Robust Inference with Multi-way Clustering
Abstract
In this paper we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit and GMM. This variance estimator enables cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state-year effects example of Bertrand et al. (2004) to two dimensions; and by application to studies in the empirical literature where two-way clustering is present.Download Info
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Paper provided by University of California, Davis, Department of Economics in its series Working Papers with number 98.Length: 43
Date of creation: 01 May 2009
Date of revision:
Handle: RePEc:cda:wpaper:09-8
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Related research
Keywords: cluster-robust standard errors; two-way clustering; multi-way clustering.;Other versions of this item:
- Cameron, A. Colin & Gelbach, Jonah B. & Miller, Douglas L., 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 238-249.
- 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.
- Miller, Douglas & Cameron, A. Colin & Gelbach, Jonah, 2009. "Robust Inference with Multi-way Clustering," Working Papers 09-9, University of California at Davis, Department of Economics.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: 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
References
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