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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. 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. Takuya Ishihara & Masayuki Sawada, 2020. "Manipulation-Robust Regression Discontinuity Designs," Papers 2009.07551, arXiv.org, revised Sep 2024.
    2. 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.
    3. Santiago Acerenza & Otávio Bartalotti & Désiré Kédagni, 2023. "Testing identifying assumptions in bivariate probit models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 407-422, April.
    4. Matias D. Cattaneo & Rocio Titiunik, 2021. "Regression Discontinuity Designs," Papers 2108.09400, arXiv.org, revised Feb 2022.
    5. Fiorini, Mario & Stevens, Katrien, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Working Papers 2021-01, University of Sydney, School of Economics.
    6. Atı̇la Abdulkadı̇roğlu & Joshua D. Angrist & Yusuke Narita & Parag Pathak, 2022. "Breaking Ties: Regression Discontinuity Design Meets Market Design," Econometrica, Econometric Society, vol. 90(1), pages 117-151, January.
    7. 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.
    8. Joshua Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path from Cause to Effect," NBER Working Papers 29726, National Bureau of Economic Research, Inc.
    9. 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.
    10. Shenglong Liu & Yuanyuan Wan & Xiaoming Zhang, 2024. "Retirement Spillover Effects on Spousal Health in Urban China," Journal of Family and Economic Issues, Springer, vol. 45(3), pages 756-783, September.
    11. 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).
    12. Santiago Acerenza & Ot'avio Bartalotti & Federico Veneri, 2024. "Testing identifying assumptions in Tobit Models," Papers 2408.02573, arXiv.org.
    13. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.

  3. 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. Kyle Colangelo & Ying-Ying Lee, 2020. "Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments," Papers 2004.03036, arXiv.org, revised Sep 2023.
    2. Shi, Pengfei & Zhang, Xinyu & Zhong, Wei, 2024. "Estimating conditional average treatment effects with heteroscedasticity by model averaging and matching," Economics Letters, Elsevier, vol. 238(C).
    3. 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.
    4. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org.
    5. Knaus, Michael C., 2020. "Double Machine Learning Based Program Evaluation under Unconfoundedness," IZA Discussion Papers 13051, Institute of Labor Economics (IZA).
    6. Wei Huang & Oliver Linton & Zheng Zhang, 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Papers 2102.08063, arXiv.org, revised Sep 2021.
    7. 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.
    8. Nan Liu & Yanbo Liu & Yuya Sasaki, 2024. "Estimation and Inference for Causal Functions with Multiway Clustered Data," Papers 2409.06654, arXiv.org.
    9. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    10. 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.
    11. 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.
    12. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
    13. 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.
    14. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    15. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    16. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
    17. Geonwoo Kim & Suyong Song, 2024. "Double/Debiased CoCoLASSO of Treatment Effects with Mismeasured High-Dimensional Control Variables," Papers 2408.14671, arXiv.org.
    18. Claudia Noack & Tomasz Olma & Christoph Rothe, 2021. "Flexible Covariate Adjustments in Regression Discontinuity Designs," Papers 2107.07942, arXiv.org, revised Jul 2024.
    19. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org.
    20. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Oct 2024.
    21. Adam Baybutt & Manu Navjeevan, 2023. "Doubly-Robust Inference for Conditional Average Treatment Effects with High-Dimensional Controls," Papers 2301.06283, arXiv.org.
    22. Julius Owusu, 2024. "A Nonparametric Test of Heterogeneous Treatment Effects under Interference," Papers 2410.00733, arXiv.org.
    23. Arthur Charpentier & Emmanuel Flachaire & Ewen Gallic, 2023. "Optimal Transport for Counterfactual Estimation: A Method for Causal Inference," Papers 2301.07755, arXiv.org.
    24. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
    25. 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).
    26. Lucas Zhang, 2024. "Continuous difference-in-differences with double/debiased machine learning," Papers 2408.10509, arXiv.org.

  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. 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.
    2. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2020. "Unconditional Quantile Regression with High Dimensional Data," Papers 2007.13659, arXiv.org, revised Feb 2022.
    3. Stefan Tübbicke, 2020. "Entropy Balancing for Continuous Treatments," CEPA Discussion Papers 21, Center for Economic Policy Analysis.
    4. 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.
    5. Wei Huang & Oliver Linton & Zheng Zhang, 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Papers 2102.08063, arXiv.org, revised Sep 2021.
    6. 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.
    7. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    8. Yizhen Xu & Numair Sani & AmirEmad Ghassami & Ilya Shpitser, 2021. "Multiply Robust Causal Mediation Analysis with Continuous Treatments," Papers 2105.09254, arXiv.org, revised Oct 2024.
    9. 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.
    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. 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.
    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. Miguel Angel Saldarriaga, 2017. "Credit Booms in Commodity Exporters," Working Papers 98, Peruvian Economic Association.
    4. 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.
    5. 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.

  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. 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.
    3. 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.

  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 & 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. Kantorowicz, Jarosław & Köppl-Turyna, Monika, 2017. "Disentangling fiscal effects of local constitutions," Working Papers 06, Agenda Austria.
    3. 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.
    4. 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.

  9. 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. 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. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    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.

