Bootstrap HAC Tests for Ordinary Least Squares Regression*
提出一种基于移动块Bootstrap和准估计量的方法，为OLS回归在未知异方差和自相关下提供稳健的渐近协方差矩阵估计和显著性检验，蒙特卡洛模拟显示其有限样本性能良好。
Abstract There is a need for tests that are derived from the ordinary least squares (OLS) estimators of regression coefficients and are useful in the presence of unspecified forms of heteroskedasticity and autocorrelation. A method that uses the moving block bootstrap and quasi‐estimators in order to derive a consistent estimator of the asymptotic covariance matrix for the OLS estimators and robust significance tests is proposed. The method is shown to be asymptotically valid and Monte Carlo evidence indicates that it is capable of providing good control of significance levels in finite samples and good power compared with two other bootstrap tests.