Andrews, Donald W. K. Heteroskedasticity and autocorrelation consistent covariance matrix estimation. (English) Zbl 0732.62052 Econometrica 59, No. 3, 817-858 (1991). Author’s summary: This paper is concerned with the estimation of covariance matrices in the presence of heteroskedasticity and autocorrelation of unknown forms. Currently available estimators that are designed for this context depend upon the choice of a lag truncation parameter and a weighting scheme. Results in the literature provide a condition on the growth rate of the lag truncation parameter as \(T\to \infty\) that is sufficient for consistency. No results are available, however, regarding the choice of lag truncation parameter for a fixed sample size, regarding data-dependent automatic lag truncation parameters, or regarding the choice of weighting scheme. In consequence, available estimators are not entirely operational and the relative merits of the estimators are unknown. This paper addresses these problems. The asymptotic truncated mean squared errors of estimators in a given class are determined and compared. Asymptotically optimal kernel/weighting scheme and bandwidth/lag truncation parameters are obtained using an asymptotic truncated mean squared error criterion. Using these results, data- dependent automatic bandwidth/lag truncation parameters are introduced. The finite sample properties of the estimators are analyzed via Monte Carlo simulation. Reviewer: B.L.S.Prakasa Rao (New Delhi) Cited in 6 ReviewsCited in 707 Documents MSC: 62H12 Estimation in multivariate analysis 62F05 Asymptotic properties of parametric tests Keywords:kernel estimator; spectral density; linear models; nonlinear models; estimation of covariance matrices; heteroskedasticity; autocorrelation; fixed sample size; weighting scheme; asymptotic truncated mean squared errors of estimators; data-dependent automatic bandwidth/lag truncation parameters PDFBibTeX XMLCite \textit{D. W. K. Andrews}, Econometrica 59, No. 3, 817--858 (1991; Zbl 0732.62052) Full Text: DOI Link