  10. 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. Ismael Mourifie & Marc Henry & Romuald Meango, 2017. "Sharp bounds and testability of a Roy model of STEM major choices," Papers 1709.09284, arXiv.org, revised Nov 2019.
    2. Matthew A. Masten & Alexandre Poirier, 2018. "Interpreting Quantile Independence," Papers 1804.10957, arXiv.org.
    3. Lixiong Li & D'esir'e K'edagni & Ismael Mourifi'e, 2020. "Discordant Relaxations of Misspecified Models," Papers 2012.11679, arXiv.org, revised Apr 2024.
    4. Henry, Marc & Méango, Romuald & Mourifié, Ismaël, 2024. "Role models and revealed gender-specific costs of STEM in an extended Roy model of major choice," Journal of Econometrics, Elsevier, vol. 238(2).
    5. Yoichi Arai & Taisuke Otsu & Mengshan Xu, 2022. "GLS under Monotone Heteroskedasticity," Papers 2210.13843, arXiv.org, revised Jan 2024.
    6. 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.
    7. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    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. 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.
    10. Zheng Fang & Juwon Seo, 2019. "A Projection Framework for Testing Shape Restrictions That Form Convex Cones," Papers 1910.07689, arXiv.org, revised Sep 2021.

  11. 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.

  12. 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. 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.
    2. Tahsin Mehdi, 2020. "Testing for Stochastic Dominance up to a Common Relative Poverty Line," Econometrics, MDPI, vol. 8(1), pages 1-9, February.
    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. 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.
    5. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2017. "Bayesian Assessment of Lorenz and Stochastic Dominance," Department of Economics - Working Papers Series 2029, The University of Melbourne.
    6. 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.
    7. 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.

  13. 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. Blaise Melly und Kaspar W thrich, 2016. "Local quantile treatment effects," Diskussionsschriften dp1605, Universitaet Bern, Departement Volkswirtschaft.
    2. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly & Kaspar Wüthrich, 2017. "Generic inference on quantile and quantile effect functions for discrete outcomes," CeMMAP working papers CWP23/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Huber, Martin & Wüthrich, Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," University of California at San Diego, Economics Working Paper Series qt4j29d8sc, Department of Economics, UC San Diego.
    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. Wüthrich, Kaspar, 2019. "A closed-form estimator for quantile treatment effects with endogeneity," Journal of Econometrics, Elsevier, vol. 210(2), pages 219-235.

  14. 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, 2006. "Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix," IEAS Working Paper : academic research 06-A009, Institute of Economics, Academia Sinica, Taipei, Taiwan.

  15. 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, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    2. 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.
    3. Tymon Sloczynski & Derya Uysal & Jeffrey Wooldridge, 2023. "Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect," Rationality and Competition Discussion Paper Series 424, CRC TRR 190 Rationality and Competition.
    4. Huber, Martin & Wüthrich, Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," University of California at San Diego, Economics Working Paper Series qt4j29d8sc, Department of Economics, UC San Diego.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.

  16. 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.

  17. 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. 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.
    2. 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.

  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, 2006. "Testing Over-Identifying Restrictions without Consistent Estimation of the Asymptotic Covariance Matrix," IEAS Working Paper : academic research 06-A009, 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, 2014. "A simple parametric model selection test," CeMMAP working papers 10/14, 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. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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).
    10. 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.
    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. Mohamed Coulibaly & Yu-Chin Hsu & Ismael Mourifié & Yuanyuan Wan, 2024. "A Sharp Test for the Judge Leniency Design," NBER Working Papers 32456, National Bureau of Economic Research, Inc.
    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. 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.
    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. 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.
    6. Hsu, Yu-Chin & Shen, Shu, 2019. "Testing treatment effect heterogeneity in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 208(2), pages 468-486.
    7. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    8. Zhou, Niwen & Guo, Xu & Zhu, Lixing, 2024. "Significance test for semiparametric conditional average treatment effects and other structural functions," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    9. 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.
    10. 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.
    11. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
    12. 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. 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. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
    3. Lechner, Michael & Knaus, Michael C. & Strittmatter, Anthony, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," CEPR Discussion Papers 13402, C.E.P.R. Discussion Papers.
    4. 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).
    5. 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.
    6. Miller, Steve, 2020. "Causal forest estimation of heterogeneous and time-varying environmental policy effects," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    7. 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.
    8. Wei Huang & Oliver Linton & Zheng Zhang, 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Papers 2102.08063, arXiv.org, revised Sep 2021.
    9. 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.
    10. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Apr 2024.
    11. 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.
    12. 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.
    13. 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.
    14. 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).
    15. Pengzhou Wu & Kenji Fukumizu, 2021. "$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap," Papers 2110.05225, arXiv.org.
    16. 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.
    17. 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.
    18. 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.
    19. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
    20. Yixiao Jiang, 2021. "Semiparametric Estimation of a Corporate Bond Rating Model," Econometrics, MDPI, vol. 9(2), pages 1-20, May.
    21. 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.
    22. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    23. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    24. Gabriel Okasa & Kenneth A. Younge, 2022. "Sample Fit Reliability," Papers 2209.06631, arXiv.org.
    25. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    26. 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.
    27. 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.
    28. 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.
    29. Zhou, Niwen & Guo, Xu & Zhu, Lixing, 2024. "Significance test for semiparametric conditional average treatment effects and other structural functions," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    30. Riccardo Di Francesco, 2024. "Aggregation Trees," Papers 2410.11408, arXiv.org.
    31. 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.
    32. 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.
    33. 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.
    34. Gregory Faletto, 2023. "Fused Extended Two-Way Fixed Effects for Difference-in-Differences With Staggered Adoptions," Papers 2312.05985, arXiv.org, revised Oct 2024.
    35. Barbera, Alessandro & Gereben, Aron & Wolski, Marcin, 2022. "Estimating conditional treatment effects of EIB lending to SMEs in Europe," EIB Working Papers 2022/03, European Investment Bank (EIB).
    36. 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.
    37. 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.
    38. Julius Owusu, 2024. "A Nonparametric Test of Heterogeneous Treatment Effects under Interference," Papers 2410.00733, arXiv.org.
    39. Arthur Charpentier & Emmanuel Flachaire & Ewen Gallic, 2023. "Optimal Transport for Counterfactual Estimation: A Method for Causal Inference," Papers 2301.07755, arXiv.org.
    40. Masahiro Kato, 2024. "Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects," Papers 2403.03240, arXiv.org.
    41. 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.
    42. 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.
    43. 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.
    44. 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.

