References
- Bertrand M, Duflo E, Mullainathan S. How much should we trust differences-in-differences estimates? The Quarterly journal of economics, 2004; 119(1): 249–275. DOI: 10.1162/003355304772839588
- Cameron AC, Gelbach JB, Miller DL. Bootstrap-based improvements for inference with clustered errors. The Review of Economics and Statistics, 2008; 90(3): 414–427. DOI: 10.1162/rest.90.3.414
- Chambers JM.
Linear models . In Chambers JM, Hastie TJ (Eds.), Statistical Models in S, chapter 4. Wadsworth Brooks/Cole; 1992. - Djogbenou AA, MacKinnon JG, Nielsen M∅. Asymptotic theory and wild bootstrap inference with clustered errors. Journal of Econometrics, 2019; 212(2): 393–412. DOI: 10.1016/j.jeconom.2019.04.035
- Eicker F. Limit theorems for regressions with unequal and dependent errors. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967; 1: 59–82.
- Elgie R, Bucur C, Dolez B, Laurent A. Proximity, candidates, and presidential power: How directly elected presidents shape the legislative party system. Political Research Quarterly, 2014; 67(3): 467–477. DOI: 10.1177/1065912914530514
- Esarey J, Menger A. Practical and effective approaches to dealing with clustered data. Political Science Research and Methods, 2018; 1–19. DOI: 10.1017/psrm.2017.42
- Greene WH.
Econometric analysis . Upper Saddle River, NJ: Pearson Prentice Hall; 2012. - Harden JJ. A bootstrap method for conducting statistical inference with clustered data. State Politics & Policy Quarterly, 2011; 11(2): 223–246. DOI: 10.1177/1532440011406233
- Hayes AF, Cai L. Using heteroskedasticity-consistent standard error estimators in ols regression: An introduction and software implementation. Behavior research methods, 2007; 39(4): 709–722. DOI: 10.3758/BF03192961
- Imbens GW, Kolesar M. Robust standard errors in small samples: Some practical advice. Review of Economics and Statistics, 2016; 98(4): 701–712. DOI: 10.1162/REST_a_00552
- Jackson J. Corrected standard errors with clustered data. Political Analysis, 2020; 28(3): 318–339. DOI: 10.1017/pan.2019.38
- Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika, 1986; 73(1): 13–22. DOI: 10.1093/biomet/73.1.13
- MacKinnon JG. How cluster-robust inference is changing applied econometrics. Canadian Journal of Economics, 2019; 52(3): 851–881. DOI: 10.1111/caje.12388
- MacKinnon JG, Webb MD. Wild bootstrap inference for wildly different cluster sizes. Journal of Applied Econometrics, 2017; 32(2): 233–254. DOI: 10.1002/jae.2508
- Pustejovsky J. clubSandwich: Cluster-Robust (Sandwich) Variance Estimators with Small-Sample Corrections. R package version 0.5.3; 2021.
- Pustejovsky JE, Tipton E. Small-sample methods for cluster-robust variance estimation and hypothesis testing in fixed effects models. Journal of Business & Economic Statistics, 2018; 36(4): 672–683. DOI: 10.1080/07350015.2016.1247004
- R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.
- Roodman D, Nielsen, M∅, MacKinnon JG, Webb MD. Fast and wild: Bootstrap inference in stata using boottest. The Stata Journal, 2019; 19(1): 4–60. DOI: 10.1177/1536867X19830877
- White HL. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 1980; 48(4): 817–838. DOI: 10.2307/1912934
- Wilkinson GN, Rogers CE. Symbolic descriptions of factorial models for analysis of variance. Applied Statistics, 1973; 22: 392–9. DOI: 10.2307/2346786
- Zeileis A. Econometric computing with hc and hac covariance matrix estimators; 2004. DOI: 10.18637/jss.v011.i10