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Ryo Okui

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. Andreas Dzemski & Ryo Okui, 2020. "Convergence rate of estimators of clustered panel models with misclassification," Papers 2008.04708, arXiv.org.

    Cited by:

    1. Lumsdaine, Robin L. & Okui, Ryo & Wang, Wendun, 2023. "Estimation of panel group structure models with structural breaks in group memberships and coefficients," Journal of Econometrics, Elsevier, vol. 233(1), pages 45-65.

  2. Tanjim Hossain & Ryo Okui, 2019. "Belief Formation Under Signal Correlation," Working Paper Series no115, Institute of Economic Research, Seoul National University.

    Cited by:

    1. David Danz & Lise Vesterlund & Alistair J. Wilson, 2020. "Belief Elicitation: Limiting Truth Telling with Information on Incentives," CESifo Working Paper Series 8048, CESifo.

  3. Ryo Okui & Takahide Yanagi, 2018. "Panel Data Analysis with Heterogeneous Dynamics," Papers 1803.09452, arXiv.org, revised Jan 2019.

    Cited by:

    1. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    2. Artūras Juodis & Simas Kučinskas, 2023. "Quantifying noise in survey expectations," Quantitative Economics, Econometric Society, vol. 14(2), pages 609-650, May.
    3. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
    4. Jochmans, K. & Weidner, M., 2019. "Inference on a distribution from noisy draws," Cambridge Working Papers in Economics 1946, Faculty of Economics, University of Cambridge.
    5. Fernández-Val, Iván & Gao, Wayne Yuan & Liao, Yuan & Vella, Francis, 2022. "Dynamic Heterogeneous Distribution Regression Panel Models, with an Application to Labor Income Processes," IZA Discussion Papers 15236, Institute of Labor Economics (IZA).
    6. Yazgan, M. Ege & Yilmazkuday, Hakan, 2011. "Price-level convergence: New evidence from U.S. cities," Economics Letters, Elsevier, vol. 110(2), pages 76-78, February.
    7. Stéphane Bonhomme & Martin Weidner, 2019. "Posterior average effects," CeMMAP working papers CWP43/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Lumsdaine, Robin L. & Okui, Ryo & Wang, Wendun, 2023. "Estimation of panel group structure models with structural breaks in group memberships and coefficients," Journal of Econometrics, Elsevier, vol. 233(1), pages 45-65.
    9. Ryo Okui, 2017. "Misspecification in Dynamic Panel Data Models and Model-Free Inferences," The Japanese Economic Review, Springer, vol. 68(3), pages 283-304, September.
    10. Laurent Barras & Patrick Gagliardini & Olivier Scaillet, 2022. "Skill, Scale, and Value Creation in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 77(1), pages 601-638, February.

  4. Ryo Okui & Wendun Wang, 2018. "Heterogeneous structural breaks in panel data models," Papers 1801.04672, arXiv.org, revised Nov 2018.

    Cited by:

    1. Liu, Yanbo & Phillips, Peter C. B. & Yu, Jun, 2022. "A Panel Clustering Approach to Analyzing Bubble Behavior," Economics and Statistics Working Papers 1-2022, Singapore Management University, School of Economics.
    2. Leng, Xuan & Chen, Heng & Wang, Wendun, 2023. "Multi-dimensional latent group structures with heterogeneous distributions," Journal of Econometrics, Elsevier, vol. 233(1), pages 1-21.
    3. Joana Almeida & Raquel M. Gaspar, 2021. "Accuracy of European Stock Target Prices," JRFM, MDPI, vol. 14(9), pages 1-27, September.
    4. Yiren Wang & Peter C. B. Phillips & Liangjun Su, 2023. "Panel Data Models with Time-Varying Latent Group Structures," Cowles Foundation Discussion Papers 2364, Cowles Foundation for Research in Economics, Yale University.
    5. Weijie Cui & Yong Li, 2023. "Bicluster Analysis of Heterogeneous Panel Data via M-Estimation," Mathematics, MDPI, vol. 11(10), pages 1-19, May.
    6. Lu, Xun & Su, Liangjun, 2023. "Uniform inference in linear panel data models with two-dimensional heterogeneity," Journal of Econometrics, Elsevier, vol. 235(2), pages 694-719.
    7. Dzemski, Andreas & Okui, Ryo, 2020. "Convergence rate of estimators of clustered panel models with misclassication," Working Papers in Economics 790, University of Gothenburg, Department of Economics.
    8. Lumsdaine, Robin L. & Okui, Ryo & Wang, Wendun, 2023. "Estimation of panel group structure models with structural breaks in group memberships and coefficients," Journal of Econometrics, Elsevier, vol. 233(1), pages 45-65.
    9. Boyuan Zhang, 2022. "Incorporating Prior Knowledge of Latent Group Structure in Panel Data Models," Papers 2211.16714, arXiv.org, revised Oct 2023.
    10. Wang, Wuyi & Su, Liangjun, 2017. "Identifying Latent Group Structures in Nonlinear Panels," Economics and Statistics Working Papers 19-2017, Singapore Management University, School of Economics.
    11. Yiren Wang & Liangjun Su & Yichong Zhang, 2022. "Low-rank Panel Quantile Regression: Estimation and Inference," Papers 2210.11062, arXiv.org.
    12. Jiang, Peiyun & Kurozumi, Eiji, 2021. "A new test for common breaks in heterogeneous panel data models," Discussion paper series HIAS-E-107, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    13. Su, Liangjun & Wang, Wuyi & Xu, Xingbai, 2023. "Identifying latent group structures in spatial dynamic panels," Journal of Econometrics, Elsevier, vol. 235(2), pages 1955-1980.

  5. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.

    Cited by:

    1. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    2. Jochmans, K. & Weidner, M., 2019. "Inference on a distribution from noisy draws," Cambridge Working Papers in Economics 1946, Faculty of Economics, University of Cambridge.
    3. Yazgan, M. Ege & Yilmazkuday, Hakan, 2011. "Price-level convergence: New evidence from U.S. cities," Economics Letters, Elsevier, vol. 110(2), pages 76-78, February.
    4. Mohd Alsaleh & A. S. Abdul-Rahim, 2022. "An evaluation of bioenergy industry sustainability impacts on forest degradation: evidence from European Union economies," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 1738-1760, February.
    5. Stéphane Bonhomme & Martin Weidner, 2019. "Posterior average effects," CeMMAP working papers CWP43/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    6. Laurent Barras & Patrick Gagliardini & Olivier Scaillet, 2022. "Skill, Scale, and Value Creation in the Mutual Fund Industry," Journal of Finance, American Finance Association, vol. 77(1), pages 601-638, February.

  6. Yanchun Jin & Ryo Okui, 2018. "Testing for Overconfidence Statistically: A Moment Inequality Approach," KIER Working Papers 984, Kyoto University, Institute of Economic Research.

    Cited by:

    1. Ryo Okui, 2021. "A moment inequality approach to statistical inference for rankings," The Japanese Economic Review, Springer, vol. 72(2), pages 169-184, April.
    2. Daiki Kishishita & Atsushi Yamagishi & Tomoko Matsumoto, 2021. "Overconfidence, Income-Ability Gap, and Preferences for Income Equality," Working Papers e159, Tokyo Center for Economic Research.

  7. Andreas Dzemski & Ryo Okui, 2017. "Confidence set for group membership," Papers 1801.00332, arXiv.org, revised Nov 2023.

    Cited by:

    1. Leng, Xuan & Chen, Heng & Wang, Wendun, 2023. "Multi-dimensional latent group structures with heterogeneous distributions," Journal of Econometrics, Elsevier, vol. 233(1), pages 1-21.
    2. Max Cytrynbaum, 2020. "Blocked Clusterwise Regression," Papers 2001.11130, arXiv.org.
    3. Ryo Okui, 2021. "A moment inequality approach to statistical inference for rankings," The Japanese Economic Review, Springer, vol. 72(2), pages 169-184, April.

  8. Sokbae Lee & Ryo Okui & Yoon-Jae Whang, 2016. "Doubly Robust Uniform Confidence Band for the Conditional Average Treatment Effect Function," Papers 1601.02801, arXiv.org, revised Oct 2016.

    Cited by:

    1. Lechner, Michael, 2019. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," CEPR Discussion Papers 13430, C.E.P.R. Discussion Papers.
    2. Pedro H. C. Sant'Anna & Jun B. Zhao, 2018. "Doubly Robust Difference-in-Differences Estimators," Papers 1812.01723, arXiv.org, revised May 2020.
    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. Benjamin Monnery & Alexandre Chirat, 2023. "Trust in the fight against political corruption: A survey experiment among citizens and experts," EconomiX Working Papers 2023-11, University of Paris Nanterre, EconomiX.
    5. Phillip Heiler, 2022. "Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization," Papers 2209.04329, arXiv.org, revised Jan 2024.
    6. Patrice Bougette & Oliver Budzinski & Frédéric Marty, 2023. "In The Light Of Dynamic Competition: Should We Make Merger Remedies More Flexible?," Working Papers halshs-04230148, HAL.
    7. 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.
    8. Pedro H. C. Sant'Anna & Xiaojun Song, 2020. "Specification tests for generalized propensity scores using double projections," Papers 2003.13803, arXiv.org, revised Apr 2023.
    9. Sung Jae Jun & Sokbae Lee, 2022. "Average Adjusted Association: Efficient Estimation with High Dimensional Confounders," Papers 2205.14048, arXiv.org, revised Apr 2023.
    10. Strittmatter, Anthony, 2019. "What is the Value Added by using Causal Machine Learning Methods in a Welfare Experiment Evaluation?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203499, Verein für Socialpolitik / German Economic Association.
    11. Yang Ning & Sida Peng & Jing Tao, 2020. "Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data," Papers 2009.03151, arXiv.org.
    12. 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.
    13. Jonathan Roth & Pedro H. C. Sant'Anna & Alyssa Bilinski & John Poe, 2022. "What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature," Papers 2201.01194, arXiv.org, revised Jan 2023.
    14. Riccardo Di Francesco, 2022. "Aggregation Trees," CEIS Research Paper 546, Tor Vergata University, CEIS, revised 20 Nov 2023.
    15. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    16. 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.
    17. Michael Zimmert & Michael Lechner, 2019. "Nonparametric estimation of causal heterogeneity under high-dimensional confounding," Papers 1908.08779, arXiv.org.
    18. Daniel Jacob, 2019. "Group Average Treatment Effects for Observational Studies," Papers 1911.02688, arXiv.org, revised Mar 2020.
    19. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    20. 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.
    21. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    22. 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.
    23. 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.
    24. Yumou Qiu & Jing Tao & Xiao‐Hua Zhou, 2021. "Inference of heterogeneous treatment effects using observational data with high‐dimensional covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 1016-1043, November.
    25. 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.
    26. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
    27. Benjamin Monnery & Alexandre Chirat, 2024. "Trust in the Fight Against Political Corruption: A Survey Experiment among Citizens and Experts," Working Papers AFED 24-02, Association Francaise d'Economie du Droit (AFED).
    28. 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.
    29. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
    30. 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.