  23. 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. Li, Xue & Haslag, Joseph H., 2021. "On Phase Shifts In A New Keynesian Model Economy," Macroeconomic Dynamics, Cambridge University Press, vol. 25(8), pages 2080-2101, December.
    3. 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.
    4. 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.
    5. 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.
    6. Guglielmo Maria Caporale & Gloria Claudio-Quiroga & Luis A. Gil-Alana, 2021. "The Relationship between Prices and Output in the UK and the US," CESifo Working Paper Series 8970, CESifo.

  24. 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. 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.
    2. 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.
    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. 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.
    6. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2024. "Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 239(2).
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. Victor Chernozhukov & Ivan Fernandez-Val & Blaise Melly & Kaspar Wüthrich, 2017. "Generic inference on quantile and quantile effect functions for discrete outcomes," CeMMAP working papers CWP23/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. García, A., 2016. "Oaxaca-Blinder Type Counterfactual Decomposition Methods for Duration Outcomes," Documentos de Trabajo 14186, Universidad del Rosario.
    13. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2018. "A Unified Framework for Efficient Estimation of General Treatment Models," Papers 1808.04936, arXiv.org, revised Aug 2018.
    14. Sant’Anna, Pedro H.C. & Song, Xiaojun, 2019. "Specification tests for the propensity score," Journal of Econometrics, Elsevier, vol. 210(2), pages 379-404.
    15. Wei Huang & Oliver Linton & Zheng Zhang, 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Papers 2102.08063, arXiv.org, revised Sep 2021.
    16. Tatsushi Oka & Shota Yasui & Yuta Hayakawa & Undral Byambadalai, 2024. "Regression Adjustment for Estimating Distributional Treatment Effects in Randomized Controlled Trials," Papers 2407.14074, arXiv.org.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
    22. 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.
    23. Pedro H. C. Sant'Anna, 2016. "Program Evaluation with Right-Censored Data," Papers 1604.02642, arXiv.org.
    24. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    25. 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).
    26. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    27. Yingjie Feng, 2020. "Causal Inference in Possibly Nonlinear Factor Models," Papers 2008.13651, arXiv.org, revised Oct 2021.
    28. 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.
    29. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    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. 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.
    36. 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.
    37. 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.
    38. 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.

  25. 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. Slichter, David, 2020. "Smile: A Simple Diagnostic for Selection on Observables," MPRA Paper 99921, University Library of Munich, Germany.
    2. 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.
    3. Huber, Martin, 2013. "A simple test for the ignorability of non-compliance in experiments," Economics Letters, Elsevier, vol. 120(3), pages 389-391.
    4. 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.
    5. 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).
    6. Dan Black & Joonhwi Joo & Robert LaLonde & Jeffrey Smith & Evan Taylor, 2020. "Simple Tests for Selection: Learning More from Instrumental Variables," Working Papers 2020-048, Human Capital and Economic Opportunity Working Group.
    7. 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.
    8. 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.
    9. Tymon S{l}oczy'nski, 2020. "When Should We (Not) Interpret Linear IV Estimands as LATE?," Papers 2011.06695, arXiv.org, revised Oct 2024.
    10. Wei Huang & Oliver Linton & Zheng Zhang, 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Papers 2102.08063, arXiv.org, revised Sep 2021.
    11. Bedoya, Guadalupe & Bittarello, Luca & Davis, Jonathan & Mittag, Nikolas, 2018. "Distributional Impact Analysis: Toolkit and Illustrations of Impacts beyond the Average Treatment Effect," IZA Discussion Papers 11863, Institute of Labor Economics (IZA).
    12. Huber, Martin & Wüthrich, Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," University of California at San Diego, Economics Working Paper Series qt4j29d8sc, Department of Economics, UC San Diego.
    13. 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.
    14. 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.
    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. Martin Huber & Jannis Kueck, 2022. "Testing the identification of causal effects in observational data," Papers 2203.15890, arXiv.org, revised Jun 2023.
    17. 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.
    18. Kazuhiko Shinoda & Takahiro Hoshino, 2022. "Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions," Papers 2212.13145, arXiv.org.
    19. 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.
    20. Sloczynski, Tymon, 2021. "When Should We (Not) Interpret Linear IV Estimands as LATE?," IZA Discussion Papers 14349, Institute of Labor Economics (IZA).
    21. Fang, Ying & Tang, Shengfang & Cai, Zongwu & Lin, Ming, 2020. "An alternative test for conditional unconfoundedness using auxiliary variables," Economics Letters, Elsevier, vol. 194(C).
    22. 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.
    23. 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.
    24. 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.
    25. Phillip Heiler, 2020. "Efficient Covariate Balancing for the Local Average Treatment Effect," Papers 2007.04346, arXiv.org.

