Robust Covariance Matrix Estimation in Time Series: A Review

Abstract

In the analysis of economic, financial and other time series, long-run variance estimators play an important role in estimating model parameters more efficiently and drawing more accurate statistical inference on the parameters. A non-technical review of long-run variance estimation is provided. Both parametric and nonparametric estimators are discussed. Kernel methods are dominant among all estimation procedures, and therefore recent developments in kernel-smoothed estimators and related inference are presented. The information given can help practitioners decide on a suitable long-run variance estimator.