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Asymptotic F Tests under Possibly Weak Identification

Julian Martinez-Iriarte (), Yixiao Sun () and Xuexin Wang

No 2019-03-12, Working Papers from Wang Yanan Institute for Studies in Economics (WISE), Xiamen University

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: Heteroskedasticity and autocorrelation robust variance; continuous updating GMM; F distribution; fixed-smoothing asymptotics; weak identification (search for similar items in EconPapers)
JEL-codes: C12 C14 C32 C36 (search for similar items in EconPapers)
Date: 2019-03-12
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: Asymptotic F tests under possibly weak identification (2020) Downloads
Working Paper: Asymptotic F Tests under Possibly Weak Identification (2019) Downloads
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