  26. 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. 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.
    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. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    4. Matt Goldman & David M. Kaplan, 2017. "Comparing distributions by multiple testing across quantiles or CDF values," Papers 1708.04658, arXiv.org.
    5. Chang, Chia-Lin & Jiménez-Martín, Juan-Ángel & Maasoumi, Esfandiar & Pérez-Amaral, Teodosio, 2015. "A stochastic dominance approach to financial risk management strategies," Journal of Econometrics, Elsevier, vol. 187(2), pages 472-485.
    6. 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.
    7. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2017. "Bayesian Assessment of Lorenz and Stochastic Dominance," Department of Economics - Working Papers Series 2029, The University of Melbourne.
    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. Chang, C-L. & Jiménez-Martín, J.A. & McAleer, M.J. & Pérez-Amaral, T., 2015. "A Stochastic Dominance Approach to the Basel III Dilemma: Expected Shortfall or VaR?," Econometric Institute Research Papers EI2015-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. 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.
    11. Beare, Brendan K. & Shi, Xiaoxia, 2019. "An improved bootstrap test of density ratio ordering," Econometrics and Statistics, Elsevier, vol. 10(C), pages 9-26.
    12. Arvanitis, Stelios & Topaloglou, Nikolas, 2017. "Testing for prospect and Markowitz stochastic dominance efficiency," Journal of Econometrics, Elsevier, vol. 198(2), pages 253-270.
    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. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    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. 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.
    17. Hsu, Yu-Chin & Shen, Shu, 2019. "Testing treatment effect heterogeneity in regression discontinuity designs," Journal of Econometrics, Elsevier, vol. 208(2), pages 468-486.
    18. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    19. Brendan K. Beare & Jackson D. Clarke, 2022. "Modified Wilcoxon-Mann-Whitney tests of stochastic dominance," Papers 2210.08892, arXiv.org.
    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. 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.
    22. 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.
    23. Rami V. Tabri & Mathew J. Elias, 2024. "Testing for Restricted Stochastic Dominance under Survey Nonresponse with Panel Data: Theory and an Evaluation of Poverty in Australia," Papers 2406.15702, arXiv.org.
    24. 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.
    25. 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.
    26. 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.

  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. 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.
    2. Badi H. Baltagi & Chihwa Kao & Long Liu, 2015. "Estimation and Identification of Change Points in Panel Models with Nonstationary or Stationary Regressors and Error Term," Center for Policy Research Working Papers 178, Center for Policy Research, Maxwell School, Syracuse University.
    3. 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.
    4. 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.
    5. 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.
    6. Chang, Seong Yeon, 2021. "Estimation of a level shift in panel data with fractionally integrated errors," Economics Letters, Elsevier, vol. 206(C).

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. Christis Katsouris, 2023. "Structural Analysis of Vector Autoregressive Models," Papers 2312.06402, arXiv.org, revised Feb 2024.
    2. 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.
    3. Arthur Charpentier & Emmanuel Flachaire & Ewen Gallic, 2023. "Optimal Transport for Counterfactual Estimation: A Method for Causal Inference," Papers 2301.07755, arXiv.org.

  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 & 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.

  5. 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).

  6. 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.

  7. 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. Victor Aguirregabiria, 2023. "Dynamic demand for differentiated products with fixed-effects unobserved heterogeneity," The Econometrics Journal, Royal Economic Society, vol. 26(1), pages 1-25.
    2. Victor Aguirregabiria & Jesus M. Carro, 2021. "Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models," Papers 2107.06141, arXiv.org, revised Jul 2024.
    3. Cavit Pakel & Martin Weidner, 2023. "Bounds on Average Effects in Discrete Choice Panel Data Models," Papers 2309.09299, arXiv.org, revised May 2024.
    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 Jul 2024.

  8. 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. 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.
    2. 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).
    3. 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.

  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. 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).
    2. 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.
    3. 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.
    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. Wei Huang & Oliver Linton & Zheng Zhang, 2021. "A Unified Framework for Specification Tests of Continuous Treatment Effect Models," Papers 2102.08063, arXiv.org, revised Sep 2021.
    6. Le-Yu Chen & Yu-Min Yen, 2021. "Estimations of the Local Conditional Tail Average Treatment Effect," Papers 2109.08793, arXiv.org, revised May 2024.
    7. Centorrino, Samuele & Pérez-Urdiales, María & Bravo-Ureta, Boris & Wall, Alan, 2022. "Binary endogenous treatment in stochastic frontier models with an application to soil conservation in El Salvador," Efficiency Series Papers 2022/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).

  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. 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. 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. 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.
    3. Matias D. Cattaneo & Rocio Titiunik, 2021. "Regression Discontinuity Designs," Papers 2108.09400, arXiv.org, revised Feb 2022.
    4. 'Agoston Reguly, 2021. "Heterogeneous Treatment Effects in Regression Discontinuity Designs," Papers 2106.11640, arXiv.org, revised Oct 2021.
    5. Cappelletti, Matilde & Giuffrida, Leonardo Maria & Rovigatti, Gabriele, 2024. "Procuring Survival," CEPR Discussion Papers 18796, C.E.P.R. Discussion Papers.
    6. Pedro Forquesato, 2022. "Who Benefits from Political Connections in Brazilian Municipalities," Papers 2204.09450, arXiv.org.
    7. 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).
    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. Cappelletti, Matilde & Giuffrida, Leonardo M., 2021. "Procuring survival," ZEW Discussion Papers 21-093, ZEW - Leibniz Centre for European Economic Research.
    10. Matias D. Cattaneo & Luke Keele & Rocio Titiunik, 2021. "Covariate Adjustment in Regression Discontinuity Designs," Papers 2110.08410, arXiv.org, revised Aug 2022.
    11. Rodríguez Arenas, Jorge Leonardo, 2024. "¿Ampliando oportunidades o desigualdades? Efectos de un crédito-beca en estudiantes de bajo desempeño académico," Documentos CEDE 21189, Universidad de los Andes, Facultad de Economía, CEDE.
    12. Likai Chen & Georg Keilbar & Liangjun Su & Weining Wang, 2023. "Inference on many jumps in nonparametric panel regression models," Papers 2312.01162, arXiv.org, revised Aug 2024.
    13. Bansak, Kirk & Nowacki, Tobias, 2022. "Effect Heterogeneity and Causal Attribution in Regression Discontinuity Designs," SocArXiv vj34m, Center for Open Science.

