Asymptotic F Test in a GMM Framework with Cross Sectional Dependence

Min Seong Kim and Yixiao Sun ()

No 32, Working Papers from Toronto Metropolitan University, Department of Economics

Abstract: The paper develops an asymptotically valid F test that is robust to spatial autocorrelation in a GMM framework. The test is based on the class of series covariance matrix estimators and ?fixed-smoothing asymptotics. The fi?xed-smoothing asymptotics and F approximation are established under mild sufficient conditions for a central limit theorem. These conditions can accommodate a wide range of spatial processes. This is in contrast with the standard arguments, which often impose very restrictive assumptions so that a functional central limit theorem holds. The proposed F test is very easy to implement, as critical values are from a standard F distribution. To a great extent, the asymptotic F test achieves triple robustness: it is asymptotically valid regardless of the spatial autocorrelation, the sampling region, and the limiting behavior of the smoothing parameter. Simulation shows that the F test is more accurate in size than the conventional chi-square tests, and it has the same size accuracy and power property as nonstandard tests that require computationally intensive simulation or bootstrap.

Keywords: F distribution; Fixed-smoothing asymptotics; Heteroskedasticity and Autocorrelation Robust; Robust Standard Error; Series Method; Spatial Analysis; Spatial Autocorrelation. (search for similar items in EconPapers)
JEL-codes: C12 C14 C18 C31 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2012-06
New Economics Papers: this item is included in nep-ecm and nep-ure
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Asymptotic F-Test in a GMM Framework with Cross-Sectional Dependence (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:rye:wpaper:wp032

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