Asymptotic F and t Tests in an Efficient GMM Setting

Jungbin Hwang and Yixiao Sun

University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego

Abstract: This paper considers two-step efficient GMM estimation and inference where the weighting matrix and asymptotic variance matrix are based on the series long run variance estimator. We propose a simple and easy-to-implement modification to the trinity of test statistics in the two-step efficient GMM setting and show that the modified test statistics are all asymptotically F distributed under the so-called fixed-smoothing asymptotics. The modification is multiplicative and involves the J statistic for testing over-identifying restrictions. This leads to convenient asymptotic F tests that use standard F critical values. Simulation shows that, in terms of both size and power, the asymptotic F tests perform as well as the nonstandard tests proposed recently by Sun (2014b) in finite samples. But the F tests are more appealing as the critical values are readily available from standard statistical tables. Compared to the conventional chi-square tests, the F tests are as powerful, but are much more accurate in size.

Keywords: Social and Behavioral Sciences; Efficient GMM; F distribution; F test; Fixed-smoothing Asymptotics; Heteroskedasticity and Autocorrelation Robust; Two-step GMM (search for similar items in EconPapers)
Date: 2015-08-18
New Economics Papers: this item is included in nep-ecm and nep-ger
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Citations: View citations in EconPapers (2)

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Journal Article: Asymptotic F and t tests in an efficient GMM setting (2017)
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