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Inference with dependent data using cluster covariance estimators

Listed author(s):
  • Bester, C. Alan
  • Conley, Timothy G.
  • Hansen, Christian B.

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, 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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File URL: http://www.sciencedirect.com/science/article/pii/S0304407611000431
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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 165 (2011)
Issue (Month): 2 ()
Pages: 137-151

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Handle: RePEc:eee:econom:v:165:y:2011:i:2:p:137-151
DOI: 10.1016/j.jeconom.2011.01.007
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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