HAC Corrections for Strongly Autocorrelated Time Series
指出常用的异方差自相关一致（HAC）标准误在强自相关小样本中表现不佳，回顾了失败原因，并基于平稳高斯AR(1)模型推导出新的有效推断方法，模拟显示其性能良好。
Applied work routinely relies on heteroscedasticity and autocorrelation consistent (HAC) standard errors when conducting inference in a time series setting. As is well known, however, these corrections perform poorly in small samples under pronounced autocorrelations. In this article, I first provide a review of popular methods to clarify the reasons for this failure. I then derive inference that remains valid under a specific form of strong dependence. In particular, I assume that the long-run properties can be approximated by a stationary Gaussian AR(1) model, with coefficient arbitrarily close to one. In this setting, I derive tests that come close to maximizing a weighted average power criterion. Small sample simulations show these tests to perform well, also in a regression context.