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Bootstrap-Based Improvements for Inference with Clustered Errors

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Author Info
A. Colin Cameron
Jonah B. Gelbach
Douglas L. Miller

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Abstract

Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (5-30) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo and Mullainathan (2004). Rejection rates of ten percent using standard methods can be reduced to the nominal size of five percent using our methods.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0344.

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Date of creation: Sep 2007
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Handle: RePEc:nbr:nberte:0344

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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  2. Horowitz, Joel L., 2001. "The Bootstrap," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 52, pages 3159-3228 Elsevier. [Downloadable!] (restricted)
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  9. James G. MacKinnon, 2007. "Bootstrap Hypothesis Testing," Working Papers 1127, Queen's University, Department of Economics. [Downloadable!]
  10. Vikström, Johan, 2009. "Cluster sample inference using sensitivity analysis: the case with few groups," Working Paper Series 2009:15, IFAU - Institute for Labour Market Policy Evaluation. [Downloadable!]
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  15. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2007. "Bootstrap-Based Improvements for Inference with Clustered Errors," NBER Technical Working Papers 0344, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
    Other versions:
  16. Matias Cattaneo & Sebastian Galiani & Paul Gertler & Sebastian Martinez & Rocio Titiunik, 2008. "Housing, Health and Happiness," Working Papers 0074, CEDLAS, Universidad Nacional de La Plata. [Downloadable!]
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  18. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2006. "Robust Inference with Multi-way Clustering," NBER Technical Working Papers 0327, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  21. Pearson, Matthew & Schipper, Burkhard C, 2009. "Menstrual cycle and competitive bidding," MPRA Paper 16784, University Library of Munich, Germany. [Downloadable!]
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