  13. 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.
  14. 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.
  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. 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. Matias D. Cattaneo & Rocio Titiunik, 2021. "Regression Discontinuity Designs," Papers 2108.09400, arXiv.org, revised Feb 2022.
    3. He, Yang & Bartalotti, Otávio, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals," ISU General Staff Papers 202005010700001071, Iowa State University, Department of Economics.
    4. Mauricio Villamizar-Villegas & Freddy A. Pinzón-Puerto & María Alejandra Ruiz-Sánchez, 2020. "A Comprehensive History of Regression Discontinuity Designs: An Empirical Survey of the last 60 Years," Borradores de Economia 1112, Banco de la Republica de Colombia.
    5. 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).
    6. Zhengyu Zhang & Zequn Jin & Lihua Lin, 2024. "Identification and inference of outcome conditioned partial effects of general interventions," Papers 2407.16950, arXiv.org.
    7. Blaise Melly & Rafael Lalive, 2020. "Estimation, Inference, and Interpretation in the Regression Discontinuity Design," Diskussionsschriften dp2016, Universitaet Bern, Departement Volkswirtschaft.
    8. Jun Ma & Zhengfei Yu, 2020. "Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs," Papers 2008.09263, arXiv.org, revised May 2024.

  16. 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. Hoshino, Tadao & Yanagi, Takahide, 2023. "Treatment effect models with strategic interaction in treatment decisions," Journal of Econometrics, Elsevier, vol. 236(2).
    2. Rami V. Tabri & Christopher D. Walker, 2020. "Inference for Moment Inequalities: A Constrained Moment Selection Procedure," Papers 2008.09021, arXiv.org, revised Aug 2020.
    3. 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.
    4. Qingliang Fan & Zijian Guo & Ziwei Mei, 2022. "A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates," Papers 2205.00171, arXiv.org, revised May 2024.
    5. 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.
    6. 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.
    7. Ye Yang & Osman Dogan & Suleyman Taspinar & Fei Jin, 2023. "A Review of Cross-Sectional Matrix Exponential Spatial Models," Papers 2311.14813, arXiv.org.
    8. Liu, Tuo & Lee, Lung-fei, 2019. "A likelihood ratio test for spatial model selection," Journal of Econometrics, Elsevier, vol. 213(2), pages 434-458.

  17. 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.
  18. 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.
  19. 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.
  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. 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).
    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.
    3. Krzysztof Piasecki & Anna Łyczkowska-Hanćkowiak, 2019. "Representation of Japanese Candlesticks by Oriented Fuzzy Numbers," Econometrics, MDPI, vol. 8(1), pages 1-24, December.
    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. 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.
    6. 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.
    7. 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.

  23. 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.
  24. 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.
  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.
    See citations under working paper version above.
  26. 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.
  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. 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.
    2. 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.
    3. Fiorini, Mario & Stevens, Katrien, 2021. "Scrutinizing the Monotonicity Assumption in IV and fuzzy RD designs," Working Papers 2021-01, University of Sydney, School of Economics.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Franck A. Cowell & Emmanuel Flachaire, 2015. "Statistical Methods for Distributional Analysis," AMSE Working Papers 1507, Aix-Marseille School of Economics, France.
    10. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    11. 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.
    12. 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, Springer;Swiss Society of Economics and Statistics, vol. 152(1), pages 49-80, January.
    13. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    14. 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.
    15. 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.
    16. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    17. 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.
    18. 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.
    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.

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    1. Martin Huber, 2015. "Testing the Validity of the Sibling Sex Ratio Instrument," LABOUR, CEIS, vol. 29(1), pages 1-14, March.
    2. 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.
    3. David M. Kaplan & Longhao Zhuo, 2018. "Frequentist size of Bayesian inequality tests," Working Papers 1802, Department of Economics, University of Missouri, revised 14 Jul 2019.
    4. 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.
    5. 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.

  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.

