We consider the derivation of data-dependent simultaneous bandwidths for double kernel heteroskedasticity and autocorrelation consistent (DK-HAC) estimators. In addition to the usual smoothing over lagged autocovariances for classical HAC estimators, the DK-HAC estimator also applies smoothing over the time direction. We obtain the optimal bandwidths that jointly minimize the global asymptotic MSE criterion and discuss the trade-off between bias and variance with respect to smoothing over lagged autocovariances and over time. Unlike the MSE results of Andrews (1991), we establish how nonstationarity affects the bias-variance trade-o?. We use the plug-in approach to construct data-dependent bandwidths for the DK-HAC estimators and compare them with the DK-HAC estimators from Casini (2021) that use data-dependent bandwidths obtained from a sequential MSE criterion. The former performs better in terms of size control, especially with stationary and close to stationary data. Finally, we consider long-run variance estimation under the assumption that the series is a function of a nonparametric estimator rather than of a semiparametric estimator that enjoys the usual T^(1/2) rate of convergence. Thus, we also establish the validity of consistent long-run variance estimation in nonparametric parameter estimation settings.

翻译：我们认为,数据依赖同步带宽对于双内核螺旋升温度和自闭度一致(DK-HAC)测算器(DK-HAC)与自闭度和自闭度一致(DK-HAC)的测算器而言,是数据依赖同时带宽的推算。除了古典HAC测算器通常的顺差自动变换法平滑,DK-HAC测算器在时间方向上也适用平稳。我们获得了最佳带宽,可以共同最大限度地减少全球无悬浮MSE标准,并讨论在平滑落后的自动变异性和时间上的偏差和偏差之间的权衡。与Andrews(1991年) MSE 的 MSE 设置结果不同,我们确定不常态常态对偏差交易交易率-o 。我们使用插头方法为DK-HAC 估测算器构建数据依赖数据的带宽宽宽带宽带宽带宽带宽(2021年),使用从连续的测算测算标准获得的数据依赖带宽差值。我们以前在规模控制方面表现得更好,特别是固定和接近定点数据。我们确定了不固定和接近度的比常态平交错值的比假设的平置值1,我们认为长期测算值的测测算值的平比值的比长期的平位值。