User’s Guide : Long-run Covariance Estimation
Long-run Covariance Estimation
The long-run (variance) covariance matrix (LRCOV) occupies an important role in modern econometric analysis. This matrix is, for example, central to calculation of efficient GMM weighting matrices (Hansen 1982), heteroskedastic and autocorrelation (HAC) robust standard errors (Newey and West 1987), and is employed in unit root (Phillips and Perron 1988) and cointegration analysis (Phillips and Hansen 1990, Hansen 1992b).
EViews offers tools for computing symmetric LRCOV and the one-sided LRCOV using nonparametric kernel (Newey-West 1987, Andrews 1991), parametric VARHAC (Den Haan and Levin 1997), and prewhitened kernel (Andrews and Monahan 1992) methods. In addition, EViews supports Andrews (1991) and Newey-West (1994) automatic bandwidth selection methods for kernel estimators, and information criteria based lag length selection methods for VARHAC and prewhitening estimation.