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Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation

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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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File URL: http://cowles.econ.yale.edu/P/cd/d08b/d0877-r.pdf
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Bibliographic Info

Paper provided by Cowles Foundation for Research in Economics, 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 (May 1991), 59(3): 817-858
Handle: RePEc:cwl:cwldpp:877r

Note: CFP 780.
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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

Related research

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

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