Least squares regression with heteroskedasticity and autocorrelation consistent (HAC) standard errors has proved very useful in cross section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HAC estimation technology to time series environments. First, in plausible time-series environments involving failure of strong exogeneity, OLS parameter estimates can be inconsistent, so that HAC inference fails even asymptotically. Second, most economic time series have strong autocorrelation, which renders HAC regression parameter estimates highly inefficient. Third, strong autocorrelation similarly renders HAC conditional predictions highly inefficient. Finally, The structure of popular HAC estimators is ill-suited for capturing the autoregressive autocorrelation typically present in economic time series, which produces large size distortions and reduced power in HACbased hypothesis testing, in all but the largest samples. We show that all four problems are largely avoided by the use of a simple dynamic regression procedure, which is easily implemented. We demonstrate the advantages of dynamic regression with detailed simulations covering a range of practical issues.

翻译：异方差自相关一致（HAC）标准误的最小二乘回归在横截面环境中已被证明非常有用。然而，当将HAC估计技术应用于时间序列环境时，必须面对几个通常被忽视的主要困难。首先，在涉及强外生性失效的合理时间序列环境中，OLS参数估计可能不一致，因此HAC推断甚至在大样本下也会失效。其次，大多数经济时间序列具有强自相关性，这使得HAC回归参数估计的效率极低。第三，强自相关性同样导致HAC条件预测的效率极低。最后，流行的HAC估计量结构不适于捕捉经济时间序列中典型的自回归自相关性，这导致除最大样本外，基于HAC的假设检验存在较大的规模扭曲和功效降低。我们表明，通过使用一种简单且易于实现的动态回归程序，这四个问题基本可以避免。我们通过涵盖一系列实际问题的详细模拟，展示了动态回归的优势。