References
- Abadie, A., Athey, S., Imbens, G., and Wooldridge, J. (2017), “When Should You Adjust Standard Errors for Clustering?” The Quarterly Journal of Economics, 138, 1–35. DOI: 10.1093/qje/qjac038.
- Angrist, J. D., and Pischke, J.-S. (2008), Mostly Harmless Econometrics, Princeton, NJ: Princeton University Press.
- Bell, R., and McCaffrey, D. E. (2002), “Bias Reduction in Standard Errors for Linear Regression with Multi-Stage Samples,” Survey Methodology, 28, 169–181.
- Bertrand, M., Duflo, E., and Sendhil, M. (2004), “How Much Should We Trust Differences-in-Differences Estimates?” Quarterly Journal of Economics, 119, 249–275. DOI: 10.1162/003355304772839588.
- Bester, C. A., Conley, T. G., and Hansen, C. B. (2011), “Inference with Dependent Data Using Cluster Covariance Estimators,” Journal of Econometrics, 165, 137–151. DOI: 10.1016/j.jeconom.2011.01.007.
- Cai, Y., Canay, I. A., Kim, D., and Shaikh, A. M. (2021), “A User’s Guide to Approximate Randomization Tests with a Small Number of Clusters,” working paper.
- Cameron, A. C., Gelbach, J. B., and Miller, D. L. (2008), “Bootstrap-Based Improvements for Inference with Clustered Errors,” The Review of Economics and Statistics, 90, 414–427. DOI: 10.1162/rest.90.3.414.
- Canay, I. A., Romano, J. P., and Shaikh, A. M. (2017), “Randomization Tests under an Approximate Symmetry Assumption,” Econometrica, 85, 1013–1030. DOI: 10.3982/ECTA13081.
- Carter, A. V., Schnepel, K. T., and Steigerwald, D. G. (2017), “Asymptotic Behavior of At-test Robust to Cluster Heterogeneity,” Review of Economics and Statistics, 99, 698–709. DOI: 10.1162/REST_a_00639.
- Davidson, R., and Flachaire, E. (2008), “The Wild Bootstrap, Tamed at Last,” Journal of Econometrics, 146, 162–169. DOI: 10.1016/j.jeconom.2008.08.003.
- Djogbenou, A. A., MacKinnon, J. G., and Nielsen, M. Ø. (2019), “Asymptotic Theory and Wild Bootstrap Inference with Clustered Errors,” Journal of Econometrics, 212, 393–412. DOI: 10.1016/j.jeconom.2019.04.035.
- Gneezy, U., List, J. A., Livingston, J. A., Qin, X., Sadoff, S., and Xu, Y. (2019), “Measuring Success in Education: The Role of Effort on the Test Itself,” American Economic Review: Insights, 1, 291–308. DOI: 10.1257/aeri.20180633.
- Hu, A., and Spamann, H. (2020), “Inference with Cluster Imbalance: The Case of State Corporate Laws,” AEA Law & Corporate Governance working paper.
- Ibragimov, R., and Müller, U. K. (2010), “t-Statistic based Correlation and Heterogeneity Robust Inference,” Journal of Business & Economic Statistics, 28, 453–468. DOI: 10.1198/jbes.2009.08046.
- —- (2016), “Inference with Few Heterogeneous Clusters,” The Review of Economics and Statistics, 98, 83–96.
- Imbens, G. W., and Kolesár, M. (2016), “Robust Standard Errors in Small Samples: Some Practical Advice,” The Review of Economics and Statistics, 98, 701–712. DOI: 10.1162/REST_a_00552.
- Liang, K.-Y., and Zeger, S. L. (1986), “Longitudinal Data Analysis for Generalized Linear Models, Biometrika, 73, 13–22. DOI: 10.1093/biomet/73.1.13.
- MacKinnon, J. G., Nielsen, M. A., and Webb, M. D. (2020), “Testing for the Appropriate Level of Clustering in Linear Regression Models,” Queens Economics Department working paper no. 1428.
- Young, A. (2019), “Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results,” The Quarterly Journal of Economics, 134, 557–598. DOI: 10.1093/qje/qjy029.