  9. OGURA Yoshiaki & OKUI Ryo & SAITO Yukiko, 2015. "Network-motivated Lending Decisions," Discussion papers 15057, Research Institute of Economy, Trade and Industry (RIETI).

    Cited by:

    1. Jiangtao FU & OGURA Yoshiaki, 2017. "Product Network Connectivity and Information for Loan Pricing," Discussion papers 17028, Research Institute of Economy, Trade and Industry (RIETI).
    2. Inoue, Hitoshi & Nakashima, Kiyotaka & Takahashi, Koji, 2018. "The Interaction Effect in a Nonlinear Specification of Bank Lending: A Reexamination of ``Unnatural Selection"," MPRA Paper 89087, University Library of Munich, Germany.
    3. Inoue, Hitoshi & Nakashima, Kiyotaka & Takahashi, Koji, 2016. "Comment on Peek and Rosengren (2005) “Unnatural Selection: Perverse Incentives and the Allocation of Credit in Japan”," MPRA Paper 72726, University Library of Munich, Germany.
    4. Gee Hee HONG & ITO Arata & SAITO Yukiko & Thi-Ngoc Anh NGUYEN, 2020. "Structural Changes in Japanese SMEs: Business Dynamism in Aging Society and Inter-Firm Transaction Network," Policy Discussion Papers 20003, Research Institute of Economy, Trade and Industry (RIETI).
    5. Jiangtao Fu & Yoshiaki Ogura, 2017. "Product Network Connectivity and Information for Loan Pricing," Working Papers 1703, Waseda University, Faculty of Political Science and Economics.

  10. Haruo Iwakura & Ryo Okui, 2014. "Asymptotic Efficiency in Factor Models and Dynamic Panel Data Models," KIER Working Papers 887, Kyoto University, Institute of Economic Research.

    Cited by:

    1. Jushan Bai, 2023. "Efficiency of QMLE for dynamic panel data models with interactive effects," Papers 2312.07881, arXiv.org.
    2. Hayakawa, Kazuhiko, 2016. "Improved GMM estimation of panel VAR models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 240-264.
    3. Bai, Jushan & Liao, Yuan, 2017. "Inferences in panel data with interactive effects using large covariance matrices," Journal of Econometrics, Elsevier, vol. 200(1), pages 59-78.

  11. Qingfeng Liu & Ryo Okui & Arihiro Yoshimura, 2013. "Generalized Least Squares Model Averaging," KIER Working Papers 855, Kyoto University, Institute of Economic Research.

    Cited by:

    1. Guozhi Hu & Weihu Cheng & Jie Zeng, 2023. "Optimal Model Averaging for Semiparametric Partially Linear Models with Censored Data," Mathematics, MDPI, vol. 11(3), pages 1-21, February.
    2. Qingfeng Liu & Qingsong Yao & Guoqing Zhao, 2020. "Model averaging estimation for conditional volatility models with an application to stock market volatility forecast," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 841-863, August.
    3. Mohitosh Kejriwal & Xuewen Yu, 2019. "Generalized Forecasr Averaging in Autoregressions with a Near Unit Root," Purdue University Economics Working Papers 1318, Purdue University, Department of Economics.
    4. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    5. Povilas Lastauskas & Julius Stakénas, 2019. "Does It Matter When Labor Market Reforms Are Implemented? The Role of the Monetary Policy Environment," CESifo Working Paper Series 7844, CESifo.
    6. Cong Li & Qi Li & Jeffrey Racine & DAIQIANG ZHANG, 2017. "Optimal Model Averaging Of Varying Coefficient Models," Department of Economics Working Papers 2017-01, McMaster University.
    7. Ryan Greenaway-McGrevy & Kade Sorensen, 2021. "A spatial model averaging approach to measuring house prices," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-32, December.
    8. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    9. Michael Schomaker & Christian Heumann, 2020. "When and when not to use optimal model averaging," Statistical Papers, Springer, vol. 61(5), pages 2221-2240, October.
    10. Cheng, Tzu-Chang F. & Ing, Ching-Kang & Yu, Shu-Hui, 2015. "Toward optimal model averaging in regression models with time series errors," Journal of Econometrics, Elsevier, vol. 189(2), pages 321-334.
    11. Liao, Jun & Wan, Alan T.K. & He, Shuyuan & Zou, Guohua, 2022. "Optimal model averaging for multivariate regression models," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    12. Qingfeng Liu & Andrey L. Vasnev, 2019. "A Combination Method for Averaging OLS and GLS Estimators," Econometrics, MDPI, vol. 7(3), pages 1-12, September.
    13. Yang Feng & Qingfeng Liu, 2020. "Nested Model Averaging on Solution Path for High-dimensional Linear Regression," Papers 2005.08057, arXiv.org.
    14. Greenaway-McGrevy, Ryan, 2022. "Forecast combination for VARs in large N and T panels," International Journal of Forecasting, Elsevier, vol. 38(1), pages 142-164.
    15. Dong, Qingkai & Liu, Binxia & Zhao, Hui, 2023. "Weighted least squares model averaging for accelerated failure time models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).

  12. Yoon-Jin Lee & Ryo Okui & Mototsugu Shintani, 2013. "Asymptotic Inference for Dynamic Panel Estimators of In nite Order Autoregressive Processes," KIER Working Papers 879, Kyoto University, Institute of Economic Research.

    Cited by:

    1. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    2. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
    3. Abhimanyu Gupta & Myung Hwan Seo, 2023. "Robust Inference on Infinite and Growing Dimensional Time‐Series Regression," Econometrica, Econometric Society, vol. 91(4), pages 1333-1361, July.
    4. Greenaway-McGrevy, Ryan, 2022. "Forecast combination for VARs in large N and T panels," International Journal of Forecasting, Elsevier, vol. 38(1), pages 142-164.
    5. Juan Sebastian Cubillos-Rocha & Luis Fernando Melo-Velandia, 2018. "Asymptotically unbiased inference for a panel VAR model with p lags," Borradores de Economia 1059, Banco de la Republica de Colombia.
    6. Lu, Xun & Su, Liangjun, 2020. "Determining individual or time effects in panel data models," Journal of Econometrics, Elsevier, vol. 215(1), pages 60-83.

  13. Yoonseok Lee & Ryo Okui, 2009. "A Specification Test for Instrumental Variables Regression with Many Instruments," Cowles Foundation Discussion Papers 1741, Cowles Foundation for Research in Economics, Yale University.

    Cited by:

    1. Walter Beckert, 2020. "A Note on Specification Testing in Some Structural Regression Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(3), pages 686-695, June.
    2. Norman R. Swanson & John C. Chao & Jerry A. Hausman & Whitney K. Newey & Tiemen Woutersen, 2011. "Testing Overidentifying Restrictions with Many Instruments and Heteroskedasticity," Departmental Working Papers 201118, Rutgers University, Department of Economics.
    3. Walter Beckert, 2019. "A Note on Specification Testing in Some Structural Regression Models," CeMMAP working papers CWP22/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Lee, Yoonseok & Okui, Ryo, 2012. "Hahn–Hausman test as a specification test," Journal of Econometrics, Elsevier, vol. 167(1), pages 133-139.
    5. C. J. Krizan & Adela Luque & Alice Zawacki, 2014. "The Effect Of Employer Health Insurance Offering On The Growth And Survival Of Small Business Prior To The Affordable Care Act," Working Papers 14-22, Center for Economic Studies, U.S. Census Bureau.

  14. Kohtarro Hitomi & Yoshinori Kawasaki & Ryo Okui & Yoshihiko Nishiyama, 2005. "A Consistent Nonparametric Test for Causality," KIER Working Papers 602, Kyoto University, Institute of Economic Research.

    Cited by:

    1. Naoto Kunitomo & Michael McAleer & Yoshihiko Nishiyama, 2010. "Moment Restriction-based Econometric Methods: An Overview," KIER Working Papers 734, Kyoto University, Institute of Economic Research.

Articles

  1. Lumsdaine, Robin L. & Okui, Ryo & Wang, Wendun, 2023. "Estimation of panel group structure models with structural breaks in group memberships and coefficients," Journal of Econometrics, Elsevier, vol. 233(1), pages 45-65.

    Cited by:

    1. Liu, Yanbo & Phillips, Peter C. B. & Yu, Jun, 2022. "A Panel Clustering Approach to Analyzing Bubble Behavior," Economics and Statistics Working Papers 1-2022, Singapore Management University, School of Economics.
    2. Yiren Wang & Peter C. B. Phillips & Liangjun Su, 2023. "Panel Data Models with Time-Varying Latent Group Structures," Cowles Foundation Discussion Papers 2364, Cowles Foundation for Research in Economics, Yale University.
    3. Weijie Cui & Yong Li, 2023. "Bicluster Analysis of Heterogeneous Panel Data via M-Estimation," Mathematics, MDPI, vol. 11(10), pages 1-19, May.

  2. Dzemski, Andreas & Okui, Ryo, 2021. "Convergence rate of estimators of clustered panel models with misclassification," Economics Letters, Elsevier, vol. 203(C).
    See citations under working paper version above.
  3. Okui, Ryo & Wang, Wendun, 2021. "Heterogeneous structural breaks in panel data models," Journal of Econometrics, Elsevier, vol. 220(2), pages 447-473.
    See citations under working paper version above.
  4. Yanchun Jin & Ryo Okui, 2020. "Testing for overconfidence statistically: A moment inequality approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(7), pages 879-892, November.
    See citations under working paper version above.
  5. Feng, Yang & Liu, Qingfeng & Okui, Ryo, 2020. "On the sparsity of Mallows model averaging estimator," Economics Letters, Elsevier, vol. 187(C).

    Cited by:

    1. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    2. Tianming Gao & Vasilii Erokhin, 2020. "Capturing a Complexity of Nutritional, Environmental, and Economic Impacts on Selected Health Parameters in the Russian High North," Sustainability, MDPI, vol. 12(5), pages 1-25, March.
    3. Yang Feng & Qingfeng Liu, 2020. "Nested Model Averaging on Solution Path for High-dimensional Linear Regression," Papers 2005.08057, arXiv.org.
    4. Vasilii Erokhin & Li Diao & Tianming Gao & Jean-Vasile Andrei & Anna Ivolga & Yuhang Zong, 2021. "The Supply of Calories, Proteins, and Fats in Low-Income Countries: A Four-Decade Retrospective Study," IJERPH, MDPI, vol. 18(14), pages 1-30, July.