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    1. Coakley, Jerry & Marzano, Michele & Nankervis, John, 2016. "How profitable are FX technical trading rules?," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 273-282.
    2. Costantini, Mauro & Crespo Cuaresma, Jesus & Hlouskova, Jaroslava, 2014. "Can Macroeconomists Get Rich Forecasting Exchange Rates?," Department of Economics Working Paper Series 176, WU Vienna University of Economics and Business.
    3. 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.
    4. Andriosopoulos, Kostas & Doumpos, Michael & Papapostolou, Nikos C. & Pouliasis, Panos K., 2013. "Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 52(C), pages 16-34.
    5. Wang, Shan & Jiang, Zhi-Qiang & Li, Sai-Ping & Zhou, Wei-Xing, 2015. "Testing the performance of technical trading rules in the Chinese markets based on superior predictive test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 114-123.
    6. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    7. 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.
    8. Yang Zhao & Charalampos Stasinakis & Georgios Sermpinis & Filipa Da Silva Fernandes, 2019. "Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1443-1463, October.
    9. 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).
    10. Taylor, Mark & Hsu, Po-Hsuan & Wang, Zigan, 2020. "The Out-of-Sample Performance of Carry Trades," CEPR Discussion Papers 15052, C.E.P.R. Discussion Papers.
    11. 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.
    12. Zarrabi, Nima & Snaith, Stuart & Coakley, Jerry, 2017. "FX technical trading rules can be profitable sometimes!," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 113-127.
    13. Jesus Crespo Cuaresma & Ines Fortin & Jaroslava Hlouskova, 2018. "Exchange rate forecasting and the performance of currency portfolios," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 519-540, August.
    14. Hung, Chiayu & Lai, Hung-Neng, 2022. "Information asymmetry and the profitability of technical analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    15. Anghel, Dan Gabriel, 2021. "Data Snooping Bias in Tests of the Relative Performance of Multiple Forecasting Models," Journal of Banking & Finance, Elsevier, vol. 126(C).
    16. Chuang, O-Chia & Chuang, Hui-Ching & Wang, Zixuan & Xu, Jin, 2024. "Profitability of technical trading rules in the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    17. Kuang, P. & Schröder, M. & Wang, Q., 2014. "Illusory profitability of technical analysis in emerging foreign exchange markets," International Journal of Forecasting, Elsevier, vol. 30(2), pages 192-205.
    18. Shynkevich, Andrei, 2012. "Short-term predictability of equity returns along two style dimensions," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 675-685.
    19. 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.
    20. Kearney, Fearghal & Cummins, Mark & Murphy, Finbarr, 2014. "Outperformance in exchange-traded fund pricing deviations: Generalized control of data snooping bias," Journal of Financial Markets, Elsevier, vol. 19(C), pages 86-109.
    21. Jying‐Nan Wang & Hung‐Chun Liu & Jiangze Du & Yuan‐Teng Hsu, 2019. "Economic benefits of technical analysis in portfolio management: Evidence from global stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 890-902, April.
    22. Christopher J. Neely & Paul A. Weller, 2011. "Technical analysis in the foreign exchange market," Working Papers 2011-001, Federal Reserve Bank of St. Louis.
    23. Suzuki, Tomoya & Ohkura, Yuushi, 2016. "Financial technical indicator based on chaotic bagging predictors for adaptive stock selection in Japanese and American markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 50-66.
    24. Huang, Jing-Zhi & Huang, Zhijian (James), 2020. "Testing moving average trading strategies on ETFs," Journal of Empirical Finance, Elsevier, vol. 57(C), pages 16-32.
    25. Zongwu Cai & Jiancheng Jiang & Jingshuang Zhang & Xibin Zhang, 2015. "A new semiparametric test for superior predictive ability," Empirical Economics, Springer, vol. 48(1), pages 389-405, February.
    26. Joseph P. Romano & Michael Wolf, 2017. "Multiple testing of one-sided hypotheses: combining Bonferroni and the bootstrap," ECON - Working Papers 254, Department of Economics - University of Zurich.
    27. Taylor, Mark & Hsu, Po-Hsuan, 2014. "Forty Years, Thirty Currencies and 21,000 Trading Rules: A Large-scale, Data-Snooping Robust Analysis of Technical Trading in t," CEPR Discussion Papers 10018, C.E.P.R. Discussion Papers.
    28. Shynkevich, Andrei, 2012. "Performance of technical analysis in growth and small cap segments of the US equity market," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 193-208.
    29. Shynkevich, Andrei, 2016. "Predictability in bond returns using technical trading rules," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 55-69.
    30. Hong, Hui & Chen, Naiwei & O’Brien, Fergal & Ryan, James, 2018. "Stock return predictability and model instability: Evidence from mainland China and Hong Kong," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 132-142.
    31. 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.
    32. Xiaoye Jin, 2022. "Evaluating the predictive power of intraday technical trading in China's crude oil market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1416-1432, November.
    33. 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.
    34. 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.
    35. Sermpinis, Georgios & Hassanniakalager, Arman & Stasinakis, Charalampos & Psaradellis, Ioannis, 2021. "Technical analysis profitability and Persistence: A discrete false discovery approach on MSCI indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    36. Christopher J. Bennett & Shabana Mitra, 2011. "Multidimensional Poverty: Measurement, Estimation, and Inference," OPHI Working Papers ophiwp047, Queen Elizabeth House, University of Oxford.
    37. Jin, Xiaoye, 2022. "Performance of intraday technical trading in China’s gold market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    38. Hambuckers, J. & Ulm, M., 2023. "On the role of interest rate differentials in the dynamic asymmetry of exchange rates," Economic Modelling, Elsevier, vol. 129(C).
    39. Isakov, Dusan & Marti, Didier, 2011. "Technical Analysis with a Long-Term Perspective: Trading Strategies and Market Timing Ability," FSES Working Papers 421, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    40. Flavio Ivo Riedlinger & João Nicolau, 2020. "The Profitability in the FTSE 100 Index: A New Markov Chain Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(1), pages 61-81, March.
    41. Nima Zarrabi & Stuart Snaith & Jerry Coakley, 2022. "Exchange rate forecasting using economic models and technical trading rules," The European Journal of Finance, Taylor & Francis Journals, vol. 28(10), pages 997-1018, July.
    42. Ioana-Andreea Boboc & Mihai-Cristian Dinică, 2013. "An Algorithm for Testing the Efficient Market Hypothesis," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-11, October.
    43. Andrei Shynkevich, 2021. "Impact of bitcoin futures on the informational efficiency of bitcoin spot market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(1), pages 115-134, January.
    44. Eero P䴤ri & Mika Vilska, 2014. "Performance of moving average trading strategies over varying stock market conditions: the Finnish evidence," Applied Economics, Taylor & Francis Journals, vol. 46(24), pages 2851-2872, August.
    45. 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.
    46. Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios, 2023. "Technical analysis, spread trading, and data snooping control," International Journal of Forecasting, Elsevier, vol. 39(1), pages 178-191.
    47. Hsu, Po-Hsuan & Taylor, Mark P. & Wang, Zigan, 2016. "Technical trading: Is it still beating the foreign exchange market?," Journal of International Economics, Elsevier, vol. 102(C), pages 188-208.
    48. Dan Anghel, 2013. "How Reliable is the Moving Average Crossover Rule for an Investor on the Romanian Stock Market?," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 5(2), pages 089-115, December.
    49. Qing Zhou & Robert Faff, 2017. "The complementary role of cross-sectional and time-series information in forecasting stock returns," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 113-139, February.
    50. Christopher J. Bennett, 2009. "p-Value Adjustments for Asymptotic Control of the Generalized Familywise Error Rate," Vanderbilt University Department of Economics Working Papers 0905, Vanderbilt University Department of Economics.
    51. Mihai Cristian Dinică & Erica Cristina (Balea) Dinică, 2015. "Testing the Weak-Form Market Eficiency of the Euronext Wheat," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 18(55), pages 25-38, March.
    52. 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).
    53. Hui Hong & Zhicun Bian & Chien-Chiang Lee, 2021. "COVID-19 and instability of stock market performance: evidence from the U.S," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-18, December.
    54. 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).
    55. Shynkevich, Andrei, 2013. "Time-series momentum as an intra- and inter-industry effect: Implications for market efficiency," Journal of Economics and Business, Elsevier, vol. 69(C), pages 64-85.
    56. Kao, Yi-Cheng & Kuan, Chung-Ming & Chen, Shikuan, 2013. "Testing the predictive power of the term structure without data snooping bias," Economics Letters, Elsevier, vol. 121(3), pages 546-549.
    57. Anghel, Dan Gabriel, 2022. "No pain, no gain: You should always incorporate trading costs for a bias-free evaluation of trading rule overperformance," Economics Letters, Elsevier, vol. 216(C).
    58. 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.
    59. Georgios Sermpinis & Arman Hassanniakalager & Charalampos Stasinakis & Ioannis Psaradellis, 2018. "Technical Analysis and Discrete False Discovery Rate: Evidence from MSCI Indices," Papers 1811.06766, arXiv.org, revised Jun 2019.
    60. 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.
    61. Zongwu Cai & Jiancheng Jiang & Jingshuang Zhang, 2013. "A New Test for Superior Predictive Ability," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    62. 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).

