【主题】Optimal HAR Inference
【报告人】窦立宇（助理教授，香港中文大学（深圳））
【时间】11月26日 星期二 15：30-17：00
【地点】经济学院楼701室
【摘要】 This paper considers the problem of deriving heteroskedasticity and autocorrelation robust (HAR) inference about a scalar parameter of interest. I derive finite-sample optimal tests in the Gaussian location model, under nonparametric assumptions on the underlying spectral density. The optimal test trades off bias and variability, and requires an adjustment of the critical value to account for the maximum bias of the implied long-run variance estimator. I find that with an appropriate adjustment to the critical value, it is nearly optimal to use the so-called equal-weighted cosine (EWC) test, where the long-run variance is estimated by projections onto qtype II cosines. The practical implications are an explicit link between the choice of q and assumptions on the underlying spectrum, as well as a corresponding adjustment to the usual Student-tcritical value. Simulations show that the suggested new EWC test also performs well outside the Gaussian location model.
