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Inference with Few Heterogeneous Clusters

Citations

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Cited by:

  1. MacKinnon, James G. & Webb, Matthew D., 2020. "Randomization inference for difference-in-differences with few treated clusters," Journal of Econometrics, Elsevier, vol. 218(2), pages 435-450.
  2. Abduraimova, Kumushoy, 2022. "Contagion and tail risk in complex financial networks," Journal of Banking & Finance, Elsevier, vol. 143(C).
  3. Timothy Conley & Silvia Gonçalves & Christian Hansen, 2018. "Inference with Dependent Data in Accounting and Finance Applications," Journal of Accounting Research, Wiley Blackwell, vol. 56(4), pages 1139-1203, September.
  4. Kojevnikov, Denis & Song, Kyungchul, 2023. "Some impossibility results for inference with cluster dependence with large clusters," Other publications TiSEM 80b8e4ed-54bc-4a34-883f-f, Tilburg University, School of Economics and Management.
  5. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
  6. Qu, Ritong & Timmermann, Allan & Zhu, Yinchu, 2024. "Comparing forecasting performance with panel data," International Journal of Forecasting, Elsevier, vol. 40(3), pages 918-941.
  7. Alberto Abadie & Susan Athey & Guido W Imbens & Jeffrey M Wooldridge, 2023. "When Should You Adjust Standard Errors for Clustering?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(1), pages 1-35.
  8. Chen, Zhimin & Ibragimov, Rustam, 2019. "One country, two systems? The heavy-tailedness of Chinese A- and H- share markets," Emerging Markets Review, Elsevier, vol. 38(C), pages 115-141.
  9. Hagemann, Andreas, 2019. "Placebo inference on treatment effects when the number of clusters is small," Journal of Econometrics, Elsevier, vol. 213(1), pages 190-209.
  10. Bruno Ferman, 2023. "Inference in difference‐in‐differences: How much should we trust in independent clusters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 358-369, April.
  11. Tom Boot & Gianmaria Niccodemi & Tom Wansbeek, 2023. "Unbiased estimation of the OLS covariance matrix when the errors are clustered," Empirical Economics, Springer, vol. 64(6), pages 2511-2533, June.
  12. Wang, Wenjie & Zhang, Yichong, 2024. "Wild bootstrap inference for instrumental variables regressions with weak and few clusters," Journal of Econometrics, Elsevier, vol. 241(1).
  13. Michael P. Leung, 2022. "Dependence‐robust inference using resampled statistics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 270-285, March.
  14. Rustam Ibragimov & Jihyun Kim & Anton Skrobotov, 2020. "New robust inference for predictive regressions," Papers 2006.01191, arXiv.org, revised Mar 2023.
  15. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
  16. Ivan A. Canay & Andres Santos & Azeem M. Shaikh, 2021. "The Wild Bootstrap with a “Small” Number of “Large” Clusters," The Review of Economics and Statistics, MIT Press, vol. 103(2), pages 346-363, May.
  17. Cai Yong & Canay Ivan A. & Kim Deborah & Shaikh Azeem M., 2023. "On the Implementation of Approximate Randomization Tests in Linear Models with a Small Number of Clusters," Journal of Econometric Methods, De Gruyter, vol. 12(1), pages 85-103, January.
  18. Pötscher, Benedikt M. & Preinerstorfer, David, 2021. "Valid Heteroskedasticity Robust Testing," MPRA Paper 117855, University Library of Munich, Germany, revised Jul 2023.
  19. Riccardo D'Adamo, 2018. "Cluster-Robust Standard Errors for Linear Regression Models with Many Controls," Papers 1806.07314, arXiv.org, revised Apr 2019.
  20. Bobonis, Gustavo J. & Stabile, Mark & Tovar, Leonardo, 2020. "Military training exercises, pollution, and their consequences for health," Journal of Health Economics, Elsevier, vol. 73(C).
  21. Ronald Klingebiel & John Joseph & Valerie Machoba, 2022. "Sequencing innovation rollout: Learning opportunity versus entry speed," Strategic Management Journal, Wiley Blackwell, vol. 43(9), pages 1763-1792, September.