  31. Kuan, Chung-Ming & Yeh, Jin-Huei & Hsu, Yu-Chin, 2009. "Assessing value at risk with CARE, the Conditional Autoregressive Expectile models," Journal of Econometrics, Elsevier, vol. 150(2), pages 261-270, June.

    Cited by:

    1. Taoufik Bouezmarni & Mohamed Doukali & Abderrahim Taamouti, 2023. "Testing Granger Non-Causality in Expectiles," University of East Anglia School of Economics Working Paper Series 2023-02, School of Economics, University of East Anglia, Norwich, UK..
    2. Xiu Xu & Andrija Mihoci & Wolfgang Karl Hardle, 2020. "lCARE -- localizing Conditional AutoRegressive Expectiles," Papers 2009.13215, arXiv.org.
    3. Hamidi, Benjamin & Maillet, Bertrand & Prigent, Jean-Luc, 2014. "A dynamic autoregressive expectile for time-invariant portfolio protection strategies," Journal of Economic Dynamics and Control, Elsevier, vol. 46(C), pages 1-29.
    4. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2017. "Extreme M-quantiles as risk measures: From L1 to Lp optimization," TSE Working Papers 17-841, Toulouse School of Economics (TSE).
    5. Alex Huang, 2013. "Value at risk estimation by quantile regression and kernel estimator," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 225-251, August.
    6. Edgars Jakobsons & Steven Vanduffel, 2015. "Dependence Uncertainty Bounds for the Expectile of a Portfolio," Risks, MDPI, vol. 3(4), pages 1-25, December.
    7. López-Espinosa, Germán & Moreno, Antonio & Rubia, Antonio & Valderrama, Laura, 2015. "Systemic risk and asymmetric responses in the financial industry," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 471-485.
    8. Man, Rebeka & Tan, Kean Ming & Wang, Zian & Zhou, Wen-Xin, 2024. "Retire: Robust expectile regression in high dimensions," Journal of Econometrics, Elsevier, vol. 239(2).
    9. Song, Song & Ritov, Ya’acov & Härdle, Wolfgang K., 2012. "Bootstrap confidence bands and partial linear quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 244-262.
    10. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2018. "Tail expectile process and risk assessment," TSE Working Papers 18-944, Toulouse School of Economics (TSE).
    11. Yundong Tu & Siwei Wang, 2023. "Variable Screening and Model Averaging for Expectile Regressions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 574-598, June.
    12. Litimein, Ouahiba & Laksaci, Ali & Mechab, Boubaker & Bouzebda, Salim, 2023. "Local linear estimate of the functional expectile regression," Statistics & Probability Letters, Elsevier, vol. 192(C).
    13. Tran, Ngoc M. & Burdejová, Petra & Ospienko, Maria & Härdle, Wolfgang K., 2019. "Principal component analysis in an asymmetric norm," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 1-21.
    14. Wang, Bingling & Li, Yingxing & Härdle, Wolfgang, 2021. "K-expectiles clustering," IRTG 1792 Discussion Papers 2021-003, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    15. Daouia, Abdelaati & Padoan, Simone A. & Stupfler, Gilles, 2023. "Extreme expectile estimation for short-tailed data, with an application to market risk assessment," TSE Working Papers 23-1414, Toulouse School of Economics (TSE), revised May 2024.
    16. Stéphane Girard & Gilles Claude Stupfler & Antoine Usseglio-Carleve, 2021. "Extreme Conditional Expectile Estimation in Heavy-Tailed Heteroscedastic Regression Models," Post-Print hal-03306230, HAL.
    17. Jakobsons Edgars, 2016. "Scenario aggregation method for portfolio expectile optimization," Statistics & Risk Modeling, De Gruyter, vol. 33(1-2), pages 51-65, September.
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    19. Carol Alexander & José María Sarabia, 2012. "Quantile Uncertainty and Value‐at‐Risk Model Risk," Risk Analysis, John Wiley & Sons, vol. 32(8), pages 1293-1308, August.
    20. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2013. "Food versus Fuel: Causality and Predictability in Distribution," Working Papers 2013.23, Fondazione Eni Enrico Mattei.
    21. Abdelaati Daouia & Gilles Stupfler & Antoine Usseglio-Carleve, 2024. "An expectile computation cookbook," Post-Print hal-04524319, HAL.
    22. Dingshi Tian & Zongwu Cai & Ying Fang, 2018. "Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201807, University of Kansas, Department of Economics, revised Oct 2018.
    23. Abdelaati Daouia & Stéphane Girard & Gilles Stupfler, 2018. "Estimation of tail risk based on extreme expectiles," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(2), pages 263-292, March.
    24. Mohammedi, Mustapha & Bouzebda, Salim & Laksaci, Ali, 2021. "The consistency and asymptotic normality of the kernel type expectile regression estimator for functional data," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
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    26. Härdle, Wolfgang Karl & Ritov, Ya'acov & Song, Song, 2010. "Partial linear quantile regression and bootstrap confidence bands," SFB 649 Discussion Papers 2010-002, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    27. Tu, Yundong & Wang, Siwei, 2020. "Jackknife model averaging for expectile regressions in increasing dimension," Economics Letters, Elsevier, vol. 197(C).
