Fixed-smoothing Asymptotics and Accurate F Approximation Using Vector Autoregressive Covariance Matrix Estimator
نویسندگان
چکیده
We develop a new asymptotic theory for autocorrelation robust tests using a vector autoregressive (VAR) covariance matrix estimator. In contrast to the conventional asymptotics where the VAR order goes to in nity but at a slower rate than the sample size, we have the VAR order grow at the same rate, as a xed fraction of the sample size. Under this xed-smoothing asymptotic speci cation, the associated Wald statistic remains asymptotically pivotal. On the basis of this asymptotics, we introduce a new and easy-to-use F test that employs a nite 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 variance estimation and kernel variance estimation. Simulations show that the new VAR F test with the empirical order selection is much more accurate in size than the conventional chi-square test. JEL Classi cation: C13; C14; C32; C51
منابع مشابه
" Fixed-smoothing Asymptotics and Ac- Curate F Approximation Using Vector Autoregressive Covariance Ma
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