  A unified frequency domain cross-validation (FDCV) method is proposed to
obtain a heteroskedasticity and autocorrelation consistent (HAC) standard
error. This method enables model/tuning parameter selection across both
parametric and nonparametric spectral estimators simultaneously. The candidate
class for this approach consists of restricted maximum likelihood-based (REML)
autoregressive spectral estimators and lag-weights estimators with the Parzen
kernel. Additionally, an efficient technique for computing the REML estimators
of autoregressive models is provided. Through simulations, the reliability of
the FDCV method is demonstrated, comparing favorably with popular HAC
estimators such as Andrews-Monahan and Newey-West.