  6. Ryo Okui & Takahide Yanagi, 2020. "Kernel estimation for panel data with heterogeneous dynamics [Econometric tools for analyzing market outcomes]," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 156-175.
    See citations under working paper version above.
  7. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    See citations under working paper version above.
  8. Lee, Yoon-Jin & Okui, Ryo & Shintani, Mototsugu, 2018. "Asymptotic inference for dynamic panel estimators of infinite order autoregressive processes," Journal of Econometrics, Elsevier, vol. 204(2), pages 147-158.
    See citations under working paper version above.
  9. 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.
    See citations under working paper version above.
  10. Qingfeng Liu & Ryo Okui & Arihiro Yoshimura, 2016. "Generalized Least Squares Model Averaging," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1692-1752, December.
    See citations under working paper version above.
  11. Okui Ryo, 2014. "Asymptotically Unbiased Estimation of Autocovariances and Autocorrelations with Panel Data in the Presence of Individual and Time Effects," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 1-53, July.

    Cited by:

    1. Ivan Fernandez-Val & Martin Weidner, 2013. "Individual and Time Effects in Nonlinear Panel Models with Large N, T," Papers 1311.7065, arXiv.org, revised Dec 2018.
    2. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    3. Andreas Joseph, 2019. "Parametric inference with universal function approximators," Papers 1903.04209, arXiv.org, revised Oct 2020.
    4. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
    5. Ryo Okui, 2017. "Misspecification in Dynamic Panel Data Models and Model-Free Inferences," The Japanese Economic Review, Springer, vol. 68(3), pages 283-304, September.
    6. Haruo Iwakura & Ryo Okui, 2014. "Asymptotic Efficiency in Factor Models and Dynamic Panel Data Models," KIER Working Papers 887, Kyoto University, Institute of Economic Research.
    7. Timothy J. Vogelsang & Jingjing Yang, 2016. "Exactly/Nearly Unbiased Estimation of Autocovariances of a Univariate Time Series With Unknown Mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 723-740, November.

  12. Tanjim Hossain & Ryo Okui, 2013. "The Binarized Scoring Rule," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(3), pages 984-1001.

    Cited by:

