Wild Bootstrap Inference for Wildly Different Cluster Sizes
AbstractThe cluster robust variance estimator (CRVE) relies on the number of clusters being large. The precise meaning of 'large' is ambiguous, but a shorthand 'rule of 42' has emerged in the literature. We show that this rule depends crucially on the assumption of equal-sized clusters. Monte Carlo evidence suggests that rejection frequencies can be much higher when a dataset has 50 clusters proportional to the populations of the US states than when it has 50 equal-sized clusters. In contrast, using a cluster wild bootstrap procedure generally works well in both cases. We also show that, when the test regressor is a dummy variable, as in a difference-in-differences framework, both conventional and bootstrap tests perform badly when the proportion of clusters treated is very small or very large. However, bootstrap tests perform very well when that is not the case. A third set of simulations studies placebo laws and finds that bootstrap tests usually perform very much better than conventional ones.
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Bibliographic InfoPaper provided by Queen's University, Department of Economics in its series Working Papers with number 1314.
Length: 22 pages
Date of creation: Nov 2013
Date of revision:
CRVE; grouped data; clustered data; panel data; cluster wild bootstrap; difference in differences; placebo laws;
Other versions of this item:
- James G. MacKinnon, 2013. "Wild Bootstrap Inference for Wildly Different Cluster Sizes," Working Papers 2013-17, Department of Economics, University of Calgary.
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Longitudinal Data; Spatial Time Series
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- Bester, C. Alan & Conley, Timothy G. & Hansen, Christian B., 2011. "Inference with dependent data using cluster covariance estimators," Journal of Econometrics, Elsevier, vol. 165(2), pages 137-151.
- Russell Davidson & Emmanuel Flachaire, 2000.
"The Wild Bootstrap, Tamed at Last,"
Econometric Society World Congress 2000 Contributed Papers
1413, Econometric Society.
- Emmanuel Flachaire, 2001. "The Wild Bootstrap, Tamed at Last," STICERD - Distributional Analysis Research Programme Papers 58, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Davidson, R. & Flachaire, E., 1999. "The Wild Bootstrap, Tamed at Last," G.R.E.Q.A.M. 99a32, Universite Aix-Marseille III.
- Russell Davidson & Emmanuel Flachaire, 2001. "The Wild Bootstrap, Tamed at Last," Working Papers 1000, Queen's University, Department of Economics.
- Ibragimov, Rustam & MÃ¼ller, Ulrich K., 2010. "t-Statistic Based Correlation and Heterogeneity Robust Inference," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(4), pages 453-468.
- Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
- Stephen G. Donald & Kevin Lang, 2007. "Inference with Difference-in-Differences and Other Panel Data," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 221-233, May.
- Kloek, T, 1981. "OLS Estimation in a Model Where a Microvariable Is Explained by Aggregates and Contemporaneous Disturbances Are Equicorrelated," Econometrica, Econometric Society, vol. 49(1), pages 205-07, January.
- Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of Californiaâ€™s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
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