    28. Fracasso, Laís Martins & Müller, Fernanda Maria & Ramos, Henrique Pinto & Righi, Marcelo Brutti, 2023. "Is there a risk premium? Evidence from thirteen measures," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 182-199.
    29. Tae-Hwy Lee & Aman Ullah & He Wang, 2018. "The Second-order Asymptotic Properties of Asymmetric Least Squares Estimation," Working Papers 201910, University of California at Riverside, Department of Economics.
    30. Xu, Xiu & Mihoci, Andrija & Härdle, Wolfgang Karl, 2018. "lCARE - localizing conditional autoregressive expectiles," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 198-220.
    31. Zhang, Yue-Jun & Bouri, Elie & Gupta, Rangan & Ma, Shu-Jiao, 2021. "Risk spillover between Bitcoin and conventional financial markets: An expectile-based approach," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    32. Härdle, Wolfgang Karl & Ling, Chengxiu, 2018. "How Sensitive are Tail-related Risk Measures in a Contamination Neighbourhood?," IRTG 1792 Discussion Papers 2018-010, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    33. Garcia-Jorcano, Laura & Sanchis-Marco, Lidia, 2021. "Systemic-systematic risk in financial system: A dynamic ranking based on expectiles," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 330-365.
    34. Yao, Yinhong & Li, Jianping & Sun, Xiaolei, 2021. "Measuring the risk of Chinese Fintech industry: evidence from the stock index," Finance Research Letters, Elsevier, vol. 39(C).
    35. 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".
    36. Marcel, Bräutigam & Marie, Kratz, 2018. "On the Dependence between Quantiles and Dispersion Estimators," ESSEC Working Papers WP1807, ESSEC Research Center, ESSEC Business School.
    37. Stupfler, Gilles & Yang, Fan, 2018. "Analyzing And Predicting Cat Bond Premiums: A Financial Loss Premium Principle And Extreme Value Modeling," ASTIN Bulletin, Cambridge University Press, vol. 48(1), pages 375-411, January.
    38. Marcel Brautigam & Marie Kratz, 2020. "The Impact of the Choice of Risk and Dispersion Measure on Procyclicality," Papers 2001.00529, arXiv.org.
    39. Ying Fu & Kien Ng & Boray Huang & Huei Huang, 2015. "Portfolio optimization with transaction costs: a two-period mean-variance model," Annals of Operations Research, Springer, vol. 233(1), pages 135-156, October.
    40. Lina Liao & Cheolwoo Park & Hosik Choi, 2019. "Penalized expectile regression: an alternative to penalized quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 409-438, April.
    41. Zongwu Cai & Ying Fang & Dingshi Tian, 2018. "Assessing Tail Risk Using Expectile Regressions with Partially Varying Coefficients," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201804, University of Kansas, Department of Economics, revised Oct 2018.
    42. Ren, Rui & Lu, Meng-Jou & Li, Yingxing & Härdle, Wolfgang Karl, 2022. "Financial Risk Meter FRM based on Expectiles," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
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    44. Johanna F. Ziegel, 2013. "Coherence and elicitability," Papers 1303.1690, arXiv.org, revised Mar 2014.
    45. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.
    46. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2021. "ExpectHill estimation, extreme risk and heavy tails," Journal of Econometrics, Elsevier, vol. 221(1), pages 97-117.
    47. Laura Garcia-Jorcano & Lidia Sanchis-Marco, 2023. "Measuring Systemic Risk Using Multivariate Quantile-Located ES Models," Journal of Financial Econometrics, Oxford University Press, vol. 21(1), pages 1-72.
    48. Fabio Busetti & Michele Caivano & Davide Delle Monache & Claudia Pacella, 2020. "The time-varying risk of Italian GDP," Temi di discussione (Economic working papers) 1288, Bank of Italy, Economic Research and International Relations Area.
    49. Yingying Jiang & Fuming Lin & Yong Zhou, 2021. "The kth power expectile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(1), pages 83-113, February.
    50. Syuhada, Khreshna & Hakim, Arief & Suprijanto, Djoko, 2024. "Assessing systemic risk and connectedness among dirty and clean energy markets from the quantile and expectile perspectives," Energy Economics, Elsevier, vol. 129(C).
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  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.

Software components

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Chapters

  1. Robert P. Lieli & Yu-Chin Hsu & Ágoston Reguly, 2022. "The Use of Machine Learning in Treatment Effect Estimation," Advanced Studies in Theoretical and Applied Econometrics, in: Felix Chan & László Mátyás (ed.), Econometrics with Machine Learning, chapter 0, pages 79-109, Springer.

    Cited by:

    1. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).
    2. Martin Huber, 2024. "An Introduction to Causal Discovery," Papers 2407.08602, arXiv.org.

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