    1. Marcela V. Parada‐Contzen, 2019. "The Value of a Statistical Life for Risk‐Averse and Risk‐Seeking Individuals," Risk Analysis, John Wiley & Sons, vol. 39(11), pages 2369-2390, November.
    2. Ahrens, Steffen & Bosch-Rosa, Ciril & Roulund, Rasmus, 2019. "Price Dynamics and Trader Overconfidence," Rationality and Competition Discussion Paper Series 161, CRC TRR 190 Rationality and Competition.
    3. Evans, Alecia & Sesmero, Juan, 2022. "Cooperation in Social Dilemmas with Correlated Noisy Payoffs: Theory and Experimental Evidence," 2021 Annual Meeting, August 1-3, Austin, Texas 322804, Agricultural and Applied Economics Association.
    4. Michael Thaler, 2020. "Gender Differences in Motivated Reasoning," Papers 2012.01538, arXiv.org, revised Jul 2021.
    5. Piotr Evdokimov & Umberto Garfagnini, 2022. "Higher-order learning," Experimental Economics, Springer;Economic Science Association, vol. 25(4), pages 1234-1266, September.
    6. Tilman H. Drerup & Matthias Wibral & Christian Zimpelmann, 2022. "Skewness Expectations and Portfolio Choice," CRC TR 224 Discussion Paper Series crctr224_2022_333, University of Bonn and University of Mannheim, Germany.
    7. Elias Tsakas, 2022. "Belief identification with state-dependent utilities," Papers 2203.10505, arXiv.org, revised Nov 2022.
    8. Deversi, Marvin & Ispano, Alessandro & Schwardmann, Peter, 2021. "Spin Doctors: An Experiment on Vague Disclosure," Rationality and Competition Discussion Paper Series 304, CRC TRR 190 Rationality and Competition.
    9. Bauer, Dominik & Wolff, Irenaeus, 2021. "Biases in Belief Reports," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242458, Verein für Socialpolitik / German Economic Association.
    10. Ahrens, Steffen & Bosch-Rosa, Ciril, 2022. "Motivated beliefs, social preferences, and limited liability in financial decision-making," Discussion Papers 2022/8, Free University Berlin, School of Business & Economics.
    11. Kinnl, Klara & Möller, Jakob & Walter, Anna, 2023. "Borrowed Plumes:," Department of Economics Working Paper Series 345, WU Vienna University of Economics and Business.
    12. Dargnies, Marie-Pierre & Kübler, Dorothea, 2017. "Self-Confidence and Unraveling In Matching Markets," Rationality and Competition Discussion Paper Series 5, CRC TRR 190 Rationality and Competition.
    13. Markus M. Möbius & Muriel Niederle & Paul Niehaus & Tanya S. Rosenblat, 2022. "Managing Self-Confidence: Theory and Experimental Evidence," Management Science, INFORMS, vol. 68(11), pages 7793-7817, November.
    14. Buchanan, Joy A., 2020. "My reference point, not yours," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 297-311.
    15. Harrison, Glenn W. & Martínez-Correa, Jimmy & Swarthout, J. Todd, 2013. "Inducing risk neutral preferences with binary lotteries: A reconsideration," Journal of Economic Behavior & Organization, Elsevier, vol. 94(C), pages 145-159.
    16. Benoît, Jean-Pierre & Dubra, Juan & Romagnoli, Giorgia, 2019. "Belief elicitation when more than money matters," MPRA Paper 95550, University Library of Munich, Germany.
    17. Lisa Bruttel & Muhammed Bulutay & Camille Cornand & Frank Heinemann & Adam Zylbersztejn, 2022. "Measuring strategic-uncertainty attitudes," CEPA Discussion Papers 54, Center for Economic Policy Analysis.
    18. Mohammed Abdellaoui & Han Bleichrodt & Emmanuel Kemel & Olivier L’haridon, 2021. "Measuring Beliefs Under Ambiguity," Post-Print halshs-02886673, HAL.
    19. Duarte Gonc{c}alves & Jonathan Libgober & Jack Willis, 2021. "Learning versus Unlearning: An Experiment on Retractions," Papers 2106.11433, arXiv.org, revised Nov 2022.
    20. Antinyan, Armenak & Corazzini, Luca & D'Agostino, Elena & Pavesi, Filippo, 2023. "Watch your words: An experimental study on communication and the opportunity cost of delegation," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 216-232.
    21. von Gaudecker, Hans-Martin & Drerup, Tilman & Enke, Benjamin, 2015. "Measurement Error in Subjective Expectations and the Empirical Content of Economic Models," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112871, Verein für Socialpolitik / German Economic Association.
    22. Crosetto, Paolo & Filippin, Antonio & Katuščák, Peter & Smith, John, 2020. "Central tendency bias in belief elicitation," Journal of Economic Psychology, Elsevier, vol. 78(C).
    23. Aksoy, Billur & Chadd, Ian & Koh, Boon Han, 2023. "Sexual identity, gender, and anticipated discrimination in prosocial behavior," European Economic Review, Elsevier, vol. 154(C).
    24. Piotr Evdokimov & Umberto Garfagnini, 2023. "Cognitive Ability and Perceived Disagreement in Learning," Rationality and Competition Discussion Paper Series 381, CRC TRR 190 Rationality and Competition.
    25. Wolff, Irenaeus, 2022. "Predicting Voluntary Contributions by `Revealed-Preference Nash-Equilibrium'," VfS Annual Conference 2022 (Basel): Big Data in Economics 264072, Verein für Socialpolitik / German Economic Association.
    26. Billur Aksoy & Ian Chadd & Boon Han Koh, 2022. "(Anticipated) Discrimination against Sexual Minorities in Prosocial Domains," University of East Anglia School of Economics Working Paper Series 2021-08, School of Economics, University of East Anglia, Norwich, UK..
    27. Timothy N. Cason & Tridib Sharma & Radovan Vadovic, 2019. "Corelated beliefs: Predicting outcomes in 2X2 games," Purdue University Economics Working Papers 1321, Purdue University, Department of Economics.
    28. Zhengyang Bao & Andreas Leibbrandt & ple391, 2019. "Thar she resurges: The case of assets that lack positive fundamental value," Monash Economics Working Papers 12-19, Monash University, Department of Economics.
    29. Benjamin Enke & Frederik Schwerter & Florian Zimmermann, 2020. "Associative Memory and Belief Formation," NBER Working Papers 26664, National Bureau of Economic Research, Inc.
    30. Thomas Buser & Leonie Gerhards & Joël J. van der Weele, 2016. "Measuring Responsiveness to Feedback as a Personal Trait," Tinbergen Institute Discussion Papers 16-043/I, Tinbergen Institute.
    31. Kai Barron & Tilman Fries, 2023. "Narrative Persuasion," CESifo Working Paper Series 10206, CESifo.
    32. Barron, Kai, 2019. "Belief updating: Does the 'good-news, bad-news' asymmetry extend to purely financial domains?," Discussion Papers, Research Unit: Economics of Change SP II 2016-309r, WZB Berlin Social Science Center, revised 2019.
    33. Masaki Aoyagi & Guillaume Frechette & Sevgi Yuksel, 2021. "Beliefs in Repeated Games," ISER Discussion Paper 1119rr, Institute of Social and Economic Research, Osaka University, revised May 2022.
    34. Kölle, Felix & Quercia, Simone, 2021. "The influence of empirical and normative expectations on cooperation," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 691-703.
    35. Karl Schlag & James Tremewan & Joel von der Weele, 2014. "A Penny for your Thoughts: A Survey of Methods of Eliciting Beliefs," Vienna Economics Papers vie1401, University of Vienna, Department of Economics.
    36. Marvin Deversi & Alessandro Ispano & Peter Schwardmann, 2018. "Spin Doctors: A Model and an Experimental Investigation of Vague Disclosure," CESifo Working Paper Series 7244, CESifo.
    37. Peter Schwardmann & Egon Tripodi & Joël J. van der Weele, 2019. "Self-Persuasion: Evidence from Field Experiments at Two International Debating Competitions," CESifo Working Paper Series 7946, CESifo.
    38. Ahrash Dianat & Federico Echenique & Leeat Yariv, 2021. "Statistical Discrimination and Affirmative Action in the Lab," Working Papers 2020-46, Princeton University. Economics Department..
    39. Erkal, Nisvan & Gangadharan, Lata & Koh, Boon Han, 2023. "Do women receive less blame than men? Attribution of outcomes in a prosocial setting," Journal of Economic Behavior & Organization, Elsevier, vol. 210(C), pages 441-452.
    40. Wenbo Zou & Xue Xu, 2023. "Ingroup bias in a social learning experiment," Experimental Economics, Springer;Economic Science Association, vol. 26(1), pages 27-54, March.
    41. Debrah Meloso & Salvatore Nunnari & Marco Ottaviani, 2023. "Looking into Crystal Balls: A Laboratory Experiment on Reputational Cheap Talk," Management Science, INFORMS, vol. 69(9), pages 5112-5127, September.
    42. Valeria Burdea & Jonathan Woon, 2021. "Online Belief Elicitation Methods," CESifo Working Paper Series 8823, CESifo.
    43. Aidas Masiliunas, 2016. "Overcoming Coordination Failure in a Critical Mass Game: Strategic Motives and Action Disclosure," AMSE Working Papers 1609, Aix-Marseille School of Economics, France.
    44. Baader, Malte & Gächter, Simon & Lee, Kyeongtae & Sefton, Martin, 2022. "Social Preferences and the Variability of Conditional Cooperation," IZA Discussion Papers 15523, Institute of Labor Economics (IZA).
    45. Nisvan Erkal & Lata Gangadharan & Boon Han Koh, 2022. "By chance or by choice? Biased attribution of others’ outcomes when social preferences matter," Experimental Economics, Springer;Economic Science Association, vol. 25(2), pages 413-443, April.
    46. Lata Gangadharan & Philip J. Grossman & Nina Xue, 2022. "Stepping Stone: Identifying self-image concerns from motivated beliefs: Does it matter how and whom you ask?," Monash Economics Working Papers 2022-05, Monash University, Department of Economics.
    47. Castillo, Marco E. & Cross, Philip J. & Freer, Mikhail, 2019. "Nonparametric utility theory in strategic settings: Revealing preferences and beliefs from proposal–response games," Games and Economic Behavior, Elsevier, vol. 115(C), pages 60-82.
    48. Lina Lozano & Ernesto Reuben, 2022. "Measuring Preferences for Competition," Working Papers 20220078, New York University Abu Dhabi, Department of Social Science, revised Aug 2022.
    49. Markus Eyting & Patrick Schmidt, 2019. "Belief Elicitation with Multiple Point Predictions," Working Papers 1818, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 16 Nov 2020.
    50. Nisvan Erkal & Lata Gangadharan & Boon Han Koh, 2021. "Gender Biases in Performance Evaluation: The Role of Beliefs Versus Outcomes," University of East Anglia School of Economics Working Paper Series 2021-09, School of Economics, University of East Anglia, Norwich, UK..
    51. Elias Tsakas, 2021. "Identification of misreported beliefs," Papers 2112.12975, arXiv.org.
    52. Tsakas, Elias, 2020. "Robust scoring rules," Theoretical Economics, Econometric Society, vol. 15(3), July.
    53. Bauer, Dominik & Wolff, Irenaeus, 2019. "Biases in Beliefs," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203601, Verein für Socialpolitik / German Economic Association.
    54. Hillenbrand, Adrian & Werner, Tobias & Winter, Fabian, 2022. "Willingness to volunteer among remote workers is insensitive to the team size," ZEW Discussion Papers 22-050, ZEW - Leibniz Centre for European Economic Research.
    55. Charlotte Cordes & Jana Friedrichsen & Simeon Schudy, 2023. "Motivated Procrastination," Rationality and Competition Discussion Paper Series 471, CRC TRR 190 Rationality and Competition.
    56. Mitchell Hoffman, 2014. "Training Contracts, Worker Overconfidence, and the Provision of Firm-Sponsored General Training," 2014 Meeting Papers 203, Society for Economic Dynamics.
    57. Greiner, Ben & Grünwald, Philipp & Lindner, Thomas & Lintner, Georg & Wiernsperger, Martin, 2024. "Incentives, Framing, and Reliance on Algorithmic Advice: An Experimental Study," Department for Strategy and Innovation Working Paper Series 01/2024, WU Vienna University of Economics and Business.
    58. Eyting, Markus & Schmidt, Patrick, 2021. "Belief elicitation with multiple point predictions," European Economic Review, Elsevier, vol. 135(C).
    59. Stefan T. Trautmann & Gijs Kuilen, 2015. "Belief Elicitation: A Horse Race among Truth Serums," Economic Journal, Royal Economic Society, vol. 125(589), pages 2116-2135, December.
    60. Li Hao & Daniel Houser, 2012. "Belief elicitation in the presence of naïve respondents: An experimental study," Journal of Risk and Uncertainty, Springer, vol. 44(2), pages 161-180, April.
    61. Erkal, Nisvan & Gangadharan, Lata & Koh, Boon Han, 2020. "Replication: Belief elicitation with quadratic and binarized scoring rules," Journal of Economic Psychology, Elsevier, vol. 81(C).
    62. Zhengyang Bao & Andreas Leibbrandt, 2020. "Tournaments with Safeguards: A Blessing or a Curse for Women," CESifo Working Paper Series 8147, CESifo.
    63. Timo Dimitriadis & Andrew J. Patton & Patrick W. Schmidt, 2019. "Testing Forecast Rationality for Measures of Central Tendency," Papers 1910.12545, arXiv.org, revised Jun 2023.
    64. Felipe R. Durazzo & David Turchick, 2023. "Welfare-improving misreported polls," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 75(2), pages 523-565, February.
    65. Nisvan Erkal & Lata Gangadharan & Boon Han Koh, 2018. "By chance or by choice? Biased attribution of others’ outcomes," Department of Economics - Working Papers Series 2040, The University of Melbourne.
    66. Antonio, Filippin & Marco, Mantovani, 2019. "Risk Aversion and Information Aggregation in Asset Markets," Working Papers 404, University of Milano-Bicocca, Department of Economics, revised Apr 2019.
    67. Drerup, Tilman H., 2019. "Eliciting subjective expectations for bivariate outcomes," Journal of Behavioral and Experimental Finance, Elsevier, vol. 23(C), pages 29-45.
    68. Chen, Yan & He, YingHua, 2021. "Information acquisition and provision in school choice: An experimental study," Journal of Economic Theory, Elsevier, vol. 197(C).
    69. Chen, Binkai & Lin, Wei & Wang, Ao, 2021. "The causal impact of economics education on decision-making: Evidence from a natural experiment in China," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 1124-1143.
    70. Corazzini, Luca & Galavotti, Stefano & Valbonesi, Paola, 2019. "An experimental study on sequential auctions with privately known capacities," Games and Economic Behavior, Elsevier, vol. 117(C), pages 289-315.
    71. Burro, Giovanni & Castagnetti, Alessandro, 2022. "Will I tell you that you are smart (dumb)? Deceiving Others about their IQ or about a Random Draw," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 100(C).
    72. Benjamin Enke & Thomas Graeber, 2019. "Cognitive Uncertainty," NBER Working Papers 26518, National Bureau of Economic Research, Inc.
    73. Kinnl, Klara & Möller, Jakob & Walter, Anna, 2023. "Borrowed Plumes: The Gender Gap in Claiming Credit for Teamwork," Department for Strategy and Innovation Working Paper Series 01/2023, WU Vienna University of Economics and Business.
    74. Dominik Bauer & Irenaeus Wolff, 2018. "Biases in Beliefs: Experimental Evidence," TWI Research Paper Series 109, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    75. Victor Aguirregabiria & Erhao Xie, 2016. "Identification of Biased Beliefs in Games of Incomplete Information Using Experimental Data," Working Papers tecipa-560, University of Toronto, Department of Economics.
    76. Cason, Timothy N. & Sharma, Tridib & Vadovič, Radovan, 2020. "Correlated beliefs: Predicting outcomes in 2 × 2 games," Games and Economic Behavior, Elsevier, vol. 122(C), pages 256-276.
    77. Francesco Bripi & Daniela Grieco, 2023. "Participatory incentives," Experimental Economics, Springer;Economic Science Association, vol. 26(4), pages 813-849, September.
    78. Choi, Jin Hyuk & Han, Kookyoung, 2020. "Optimal contract for outsourcing information acquisition," Economics Letters, Elsevier, vol. 195(C).
    79. Robertson, Matthew J., 2018. "Contests with Ex-Ante Target Setting," CRETA Online Discussion Paper Series 47, Centre for Research in Economic Theory and its Applications CRETA.
    80. Syngjoo Choi & Byung-Yeon Kim & Jungmin Lee & Sokbae Lee, 2021. "Why North Korean Refugees are Reluctant to Compete: The Roles of Cognitive Ability," Papers 2108.08097, arXiv.org, revised Aug 2023.
    81. Alvaro Sandroni & Eran Shmaya, 2013. "Eliciting beliefs by paying in chance," Economic Theory Bulletin, Springer;Society for the Advancement of Economic Theory (SAET), vol. 1(1), pages 33-37, May.
    82. Irenaeus Wolff & Dominik Bauer, 2018. "Elusive Beliefs: Why Uncertainty Leads to Stochastic Choice and Errors," TWI Research Paper Series 111, Thurgauer Wirtschaftsinstitut, Universität Konstanz.
    83. Patrick Schmidt, 2019. "Eliciting ambiguity with mixing bets," Papers 1902.07447, arXiv.org, revised Jul 2022.
    84. Sandro Ambuehl, 2017. "An Offer You Can't Refuse? Testing Undue Inducement," CESifo Working Paper Series 6296, CESifo.
    85. Blume, Andreas & Lai, Ernest K. & Lim, Wooyoung, 2019. "Eliciting private information with noise: The case of randomized response," Games and Economic Behavior, Elsevier, vol. 113(C), pages 356-380.
    86. Olivier L'Haridon & Craig S. Webb & Horst Zank, 2021. "An Effective and Simple Tool for Measuring Loss Aversion," Economics Discussion Paper Series 2107, Economics, The University of Manchester.
    87. Arthur Carvalho & Stanko Dimitrov & Kate Larson, 2018. "On proper scoring rules and cumulative prospect theory," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 343-376, November.
    88. Tsakas, Elias, 2018. "Robust scoring rules," Research Memorandum 023, Maastricht University, Graduate School of Business and Economics (GSBE).
    89. Jan Feld & Edwin Ip & Andreas Leibbrandt & Joseph Vecci, 2022. "Identifying and Overcoming Gender Barriers in Tech: A Field Experiment on Inaccurate Statistical Discrimination," CESifo Working Paper Series 9970, CESifo.
    90. Marco Mantovani & Antonio Filippin, 2024. "When do prediction markets return average beliefs? Experimental evidence," Working Papers 532, University of Milano-Bicocca, Department of Economics.
    91. Jacob K Goeree & Bernardo Garcia-Pola, 2023. "S Equilibrium: A Synthesis of (Behavioral) Game Theory," Papers 2307.06309, arXiv.org.
    92. Dustan, Andrew & Koutout, Kristine & Leo, Greg, 2022. "Second-order beliefs and gender," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 752-781.
    93. Harrison, Glenn W. & Martínez-Correa, Jimmy & Swarthout, J. Todd, 2014. "Eliciting subjective probabilities with binary lotteries," Journal of Economic Behavior & Organization, Elsevier, vol. 101(C), pages 128-140.
    94. Klara Kinnl & Jakob Möller & Anna Walter, 2023. "The Gender Gap in Claiming Credit for Teamwork," Department of Economics Working Papers wuwp345, Vienna University of Economics and Business, Department of Economics.
    95. Grieco, Daniela & Bripi, Francesco, 2022. "Participation of charity beneficiaries," Journal of Economic Behavior & Organization, Elsevier, vol. 199(C), pages 1-17.
    96. Hillenbrand, Adrian & Schmelzer, André, 2017. "Beyond information: Disclosure, distracted attention, and investor behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 16(C), pages 14-21.
    97. Ahrens, Steffen & Bosch-Rosa, Ciril, 2019. "Heads We Both Win, Tails Only You Lose: the Effect of Limited Liability On Risk-Taking in Financial Decision Making," Rationality and Competition Discussion Paper Series 162, CRC TRR 190 Rationality and Competition.
    98. Jens Witkowski & Rupert Freeman & Jennifer Wortman Vaughan & David M. Pennock & Andreas Krause, 2023. "Incentive-Compatible Forecasting Competitions," Management Science, INFORMS, vol. 69(3), pages 1354-1374, March.
    99. Ahrens, Steffen & Bosch-Rosa, Ciril, 2023. "Motivated beliefs, social preferences, and limited liability in financial decision-Making," Journal of Banking & Finance, Elsevier, vol. 154(C).
    100. Ingrid Burfurd & Tom Wilkening, 2022. "Cognitive heterogeneity and complex belief elicitation," Experimental Economics, Springer;Economic Science Association, vol. 25(2), pages 557-592, April.
    101. Samuel Häfner & Curtis R. Taylor, 2022. "On young Turks and yes men: optimal contracting for advice," RAND Journal of Economics, RAND Corporation, vol. 53(1), pages 63-94, March.
    102. Alexander Coutts & Boon Han Koh & Zahra Murad, 2024. "The signals we give: Performance feedback, gender, and competition," Working Papers in Economics & Finance 2024-02, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    103. Demuynck, Thomas, 2013. "A mechanism for eliciting the mean and quantiles of a random variable," Economics Letters, Elsevier, vol. 121(1), pages 121-123.
    104. Charness, Gary & Gneezy, Uri & Rasocha, Vlastimil, 2021. "Experimental methods: Eliciting beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 234-256.
    105. Sofianos, Andis, 2022. "Self-reported & revealed trust: Experimental evidence," Journal of Economic Psychology, Elsevier, vol. 88(C).
    106. David Danz & Lise Vesterlund & Alistair J. Wilson, 2020. "Belief Elicitation: Limiting Truth Telling with Information on Incentives," CESifo Working Paper Series 8048, CESifo.
    107. Rafkin, Charlie & Shreekumar, Advik & Vautrey, Pierre-Luc, 2021. "When guidance changes: Government stances and public beliefs," Journal of Public Economics, Elsevier, vol. 196(C).
    108. Drerup, Tilman & Enke, Benjamin & von Gaudecker, Hans-Martin, 2017. "The precision of subjective data and the explanatory power of economic models," Journal of Econometrics, Elsevier, vol. 200(2), pages 378-389.
    109. Hillenbrand, Adrian & Verrina, Eugenio, 2022. "The asymmetric effect of narratives on prosocial behavior," Games and Economic Behavior, Elsevier, vol. 135(C), pages 241-270.
    110. de Haan, Thomas, 2020. "Eliciting belief distributions using a random two-level partitioning of the state space," Working Papers in Economics 1/20, University of Bergen, Department of Economics.
    111. Kirchkamp, Oliver & Oechssler, Joerg & Sofianos, Andis, 2021. "The Binary Lottery Procedure does not induce risk neutrality in the Holt & Laury and Eckel & Grossman tasks," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 348-369.
    112. Alvaro Sandroni & Eran Shmaya, 2013. "Eliciting Beliefs by Paying in Chance," Discussion Papers 1565, Northwestern University, Center for Mathematical Studies in Economics and Management Science.

