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A unified frequency domain cross-validation (FDCV) method is proposed toobtain a heteroskedasticity and autocorrelation consistent (HAC) standarderror. This method enables model/tuning parameter selection across bothparametric and nonparametric spectral estimators simultaneously. The candidateclass for this approach consists of restricted maximum likelihood-based (REML)autoregressive spectral estimators and lag-weights estimators with the Parzenkernel. Additionally, an efficient technique for computing the REML estimatorsof autoregressive models is provided. Through simulations, the reliability ofthe FDCV method is demonstrated, comparing favorably with popular HACestimators such as Andrews-Monahan and Newey-West.

A Unified Frequency Domain Cross-Validatory Approach to HAC Standard Error Estimation