Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation

Donald Andrews ()

No 877R, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: 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. No results are available, however, regarding the choice of a lag truncation parameter for a fixed sample size, regarding data-dependent automatic lag truncation parameters, or regarding the choice of weighing scheme. In consequence, available estimators are not entirely operational and the relative merits of the estimators are unknown.

Keywords: Autocorrelation; kernel estimator; spectral density; heteroskedasticity; mean squared error; covariance matrix (search for similar items in EconPapers)
Pages: 62 pages
Date: 1988, Revised 1989-07
Note: CFP 780.
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Citations: View citations in EconPapers (3)

Published in Econometrica (May 1991), 59(3): 817-858

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Related works:
Journal Article: Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation (1991)
Working Paper: Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation (1988)
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