  13. Qingfeng Liu & Ryo Okui, 2013. "Heteroscedasticity‐robust C(p) model averaging," Econometrics Journal, Royal Economic Society, vol. 16(3), pages 463-472, October.

    Cited by:

    1. Yin, Anwen, 2015. "Forecasting and model averaging with structural breaks," ISU General Staff Papers 201501010800005727, Iowa State University, Department of Economics.
    2. Steven Lehrer & Tian Xie, 2020. "The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success," Working Paper 1449, Economics Department, Queen's University.
    3. Toru Kitagawa & Chris Muris, 2015. "Model averaging in semiparametric estimation of treatment effects," CeMMAP working papers CWP46/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Qingfeng Liu & Qingsong Yao & Guoqing Zhao, 2020. "Model averaging estimation for conditional volatility models with an application to stock market volatility forecast," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 841-863, August.
    5. Zhang, Xinyu & Liu, Chu-An, 2023. "Model averaging prediction by K-fold cross-validation," Journal of Econometrics, Elsevier, vol. 235(1), pages 280-301.
    6. Anatolyev, Stanislav, 2021. "Mallows criterion for heteroskedastic linear regressions with many regressors," Economics Letters, Elsevier, vol. 203(C).
    7. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    8. Zhao, Shangwei & Zhou, Jianhong & Li, Hongjun, 2016. "Model averaging with high-dimensional dependent data," Economics Letters, Elsevier, vol. 148(C), pages 68-71.
    9. Shangwei Zhao & Aman Ullah & Xinyu Zhang, 2018. "A Class of Model Averaging Estimators," Working Paper series 18-11, Rimini Centre for Economic Analysis.
    10. Povilas Lastauskas & Julius Stakénas, 2019. "Does It Matter When Labor Market Reforms Are Implemented? The Role of the Monetary Policy Environment," CESifo Working Paper Series 7844, CESifo.
    11. Yongmiao Hong & Tae-Hwy Lee & Yuying Sun & Shouyang Wang & Xinyu Zhang, 2017. "Time-varying Model Averaging," Working Papers 202001, University of California at Riverside, Department of Economics.
    12. Cong Li & Qi Li & Jeffrey Racine & DAIQIANG ZHANG, 2017. "Optimal Model Averaging Of Varying Coefficient Models," Department of Economics Working Papers 2017-01, McMaster University.
    13. Zhang, Xinyu & Ullah, Aman & Zhao, Shangwei, 2016. "On the dominance of Mallows model averaging estimator over ordinary least squares estimator," Economics Letters, Elsevier, vol. 142(C), pages 69-73.
    14. Xiaomeng Zhang & Wendun Wang & Xinyu Zhang, 2022. "Asymptotic Properties of the Synthetic Control Method," Papers 2211.12095, arXiv.org.
    15. Tadao Hoshino, 2018. "Semiparametric Spatial Autoregressive Models With Endogenous Regressors: With an Application to Crime Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 160-172, January.
    16. Zhao, Shangwei & Zhang, Xinyu & Gao, Yichen, 2016. "Model averaging with averaging covariance matrix," Economics Letters, Elsevier, vol. 145(C), pages 214-217.
    17. Qingfeng Liu & Ryo Okui & Arihiro Yoshimura, 2013. "Generalized Least Squares Model Averaging," KIER Working Papers 855, Kyoto University, Institute of Economic Research.
    18. Yuting Wei & Qihua Wang & Wei Liu, 2021. "Model averaging for linear models with responses missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 535-553, June.
    19. Xinyu Zhang & Dalei Yu & Guohua Zou & Hua Liang, 2016. "Optimal Model Averaging Estimation for Generalized Linear Models and Generalized Linear Mixed-Effects Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1775-1790, October.
    20. Yin-Wong Cheung & Wenhao Wang, 2019. "A Jackknife Model Averaging Analysis of RMB Misalignment Estimates," CESifo Working Paper Series 7840, CESifo.
    21. Ren, Yu & Liang, Xuanxuan & Wang, Qin, 2021. "Short-term exchange rate forecasting: A panel combination approach," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    22. Cheng, Tzu-Chang F. & Ing, Ching-Kang & Yu, Shu-Hui, 2015. "Toward optimal model averaging in regression models with time series errors," Journal of Econometrics, Elsevier, vol. 189(2), pages 321-334.
    23. Qiu, Yue & Wang, Zongrun & Xie, Tian & Zhang, Xinyu, 2021. "Forecasting Bitcoin realized volatility by exploiting measurement error under model uncertainty," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 179-201.
    24. Lu, Xun & Su, Liangjun, 2015. "Jackknife model averaging for quantile regressions," Journal of Econometrics, Elsevier, vol. 188(1), pages 40-58.
    25. Tian Xie, 2019. "Forecast Bitcoin Volatility with Least Squares Model Averaging," Econometrics, MDPI, vol. 7(3), pages 1-20, September.
    26. Zhao, Shangwei & Ullah, Aman & Zhang, Xinyu, 2018. "A class of model averaging estimators," Economics Letters, Elsevier, vol. 162(C), pages 101-106.
    27. Yuan, Chaoxia & Fang, Fang & Ni, Lyu, 2022. "Mallows model averaging with effective model size in fragmentary data prediction," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    28. Yan Gao & Xinyu Zhang & Shouyang Wang & Terence Tai-leung Chong & Guohua Zou, 2019. "Frequentist model averaging for threshold models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 275-306, April.
    29. Shou-Yung Yin & Chu-An Liu & Chang-Ching Lin, 2016. "Focused Information Criterion and Model Averaging for Large Panels with a Multifactor Error Structure," IEAS Working Paper : academic research 16-A016, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    30. Wei, Yuting & Wang, Qihua, 2021. "Cross-validation-based model averaging in linear models with response missing at random," Statistics & Probability Letters, Elsevier, vol. 171(C).
    31. Zhang, Xinyu & Yu, Jihai, 2018. "Spatial weights matrix selection and model averaging for spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 203(1), pages 1-18.
    32. Chu-An Liu & Biing-Shen Kuo & Wen-Jen Tsay, 2017. "Autoregressive Spectral Averaging Estimator," IEAS Working Paper : academic research 17-A013, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    33. Zhang, Xinyu, 2015. "Consistency of model averaging estimators," Economics Letters, Elsevier, vol. 130(C), pages 120-123.
    34. Shangwei Zhao & Jun Liao & Dalei Yu, 2020. "Model averaging estimator in ridge regression and its large sample properties," Statistical Papers, Springer, vol. 61(4), pages 1719-1739, August.
    35. Aman Ullah & Xinyu Zhang, 2015. "Grouped Model Averaging for Finite Sample Size," Working Papers 201501, University of California at Riverside, Department of Economics.
    36. Liao, Jun & Zou, Guohua & Gao, Yan & Zhang, Xinyu, 2021. "Model averaging prediction for time series models with a diverging number of parameters," Journal of Econometrics, Elsevier, vol. 223(1), pages 190-221.
    37. Liao, Jun & Zou, Guohua, 2020. "Corrected Mallows criterion for model averaging," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    38. Yan, Xiaodong & Wang, Hongni & Wang, Wei & Xie, Jinhan & Ren, Yanyan & Wang, Xinjun, 2021. "Optimal model averaging forecasting in high-dimensional survival analysis," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1147-1155.
    39. Liao, Jun & Zong, Xianpeng & Zhang, Xinyu & Zou, Guohua, 2019. "Model averaging based on leave-subject-out cross-validation for vector autoregressions," Journal of Econometrics, Elsevier, vol. 209(1), pages 35-60.
    40. Fang, Fang & Li, Jialiang & Xia, Xiaochao, 2022. "Semiparametric model averaging prediction for dichotomous response," Journal of Econometrics, Elsevier, vol. 229(2), pages 219-245.
    41. Peng, Jingfu & Yang, Yuhong, 2022. "On improvability of model selection by model averaging," Journal of Econometrics, Elsevier, vol. 229(2), pages 246-262.
    42. Qingfeng Liu & Andrey L. Vasnev, 2019. "A Combination Method for Averaging OLS and GLS Estimators," Econometrics, MDPI, vol. 7(3), pages 1-12, September.
    43. Jie Zeng & Weihu Cheng & Guozhi Hu, 2023. "Optimal Model Averaging Estimation for the Varying-Coefficient Partially Linear Models with Missing Responses," Mathematics, MDPI, vol. 11(8), pages 1-21, April.
    44. Yang Feng & Qingfeng Liu, 2020. "Nested Model Averaging on Solution Path for High-dimensional Linear Regression," Papers 2005.08057, arXiv.org.
    45. Feng, Yang & Liu, Qingfeng & Okui, Ryo, 2020. "On the sparsity of Mallows model averaging estimator," Economics Letters, Elsevier, vol. 187(C).
    46. Greenaway-McGrevy, Ryan, 2022. "Forecast combination for VARs in large N and T panels," International Journal of Forecasting, Elsevier, vol. 38(1), pages 142-164.
    47. Xie, Tian, 2017. "Heteroscedasticity-robust model screening: A useful toolkit for model averaging in big data analytics," Economics Letters, Elsevier, vol. 151(C), pages 119-122.
    48. Alena Skolkova, 2023. "Model Averaging with Ridge Regularization," CERGE-EI Working Papers wp758, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

