 2013. "Inference For Dominance Relations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 54(4), pages 1309-1328, November.
    14. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    15. Philippe Van Kerm & Seunghee Yu & Chung Choe, 2016. "Decomposing quantile wage gaps: a conditional likelihood approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 507-527, August.
    16. Domma, Filippo & Condino, Francesca & Giordano, Sabrina, 2018. "A new formulation of the Dagum distribution in terms of income inequality and poverty measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 104-126.
    17. Brantly Callaway & Weige Huang, 2020. "Distributional Effects of a Continuous Treatment with an Application on Intergenerational Mobility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(4), pages 808-842, August.
    18. Yun Shi & Lin Yang & Mei Huang & Jun Steed Huang, 2021. "Multi-Factorized Semi-Covariance of Stock Markets and Gold Price," JRFM, MDPI, vol. 14(4), pages 1-11, April.
    19. Sungwon Lee, 2024. "Partial identification and inference for conditional distributions of treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 107-127, January.
    20. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

  29. Donald, Stephen G. & Hsu, Yu-Chin, 2011. "A new test for linear inequality constraints when the variance–covariance matrix depends on the unknown parameters," Economics Letters, Elsevier, vol. 113(3), pages 241-243.

    Cited by:

    1. Huber, Martin, 2012. "Statistical verification of a natural "natural experiment": Tests and sensitivity checks for the sibling sex ratio instrument," Economics Working Paper Series 1219, University of St. Gallen, School of Economics and Political Science.
    2. David M. Kaplan & Longhao Zhuo, 2015. "Bayesian and frequentist inequality tests," Working Papers 1516, Department of Economics, University of Missouri, revised Feb 2018.
    3. Zeng-Hua Lu & Alec Zuo, 2017. "Child disability, welfare payments, marital status and mothers’ labor supply: Evidence from Australia," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1339769-133, January.
    4. Martin Huber, 2015. "Testing the Validity of the Sibling Sex Ratio Instrument," LABOUR, CEIS, vol. 29(1), pages 1-14, March.
    5. Yu-Chin Hsu & Ji-Liang Shiu & Yuanyuan Wan, 2023. "Testing Identification Conditions of LATE in Fuzzy Regression Discontinuity Designs," Working Papers tecipa-761, University of Toronto, Department of Economics.

  30. Hsu, Po-Hsuan & Hsu, Yu-Chin & Kuan, Chung-Ming, 2010. "Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 471-484, June.

    Cited by:

    1. Costantini, Mauro & Cuaresma, Jesus Crespo & Hlouskova, Jaroslava, 2014. "Can Macroeconomists Get Rich Forecasting Exchange Rates?," Economics Series 305, Institute for Advanced Studies.
    2. Urquhart, Andrew & Zhang, Hanxiong, 2019. "The performance of technical trading rules in Socially Responsible Investments," International Review of Economics & Finance, Elsevier, vol. 63(C), pages 397-411.
    3. Christopher J. Neely & Paul A. Weller, 2011. "Technical analysis in the foreign exchange market," Working Papers 2011-001, Federal Reserve Bank of St. Louis.
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    72. Ren, Rui & Lu, Meng-Jou & Li, Yingxing & Härdle, Wolfgang, 2021. "Financial Risk Meter based on expectiles," IRTG 1792 Discussion Papers 2021-008, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    73. Marcel, Bräutigam & Marie, Kratz, 2018. "On the Dependence between Quantiles and Dispersion Estimators," ESSEC Working Papers WP1807, ESSEC Research Center, ESSEC Business School.
    74. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2018. "Tail expectile process and risk assessment," TSE Working Papers 18-944, Toulouse School of Economics (TSE).
    75. Daouia, Abdelaati & Paindaveine, Davy, 2019. "Multivariate Expectiles, Expectile Depth and Multiple-Output Expectile Regression," TSE Working Papers 19-1022, Toulouse School of Economics (TSE), revised Feb 2023.
    76. Shih-Kang Chao & Wolfgang Karl Härdle & Weining Wang, 2012. "Quantile Regression in Risk Calibration," SFB 649 Discussion Papers SFB649DP2012-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

  32. Hsu, Yu-Chin & Kuan, Chung-Ming, 2008. "Change-point estimation of nonstationary I(d) processes," Economics Letters, Elsevier, vol. 98(2), pages 115-121, February.
    See citations under working paper version above.

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