  22. Walter Distaso & Rustam Ibragimov & Alexander Semenov & Anton Skrobotov, 2020. "COVID-19: Tail Risk and Predictive Regressions," Papers 2009.02486, arXiv.org, revised Oct 2021.
  23. Kojevnikov, Denis & Song, Kyungchul, 2023. "Some impossibility results for inference with cluster dependence with large clusters," Journal of Econometrics, Elsevier, vol. 237(2).
  24. Jorge González Chapela & Sergi Jiménez-Martín & Judit Vall Castello, 2023. "Education and internal migration: evidence from a child labor reform in Spain," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 14(2), pages 143-164, June.
  25. Andreas Hagemann, 2020. "Inference with a single treated cluster," Papers 2010.04076, arXiv.org.
  26. Dianat, Ahrash & Echenique, Federico & Yariv, Leeat, 2022. "Statistical discrimination and affirmative action in the lab," Games and Economic Behavior, Elsevier, vol. 132(C), pages 41-58.
  27. Christoph Engel & Keren Weinshall, 2020. "Manna from Heaven for Judges: Judges’ Reaction to a Quasi‐Random Reduction in Caseload," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(4), pages 722-751, December.
  28. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
  29. Timmermann, Allan & Zhu, Yinchu, 2019. "Comparing Forecasting Performance with Panel Data," CEPR Discussion Papers 13746, C.E.P.R. Discussion Papers.
  30. Gyongyosi, Gyozo & Verner, Emil, 2018. "Financial Crisis, Creditor-Debtor Conflict, and Political Extremism," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181587, Verein für Socialpolitik / German Economic Association.
  31. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Testing for the appropriate level of clustering in linear regression models," Journal of Econometrics, Elsevier, vol. 235(2), pages 2027-2056.
  32. Jungbin Hwang, 2017. "Simple and Trustworthy Cluster-Robust GMM Inference," Working papers 2017-19, University of Connecticut, Department of Economics, revised Aug 2020.
  33. Wang, Wenjie, 2021. "Wild Bootstrap for Instrumental Variables Regression with Weak Instruments and Few Clusters," MPRA Paper 106227, University Library of Munich, Germany.
  34. Alexis Akira Toda & Yulong Wang, 2021. "Efficient minimum distance estimation of Pareto exponent from top income shares," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(2), pages 228-243, March.
  35. Andreas Hagemann, 2019. "Permutation inference with a finite number of heterogeneous clusters," Papers 1907.01049, arXiv.org, revised Feb 2023.
  36. Yong Cai, 2021. "Panel Data with Unknown Clusters," Papers 2106.05503, arXiv.org, revised Jan 2022.
  37. Hwang, Jungbin, 2021. "Simple and trustworthy cluster-robust GMM inference," Journal of Econometrics, Elsevier, vol. 222(2), pages 993-1023.
  38. Michael P. Leung, 2023. "Network Cluster‐Robust Inference," Econometrica, Econometric Society, vol. 91(2), pages 641-667, March.
  39. Rustam Ibragimov & Paul Kattuman & Anton Skrobotov, 2021. "Robust Inference on Income Inequality: $t$-Statistic Based Approaches," Papers 2105.05335, arXiv.org, revised Nov 2021.
  40. Yong Cai, 2021. "A Modified Randomization Test for the Level of Clustering," Papers 2105.01008, arXiv.org, revised Jan 2022.
  41. Brown, Donald & Ibragimov, Rustam, 2019. "Sign tests for dependent observations," Econometrics and Statistics, Elsevier, vol. 10(C), pages 1-8.
  42. Andreas Hagemann, 2023. "Inference on quantile processes with a finite number of clusters," Papers 2301.04687, arXiv.org, revised Jun 2023.
  43. Huang Zibin & Ibragimov Rustam, 2022. "Equity returns and sentiment," Dependence Modeling, De Gruyter, vol. 10(1), pages 159-176, January.
  44. Győző Gyöngyösi & Emil Verner, 2022. "Financial Crisis, Creditor‐Debtor Conflict, and Populism," Journal of Finance, American Finance Association, vol. 77(4), pages 2471-2523, August.
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