Inference with dependent data using cluster covariance estimators |
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Authors: | C. Alan Bester Timothy G. Conley Christian B. Hansen |
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Affiliation: | aUniversity of Chicago Booth School of Business, United States;bUniversity of Western Ontario, Canada |
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Abstract: | This paper presents an inference approach for dependent data in time series, spatial, and panel data applications. The method involves constructing t and Wald statistics using a cluster covariance matrix estimator (CCE). We use an approximation that takes the number of clusters/groups as fixed and the number of observations per group to be large. The resulting limiting distributions of the t and Wald statistics are standard t and F distributions where the number of groups plays the role of sample size. Using a small number of groups is analogous to ‘fixed-b’ asymptotics of [Kiefer and Vogelsang, 2002] and [Kiefer and Vogelsang, 2005] (KV) for heteroskedasticity and autocorrelation consistent inference. We provide simulation evidence that demonstrates that the procedure substantially outperforms conventional inference procedures. |
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Keywords: | JEL classification: C12 C21 C22 C23 |
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