  14. Lee, Yoonseok & Okui, Ryo, 2012. "Hahn–Hausman test as a specification test," Journal of Econometrics, Elsevier, vol. 167(1), pages 133-139.

    Cited by:

    1. Tae-Hwan Kim & Christophe Muller, 2017. "A Robust Test of Exogeneity Based on Quantile Regressions," Working Papers halshs-01508067, HAL.
    2. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "A specification test for the strength of instrumental variables," Papers 2302.14396, arXiv.org.
    3. Mehmet Caner & Xu Han & Yoonseok Lee, 2018. "Adaptive Elastic Net GMM Estimation With Many Invalid Moment Conditions: Simultaneous Model and Moment Selection," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 24-46, January.
    4. Kolesár, Michal, 2018. "Minimum distance approach to inference with many instruments," Journal of Econometrics, Elsevier, vol. 204(1), pages 86-100.
    5. Tae-Hwan Kim & Christophe Muller, 2013. "A Test for Endogeneity in Conditional Quantiles," AMSE Working Papers 1342, Aix-Marseille School of Economics, France, revised Aug 2013.
    6. Firmin DOKO TCHATOKA & Jean-Marie DUFOUR, 2016. "Exogeneity Tests, Incomplete Models, Weak Identification and Non-Gaussian Distributions : Invariance and Finite-Sample Distributional Theory," Cahiers de recherche 14-2016, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    7. Byunghoon Kang, 2018. "Higher Order Approximation of IV Estimators with Invalid Instruments," Working Papers 257105320, Lancaster University Management School, Economics Department.
    8. Qingliang Fan & Zijian Guo & Ziwei Mei, 2022. "A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates," Papers 2205.00171, arXiv.org, revised Mar 2023.
    9. Jamal Bouoiyour & Amal Miftah & Refk Selmi, 2019. "The economic contribution of immigration on Europe: Fresh evidence from a “hybrid” quantile regression model," Working Papers hal-02346700, HAL.
    10. Yoonseok Lee & Yu Zhou, 2015. "Averaged Instrumental Variables Estimators," Center for Policy Research Working Papers 180, Center for Policy Research, Maxwell School, Syracuse University.
    11. Guo, Zijian & Kang, Hyunseung & Cai, T. Tony & Small, Dylan S., 2018. "Testing endogeneity with high dimensional covariates," Journal of Econometrics, Elsevier, vol. 207(1), pages 175-187.
    12. Marine Carrasco & Mohamed Doukali, 2022. "Testing overidentifying restrictions with many instruments and heteroscedasticity using regularised jackknife IV [Specification testing in models with many instruments]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 71-97.
    13. Kohtaro Hitomi & Masamune Iwasawa & Yoshihiko Nishiyama, 2018. "Rate Optimal Specification Test When the Number of Instruments is Large," KIER Working Papers 986, Kyoto University, Institute of Economic Research.
    14. Christis Katsouris, 2023. "Optimal Estimation Methodologies for Panel Data Regression Models," Papers 2311.03471, arXiv.org, revised Nov 2023.
    15. Zhenhong Huang & Chen Wang & Jianfeng Yao, 2023. "Assessing the strength of many instruments with the first-stage F and Cragg-Donald statistics," Papers 2302.14423, arXiv.org.

  15. Okui, Ryo, 2011. "Asymptotically unbiased estimation of autocovariances and autocorrelations for panel data with incidental trends," Economics Letters, Elsevier, vol. 112(1), pages 49-52, July.

    Cited by:

    1. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    2. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
    3. Yifan Li & Yao Rao, 2021. "A simple nearly unbiased estimator of cross‐covariances," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(2), pages 240-266, March.
    4. Ryo Okui, 2017. "Misspecification in Dynamic Panel Data Models and Model-Free Inferences," The Japanese Economic Review, Springer, vol. 68(3), pages 283-304, September.
    5. Okui Ryo, 2014. "Asymptotically Unbiased Estimation of Autocovariances and Autocorrelations with Panel Data in the Presence of Individual and Time Effects," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 1-53, July.
    6. Haruo Iwakura & Ryo Okui, 2014. "Asymptotic Efficiency in Factor Models and Dynamic Panel Data Models," KIER Working Papers 887, Kyoto University, Institute of Economic Research.
    7. Timothy J. Vogelsang & Jingjing Yang, 2016. "Exactly/Nearly Unbiased Estimation of Autocovariances of a Univariate Time Series With Unknown Mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 723-740, November.

  16. Okui, Ryo, 2011. "Instrumental variable estimation in the presence of many moment conditions," Journal of Econometrics, Elsevier, vol. 165(1), pages 70-86.

    Cited by:

    1. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-Dimensional Econometrics and Regularized GMM," Papers 1806.01888, arXiv.org, revised Jun 2018.
    2. Dennis Lim & Wenjie Wang & Yichong Zhang, 2022. "A Conditional Linear Combination Test with Many Weak Instruments," Papers 2207.11137, arXiv.org, revised Apr 2023.
    3. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    4. Carrasco, Marine & Tchuente, Guy, 2015. "Regularized LIML for many instruments," Journal of Econometrics, Elsevier, vol. 186(2), pages 427-442.
    5. Hansen, Christian & Kozbur, Damian, 2014. "Instrumental variables estimation with many weak instruments using regularized JIVE," Journal of Econometrics, Elsevier, vol. 182(2), pages 290-308.
    6. Liu, Chu-An & Tao, Jing, 2016. "Model selection and model averaging in nonparametric instrumental variables models," MPRA Paper 69492, University Library of Munich, Germany.
    7. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    8. Qingliang Fan & Yaqian Wu, 2020. "Endogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments," Papers 2006.14998, arXiv.org.
    9. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2015. "Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments," Papers 1501.03185, arXiv.org.
    10. Victor Chernozhukov & Christian Hansen & Martin Spindler, 2016. "Valid post-selection and post-regularization inference: An elementary, general approach," CeMMAP working papers CWP36/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Naoto Kunitomo & Michael McAleer & Yoshihiko Nishiyama, 2010. "Moment Restriction-based Econometric Methods: An Overview," KIER Working Papers 734, Kyoto University, Institute of Economic Research.
    12. Carrasco, Marine, 2012. "A regularization approach to the many instruments problem," Journal of Econometrics, Elsevier, vol. 170(2), pages 383-398.
    13. Byunghoon Kang, 2018. "Higher Order Approximation of IV Estimators with Invalid Instruments," Working Papers 257105320, Lancaster University Management School, Economics Department.
    14. Qingliang Fan & Zijian Guo & Ziwei Mei, 2022. "A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates," Papers 2205.00171, arXiv.org, revised Mar 2023.
    15. Ziyu Wang & Yuhao Zhou & Jun Zhu, 2022. "Fast Instrument Learning with Faster Rates," Papers 2205.10772, arXiv.org, revised Oct 2022.
    16. Kuersteiner, Guido M., 2012. "Kernel-weighted GMM estimators for linear time series models," Journal of Econometrics, Elsevier, vol. 170(2), pages 399-421.
    17. Yoonseok Lee & Yu Zhou, 2015. "Averaged Instrumental Variables Estimators," Center for Policy Research Working Papers 180, Center for Policy Research, Maxwell School, Syracuse University.
    18. Denis Heng-Yan Leung & Dylan S. Small & Jing Qin & Min Zhu, 2013. "Shrinkage Empirical Likelihood Estimator in Longitudinal Analysis with Time-Dependent Covariates—Application to Modeling the Health of Filipino Children," Biometrics, The International Biometric Society, vol. 69(3), pages 624-632, September.
    19. Anna Mikusheva & Liyang Sun, 2023. "Weak Identification with Many Instruments," Papers 2308.09535, arXiv.org, revised Jan 2024.
    20. Thomas Wiemann, 2023. "Optimal Categorical Instrumental Variables," Papers 2311.17021, arXiv.org.
    21. Hujer Reinhard & Rodrigues Paulo J. M. & Wolf Katja, 2008. "Dynamic Panel Data Models with Spatial Correlation," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(5-6), pages 612-629, October.
    22. Guy Tchuente, 2016. "Estimation of social interaction models using regularization," Studies in Economics 1607, School of Economics, University of Kent.
    23. Eric Gautier & Alexandre Tsybakov, 2011. "High-Dimensional Instrumental Variables Regression and Confidence Sets," Working Papers 2011-13, Center for Research in Economics and Statistics.
    24. Guy Tchuente, 2019. "Weak Identification and Estimation of Social Interaction Models," Papers 1902.06143, arXiv.org.
    25. Kazuhiko Hayakawa, 2006. "Efficient GMM Estimation of Dynamic Panel Data Models Where Large Heterogeneity May Be Present," Hi-Stat Discussion Paper Series d05-130, Institute of Economic Research, Hitotsubashi University.
    26. Martins, Luis F. & Gabriel, Vasco J., 2014. "Linear instrumental variables model averaging estimation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 709-724.
    27. Kazuhiko Hayakawa, 2008. "On the Effect of Nonstationary Initial Conditions in Dynamic Panel Data Models," Hi-Stat Discussion Paper Series d07-245, Institute of Economic Research, Hitotsubashi University.
    28. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

