Simple and Trustworthy Cluster-Robust GMM Inference

Jungbin Hwang

No 2017-19, Working papers from University of Connecticut, Department of Economics

Abstract: This paper develops a new asymptotic theory for GMM estimation and inference in the presence of clustered dependence. The key feature of our alternative asymptotics is that the number of clusters G is regarded as fixed as the sample size increases. Under the fixed-G asymptotics, we show that the Wald and t tests in two-step GMM are asymptotically pivotal only if we recenter the estimated moment process in the clustered covariance estimator (CCE). Also, the J statistic, the trinity of two-step GMM statistics (QLR, LM, and Wald), and the t statistic can be modified to have an asymptotic standard F distribution or t distribution. We suggest a finite-sample variance correction to further improve the accuracy of the F and t approximations. The proposed tests are very appealing to practitioners because the test statistics are simple modifications of conventional GMM test statistics, and critical values are readily available from F and t tables. No further simulations or resampling methods are needed. A Monte Carlo study shows that our proposed tests are more accurate than the conventional large-G asymptotic inferences.

Keywords: Two-step GMM; Heteroskedasticity and Autocorrelation Robust; Clustered Dependence; t distribution; F distribution (search for similar items in EconPapers)
JEL-codes: C12 C21 C23 C31 (search for similar items in EconPapers)
Pages: 78 pages
Date: 2017-08, Revised 2020-08
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (5)

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