We propose a unified frequency domain cross-validation (FDCV) method to obtain an HAC standard error. Our proposed method allows for model/tuning parameter selection across parametric and nonparametric spectral estimators simultaneously. Our candidate class consists of restricted maximum likelihood-based (REML) autoregressive spectral estimators and lag-weights estimators with the Parzen kernel. We provide a method for efficiently computing the REML estimators of the autoregressive models. In simulations, we demonstrate the reliability of our FDCV method compared with the popular HAC estimators of Andrews-Monahan and Newey-West. Supplementary material for the article is available online.

翻译：我们建议采用统一频域交叉校验方法,以获得HAC标准误差。我们提议的方法允许同时通过参数和非参数光谱测算器进行模型/调试参数选择。我们候选的类别包括限制最大概率的Parzen内核自递偏移光谱测算器和滞后重量测算器。我们提供了一种高效计算自动反向模型的REML测算器的方法。在模拟中,我们展示了我们FDCV方法与流行的安德鲁斯-蒙纳汉和纽西韦斯HAC测算器相比的可靠性。该文章的补充材料可在网上查阅。