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.
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