Asymptotic F Tests under Possibly Weak Identification

Julián Martínez-Iriarte, Yixiao Sun and Xuexin Wang

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

Abstract: This paper develops asymptotic F tests robust to weak identification and temporal dependence. The test statistics are modified versions of the S statistic of Stock and Wright (2000) and the K statistic of Kleibergen (2005), both of which are based on the continuous updating generalized method of moments. In the former case, the modification involves only a multiplicative degree-of-freedom adjustment. In the latter case, the modification involves an additional multiplicative adjustment that uses a J statistic for testing overidentification. By adopting fixed-smoothing asymptotics, we show that both the modified S statistic and the modified K statistic are asymptotically F-distributed. The asymptotic F theory accounts for the estimation errors in the underlying heteroskedasticity and autocorrelation robust variance estimators, which the asymptotic chi-squared theory ignores. Monte Carlo simulations show that the F approximations are much more accurate than the corresponding chi-squared approximations in finite samples.

Keywords: Social and Behavioral Sciences; Heteroskedasticity and autocorrelation robust variance; continuous updating GMM; F distribution; fixed-smoothing asymptotics; weak identification. (search for similar items in EconPapers)
Date: 2019-03-12
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Asymptotic F tests under possibly weak identification (2020)
Working Paper: Asymptotic F Tests under Possibly Weak Identification (2019)
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