Inference for partial correlations of a multivariate Gaussian time series
推导了多元高斯时间序列偏相关性的渐近联合分布和新协方差估计量，并开发了Wald置信区间和检验方法，在自相关存在时比独立观测假设方法更准确。
Abstract We derive an asymptotic joint distribution and novel covariance estimator for the partial correlations of a multivariate Gaussian time series under mild regularity conditions. Using our derived asymptotic distribution, we develop a Wald confidence interval and testing procedure for inference of individual partial correlations for time series data. Through simulation we demonstrate that our proposed confidence interval attains higher coverage rates and our testing procedure achieves false positive rates closer to the nominal levels than approaches that assume independent observations when autocorrelation is present.