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Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation

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Author Info
Donald W.K. Andrews () (Cowles Foundation, Yale University)

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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.

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Publisher Info
Paper provided by Cowles Foundation, Yale University in its series Cowles Foundation Discussion Papers with number 877R.

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Length: 62 pages
Date of creation: 1988
Date of revision: Jul 1989
Publication status: Published in Econometrica, 59(3), 1991
Handle: RePEc:cwl:cwldpp:877r

Note: CFP 780.
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Postal: Yale University, Box 208281, New Haven, CT 06520-8281 USA
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Web page: http://cowles.econ.yale.edu/
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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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Related research
Keywords: Autocorrelation kernel estimator spectral density heteroskedasticity mean squared error covariance matrix

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