Asymptotic properties of a robust variance matrix estimator for panel data when T is large
نویسنده
چکیده
I consider the asymptotic properties of a commonly advocated covariance matrix estimator for panel data. Under asymptotics where the cross-section dimension, n, grows large with the time dimension, T, fixed, the estimator is consistent while allowing essentially arbitrary correlation within each individual. However, many panel data sets have a non-negligible time dimension. I extend the usual analysis to cases where n and T go to infinity jointly and where T ! 1 with n fixed. I provide conditions under which t and F statistics based on the covariance matrix estimator provide valid inference and illustrate the properties of the estimator in a simulation study. r 2007 Elsevier B.V. All rights reserved. JEL classification: C12; C13; C23
منابع مشابه
Technical Appendix for Asymptotic Properties of a Robust Variance Matrix Estimator for Panel Data When T Is Large (to Be Omitted from Publication)
Throughout the appendix, let A = [trace(A A)] 1/2 be the Euclidean norm of a matrix A, and let i = n i=1 , t = T t=1 , and h = k h=1. Repeated use will be made of the following simple results, which are stated here for convenience.
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