  17. Guido Kuersteiner & Ryo Okui, 2010. "Constructing Optimal Instruments by First-Stage Prediction Averaging," Econometrica, Econometric Society, vol. 78(2), pages 697-718, March.

    Cited by:

    1. Dennis Lim & Wenjie Wang & Yichong Zhang, 2022. "A Conditional Linear Combination Test with Many Weak Instruments," Papers 2207.11137, arXiv.org, revised Apr 2023.
    2. Michel Berthélemy & Petyo Bonev & Damien Dussaux & Magnus Söderberg, 2018. "Methods for strengthening a weak instrument in the case of a persistent treatment," Post-Print hal-01829558, HAL.
    3. Lewbel, Arthur & Choi, Jin Young & Zhou, Zhuzhu, 2023. "Over-identified Doubly Robust identification and estimation," Journal of Econometrics, Elsevier, vol. 235(1), pages 25-42.
    4. Cheng, Xu & Liao, Zhipeng, 2015. "Select the valid and relevant moments: An information-based LASSO for GMM with many moments," Journal of Econometrics, Elsevier, vol. 186(2), pages 443-464.
    5. Liu, Chu-An & Tao, Jing, 2016. "Model selection and model averaging in nonparametric instrumental variables models," MPRA Paper 69492, University Library of Munich, Germany.
    6. Qingliang Fan & Yaqian Wu, 2020. "Endogenous Treatment Effect Estimation with some Invalid and Irrelevant Instruments," Papers 2006.14998, arXiv.org.
    7. Zhang, Xinyu & Ullah, Aman & Zhao, Shangwei, 2016. "On the dominance of Mallows model averaging estimator over ordinary least squares estimator," Economics Letters, Elsevier, vol. 142(C), pages 69-73.
    8. Seojeong Lee & Youngki Shin, 2021. "Complete subset averaging with many instruments," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 290-314.
    9. Lee, Ji Hyung & Shin, Youngki, 2023. "Complete Subset Averaging For Quantile Regressions," Econometric Theory, Cambridge University Press, vol. 39(1), pages 146-188, February.
    10. Jeffrey S. Racine & Qi Li & Li Zheng, 2018. "Optimal Model Averaging of Mixed-Data Kernel-Weighted Spline Regressions," Department of Economics Working Papers 2018-10, McMaster University.
    11. Aman Ullah & Huansha Wang, 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging," Econometrics, MDPI, vol. 1(2), pages 1-23, September.
    12. Seojeong Lee & Siha Lee & Julius Owusu & Youngki Shin, 2022. "csa2sls: A complete subset approach for many instruments using Stata," Papers 2207.01533, arXiv.org, revised Apr 2023.
    13. Caner, Mehmet & Fan, Qingliang, 2015. "Hybrid generalized empirical likelihood estimators: Instrument selection with adaptive lasso," Journal of Econometrics, Elsevier, vol. 187(1), pages 256-274.
    14. Francis J. DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 14-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Dec 2014.
    15. Zhang, Xinyu & Wan, Alan T.K. & Zou, Guohua, 2013. "Model averaging by jackknife criterion in models with dependent data," Journal of Econometrics, Elsevier, vol. 174(2), pages 82-94.
    16. Byunghoon Kang, 2018. "Higher Order Approximation of IV Estimators with Invalid Instruments," Working Papers 257105320, Lancaster University Management School, Economics Department.
    17. Wang, Wenjie & Kaffo, Maximilien, 2016. "Bootstrap inference for instrumental variable models with many weak instruments," Journal of Econometrics, Elsevier, vol. 192(1), pages 231-268.
    18. Lu, Xun & Su, Liangjun, 2015. "Jackknife model averaging for quantile regressions," Journal of Econometrics, Elsevier, vol. 188(1), pages 40-58.
    19. Kuersteiner, Guido M., 2012. "Kernel-weighted GMM estimators for linear time series models," Journal of Econometrics, Elsevier, vol. 170(2), pages 399-421.
    20. Yoonseok Lee & Yu Zhou, 2015. "Averaged Instrumental Variables Estimators," Center for Policy Research Working Papers 180, Center for Policy Research, Maxwell School, Syracuse University.
    21. Tomohiro Ando & Naoya Sueishi, 2019. "On the Convergence Rate of the SCAD-Penalized Empirical Likelihood Estimator," Econometrics, MDPI, vol. 7(1), pages 1-14, March.
    22. DiTraglia, Francis J., 2016. "Using invalid instruments on purpose: Focused moment selection and averaging for GMM," Journal of Econometrics, Elsevier, vol. 195(2), pages 187-208.
    23. Aman Ullah & Xinyu Zhang, 2015. "Grouped Model Averaging for Finite Sample Size," Working Papers 201501, University of California at Riverside, Department of Economics.
    24. Chirok Han & Hyoungjong Kim, 2023. "Dynamic panel GMM estimators with improved finite sample properties using parametric restrictions for dimension reduction," Empirical Economics, Springer, vol. 64(6), pages 2589-2610, June.
    25. Moral-Benito, Enrique, 2010. "Model averaging in economics," MPRA Paper 26047, University Library of Munich, Germany.
    26. Xiaohong Chen & David Jacho-Chávez & Oliver Linton, 2012. "Averaging of moment condition estimators," CeMMAP working papers CWP26/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    27. Yang Feng & Qingfeng Liu, 2020. "Nested Model Averaging on Solution Path for High-dimensional Linear Regression," Papers 2005.08057, arXiv.org.
    28. Seojeong Lee & Youngki Shin, 2018. "Optimal Estimation with Complete Subsets of Instruments," Department of Economics Working Papers 2018-15, McMaster University.
    29. Francis DiTraglia, 2011. "Using Invalid Instruments on Purpose: Focused Moment Selection and Averaging for GMM, Second Version," PIER Working Paper Archive 15-027, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 10 Aug 2015.
    30. Enrique Moral-Benito, 2015. "Model Averaging In Economics: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 46-75, February.
    31. Martins, Luis F. & Gabriel, Vasco J., 2014. "Linear instrumental variables model averaging estimation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 709-724.
    32. Xu Cheng & Zhipeng Liao, 2012. "Select the Valid and Relevant Moments: A One-Step Procedure for GMM with Many Moments," PIER Working Paper Archive 12-045, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    33. Tsay, Wen-Jen, 2021. "Estimating cartel damages with model averaging approaches," International Review of Law and Economics, Elsevier, vol. 68(C).

  18. Okui, Ryo, 2010. "Asymptotically Unbiased Estimation Of Autocovariances And Autocorrelations With Long Panel Data," Econometric Theory, Cambridge University Press, vol. 26(5), pages 1263-1304, October.

    Cited by:

    1. Okui, Ryo, 2009. "Testing serial correlation in fixed effects regression models based on asymptotically unbiased autocorrelation estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2897-2909.
    2. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    3. Lee, Yoon-Jin & Okui, Ryo & Shintani, Mototsugu, 2018. "Asymptotic inference for dynamic panel estimators of infinite order autoregressive processes," Journal of Econometrics, Elsevier, vol. 204(2), pages 147-158.
    4. Ziwei Mei & Liugang Sheng & Zhentao Shi, 2023. "Nickell Bias in Panel Local Projection: Financial Crises Are Worse Than You Think," Papers 2302.13455, arXiv.org, revised Oct 2023.
    5. Yifan Li & Yao Rao, 2021. "A simple nearly unbiased estimator of cross‐covariances," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(2), pages 240-266, March.
    6. Yang, Jingjing & Vogelsang, Timothy J., 2018. "Finite sample performance of a long run variance estimator based on exactly (almost) unbiased autocovariance estimators," Economics Letters, Elsevier, vol. 165(C), pages 21-27.
    7. Ryo Okui, 2017. "Misspecification in Dynamic Panel Data Models and Model-Free Inferences," The Japanese Economic Review, Springer, vol. 68(3), pages 283-304, September.
    8. Jungmo Yoon & Antonio F. Galvao, 2020. "Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects," Quantitative Economics, Econometric Society, vol. 11(2), pages 579-608, May.
    9. Okui Ryo, 2014. "Asymptotically Unbiased Estimation of Autocovariances and Autocorrelations with Panel Data in the Presence of Individual and Time Effects," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 1-53, July.
    10. Haruo Iwakura & Ryo Okui, 2014. "Asymptotic Efficiency in Factor Models and Dynamic Panel Data Models," KIER Working Papers 887, Kyoto University, Institute of Economic Research.
    11. Okui, Ryo, 2011. "Asymptotically unbiased estimation of autocovariances and autocorrelations for panel data with incidental trends," Economics Letters, Elsevier, vol. 112(1), pages 49-52, July.
    12. Timothy J. Vogelsang & Jingjing Yang, 2016. "Exactly/Nearly Unbiased Estimation of Autocovariances of a Univariate Time Series With Unknown Mean," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 723-740, November.

  19. Okui, Ryo, 2009. "Testing serial correlation in fixed effects regression models based on asymptotically unbiased autocorrelation estimators," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2897-2909.

