Asymptotic F Test in a GMM Framework with Cross Sectional Dependence
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.
|Date of creation:||Jun 2012|
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- Sun, Yixiao, 2011. "Robust trend inference with series variance estimator and testing-optimal smoothing parameter," Journal of Econometrics, Elsevier, vol. 164(2), pages 345-366, October.
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- Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005.
"A New Asymptotic Theory For Heteroskedasticity-Autocorrelation Robust Tests,"
Cambridge University Press, vol. 21(06), pages 1130-1164, December.
- Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005. "A New Asymptotic Theory for Heteroskedasticity-Autocorrelation Robust Tests," Working Papers 05-08, Cornell University, Center for Analytic Economics.
- Conley, T. G., 1999. "GMM estimation with cross sectional dependence," Journal of Econometrics, Elsevier, vol. 92(1), pages 1-45, September. Full references (including those not matched with items on IDEAS)