题目：Simple and Accurate F Tests in the Time Series GMM Framework

主讲人：Sun, Yixiao Associate Professor, University of California - San Diego

Abstract

In this paper, we develop a new asymptotic theory of the long run variance estimator obtained by fitting a vector autoregressive model to the transformed moment processes in a GMM framework.. In contrast to the conventional asymptotics where the VAR lag order p goes to infinity but at a slower rate than the sample size, we assume that p grows at the same rate as the sample size. Under this asymptotic specification, the long run variance estimator is not consistent, but the associated Wald statistic and t-statistic remain asymptotically pivotal. On the basis of the new asymptotic theory, we introduce a new and easy-to-use F* test that employs a finite sample corrected Wald statistic and uses critical values from an F-distribution. We also propose an empirical VAR order selection rule that exploits the connection between VAR long run variance estimation and kernel long run variance estimation. Simulations show that the new VAR F* test with the empirical order selection is more accurate in size than the conventional chi-square test and kernel-based F* test with no or minimal power loss. The paper complements the recent paper by Sun(2010d)who considers kernel-based F* texts.