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.

翻译：提出了一种统一的频域交叉验证（FDCV）方法，用于获得异方差自相关一致（HAC）标准误。该方法能够同时在参数和非参数谱估计器中进行模型及调优参数选择。该方法的候选类别包括基于约束最大似然（REML）的自回归谱估计器以及采用Parzen核的滞后权重估计器。此外，还提供了一种高效计算自回归模型REML估计量的技术。通过模拟实验，验证了FDCV方法的可靠性，其表现优于诸如Andrews-Monahan和Newey-West等常用HAC估计器。