    Cited by:

    1. Jochmans, Koen, 2020. "A Portmanteau Test For Correlation In Short Panels," Econometric Theory, Cambridge University Press, vol. 36(6), pages 1159-1166, December.
    2. Su, Liangjun & Lu, Xun, 2013. "Nonparametric dynamic panel data models: Kernel estimation and specification testing," Journal of Econometrics, Elsevier, vol. 176(2), pages 112-133.
    3. Okui Ryo, 2014. "Asymptotically Unbiased Estimation of Autocovariances and Autocorrelations with Panel Data in the Presence of Individual and Time Effects," Journal of Time Series Econometrics, De Gruyter, vol. 6(2), pages 1-53, July.
    4. Du, Zaichao, 2014. "Testing for serial independence of panel errors," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 248-261.

  20. Okui, Ryo, 2009. "The optimal choice of moments in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 151(1), pages 1-16, July.

    Cited by:

    1. von Gaessler, Anne Edle & Ziesemer, Thomas, 2016. "Optimal education in times of ageing: The dependency ratio in the Uzawa–Lucas growth model," The Journal of the Economics of Ageing, Elsevier, vol. 7(C), pages 125-142.
    2. Thomas Ziesemer, 2016. "The Impact of Development Aid on Education and Health: Survey and New Evidence for Low‐income Countries from Dynamic Models," Journal of International Development, John Wiley & Sons, Ltd., vol. 28(8), pages 1358-1380, November.
    3. Salima Bouayad-Agha & Nadine Turpin & Lionel Védrine, 2010. "Fostering the potential endogenous development of European regions: a spatial dynamic panel data analysis of the Cohesion Policy on regional convergence over the period 1980-2005," TEPP Working Paper 2010-17, TEPP.
    4. Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
    5. Augustin Kwasi Fosu & Yoseph Yilma Getachew & Thomas Ziesemer, 2012. "Optimal public investment, growth and consumption: evidence from African countries," Global Development Institute Working Paper Series 16412, GDI, The University of Manchester.
    6. Ziesemer, T., 2014. "Country Terms of Trade 1960-2012: Trends, unit roots, over-differencing, endogeneity, time dummies, and heterogeneity," MERIT Working Papers 2014-027, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    7. Eva Barteková & Thomas H. W Ziesemer, 2019. "The impact of electricity prices on foreign direct investment: evidence from the European Union," Applied Economics, Taylor & Francis Journals, vol. 51(11), pages 1183-1198, March.
    8. Mustafa Salamh & Liqun Wang, 2021. "Second-Order Least Squares Method for Dynamic Panel Data Models with Application," JRFM, MDPI, vol. 14(9), pages 1-19, September.
    9. Meijer, Erik & Spierdijk, Laura & Wansbeek, Tom, 2017. "Consistent estimation of linear panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 200(2), pages 169-180.
    10. Magnac, Thierry & Pistolesi, Nicolas & Roux, Sébastien, 2013. "Post schooling human capital investments and the life cycle variance of earnings," IDEI Working Papers 765, Institut d'Économie Industrielle (IDEI), Toulouse.
    11. Miyazaki, Tomomi, 2013. "Fiscal Policy and Regional Business Cycle Fluctuations in Japan," Discussion Paper Series 583, Institute of Economic Research, Hitotsubashi University.
    12. Guowei Cui & Vasilis Sarafidis & Takashi Yamagata, 2020. "IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude," Monash Econometrics and Business Statistics Working Papers 11/20, Monash University, Department of Econometrics and Business Statistics.
    13. Ruize Cai & Kyung Hwan Yun & Minho Kim, 2022. "Financing Constraints and Corporate Value in China: The Moderating Role of Multinationality and Ownership Type," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    14. Jan Kiviet & Milan Pleus & Rutger Poldermans, 2017. "Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models," Econometrics, MDPI, vol. 5(1), pages 1-54, March.
    15. Tomomi Miyazaki & Haruo Kondoh, 2017. "Local Public Investment and Regional Business Cycle Fluctuations in Japan," Economics Bulletin, AccessEcon, vol. 37(1), pages 402-410.
    16. El-Shagi, Makram & Fidrmuc, Jarko & Yamarik, Steven, 2020. "Inequality and credit growth in Russian regions," Economic Modelling, Elsevier, vol. 91(C), pages 550-558.
    17. Manuel Arellano, 2003. "Modelling Optimal Instrumental Variables for Dynamic Panel Data Models," Working Papers wp2003_0310, CEMFI.
    18. Lee, Nayoung & Moon, Hyungsik Roger & Weidner, Martin, 2012. "Analysis of interactive fixed effects dynamic linear panel regression with measurement error," Economics Letters, Elsevier, vol. 117(1), pages 239-242.
    19. Guowei Cui & Vasilis Sarafidis & Takashi Yamagata, 2023. "IV estimation of spatial dynamic panels with interactive effects: large sample theory and an application on bank attitude towards risk," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 124-146.
    20. Bohdan Yu. Kyshakevych & Anatoliy K. Prykarpatsky & Denis Blackmore & Ivan P. Tverdokhlib, 2010. "Statistically Optimal Strategy Analysis of a Competing Portfolio Market with a Polyvariant Profit Function," Papers 1005.2661, arXiv.org.
    21. Makram El-Shagi & Steven Yamarik, 2018. "State-Level Capital and Investment: Refinements and Update," CFDS Discussion Paper Series 2018/1, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
    22. Hujer Reinhard & Rodrigues Paulo J. M. & Wolf Katja, 2008. "Dynamic Panel Data Models with Spatial Correlation," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(5-6), pages 612-629, October.
    23. Augustin K. Fosu & Thomas H. W. Ziesemer & Yoseph Y. Getachew, 2014. "Optimal Public Investment, Growth, and Consumption: Fresh Evidence from African Countries," Working Papers 471, Economic Research Southern Africa.
    24. Mariangela Bonasia, 2012. "Twenty years of internal migration in Italy. Answers from some economic and non-economic determinants," Discussion Papers 6_2012, CRISEI, University of Naples "Parthenope", Italy.
    25. Tomomi Miyazaki, 2016. "Interactions between Regional Public and Private Investment: Evidence from Japanese Prefectures," Discussion Papers 1608, Graduate School of Economics, Kobe University.
    26. Ivan Savin & Peter Winker, 2012. "Heuristic Optimization Methods for Dynamic Panel Data Model Selection: Application on the Russian Innovative Performance," Computational Economics, Springer;Society for Computational Economics, vol. 39(4), pages 337-363, April.
    27. Desjardins, Denise & Dionne, Georges & Koné, N’Golo, 2022. "Reinsurance demand and liquidity creation: A search for bicausality," Journal of Empirical Finance, Elsevier, vol. 66(C), pages 137-154.
    28. Magnac, Thierry & Roux, Sébastien, 2021. "Heterogeneity and wage inequalities over the life cycle," European Economic Review, Elsevier, vol. 134(C).
    29. Laura Spierdijk, 2023. "Assessing the consistency of the fixed-effects estimator: a regression-based Wald test," Empirical Economics, Springer, vol. 64(4), pages 1599-1630, April.
    30. Shiyun Cao & Yonghui Zhang & Qiankun Zhou, 2021. "2SLS and IV Estimation of Dynamic Panel Models with Heterogeneous Trend," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1408-1431, December.
    31. Jan F. Kiviet & Milan Pleus & Rutger Poldermans, 2014. "Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models," UvA-Econometrics Working Papers 14-09, Universiteit van Amsterdam, Dept. of Econometrics.
    32. Kazuhiko Hayakawa, 2008. "On the Effect of Nonstationary Initial Conditions in Dynamic Panel Data Models," Hi-Stat Discussion Paper Series d07-245, Institute of Economic Research, Hitotsubashi University.

  21. Okui, Ryo, 2008. "Panel AR(1) estimators under misspecification," Economics Letters, Elsevier, vol. 101(3), pages 210-213, December.

    Cited by:

    1. Okui, Ryo & Yanagi, Takahide, 2019. "Panel data analysis with heterogeneous dynamics," Journal of Econometrics, Elsevier, vol. 212(2), pages 451-475.
    2. Ryo Okui & Takahide Yanagi, 2018. "Kernel Estimation for Panel Data with Heterogeneous Dynamics," Papers 1802.08825, arXiv.org, revised May 2019.
    3. Lee, Yoonseok, 2012. "Bias in dynamic panel models under time series misspecification," Journal of Econometrics, Elsevier, vol. 169(1), pages 54-60.
    4. Ryo Okui, 2017. "Misspecification in Dynamic Panel Data Models and Model-Free Inferences," The Japanese Economic Review, Springer, vol. 68(3), pages 283-304, September.

  22. Hitomi, Kohtaro & Nishiyama, Yoshihiko & Okui, Ryo, 2008. "A Puzzling Phenomenon In Semiparametric Estimation Problems With Infinite-Dimensional Nuisance Parameters," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1717-1728, December.

    Cited by:

    1. Kyoo il Kim, 2019. "Efficiency of Average Treatment Effect Estimation When the True Propensity Is Parametric," Econometrics, MDPI, vol. 7(2), pages 1-13, May.
    2. Bryan S. Graham & Cristine Campos de Xavier Pinto, 2018. "Semiparametrically efficient estimation of the average linear regression function," Papers 1810.12511, arXiv.org.
    3. Otávio Bartalotti, 2013. "GMM Efficiency and IPW Estimation for Nonsmooth Functions," Working Papers 1301, Tulane University, Department of Economics.
    4. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    5. Fan Li & Ashley L. Buchanan & Stephen R. Cole, 2022. "Generalizing trial evidence to target populations in non‐nested designs: Applications to AIDS clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 669-697, June.
    6. Yihui He & Fang Han, 2023. "On propensity score matching with a diverging number of matches," Papers 2310.14142, arXiv.org, revised Nov 2023.
    7. Han, Chirok & Kim, Beomsoo, 2011. "A GMM interpretation of the paradox in the inverse probability weighting estimation of the average treatment effect on the treated," Economics Letters, Elsevier, vol. 110(2), pages 163-165, February.
    8. Hristache, Marian & Patilea, Valentin, 2021. "Equivalent models for observables under the assumption of missing at random," Econometrics and Statistics, Elsevier, vol. 20(C), pages 153-165.
    9. Kevin Burke & Valentin Patilea, 2021. "A likelihood-based approach for cure regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 693-712, September.
    10. Prokhorov, Artem & Schmidt, Peter, 2009. "GMM redundancy results for general missing data problems," Journal of Econometrics, Elsevier, vol. 151(1), pages 47-55, July.

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