Template-Type: ReDIF-Paper 1.0
Author-Name: Min Seong Kim
Author-X-Name-First: Min Seong
Author-X-Name-Last: Kim
Author-Email: minseong.kim@economics.ryerson.ca 
Author-Workplace-Name: Department of Economics, Ryerson University, Toronto, Canada
Author-Name: Yixiao Sun
Author-X-Name-First: Yixiao
Author-X-Name-Last: Sun
Author-Email: yisun@ucsd.edu
Author-Workplace-Name: Department of Economics, UC San Diego
Title: Asymptotic F Test in a GMM Framework with Cross Sectional Dependence
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.
Classification-JEL: C12, C14, C18, C31.
Keywords: F distribution, Fixed-smoothing asymptotics, Heteroskedasticity and Autocorrelation Robust, Robust Standard Error, Series Method, Spatial Analysis, Spatial Autocorrelation.
Length: 35 pages
Creation-Date: 2012-06
Number: 032
File-URL: http://economics.ryerson.ca/workingpapers/wp032.pdf
File-Format: Application/pdf
Handle: RePEc:rye:wpaper:wp032