Estimation of the Covariance Matrix of the Least-Squares Regression Coefficients When the Disturbance Covariance Matrix Is of Unknown Form

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Estimation of the Covariance Matrix of the Least-Squares Regression Coefficients When the Disturbance Covariance Matrix Is of Unknown Form

Author Info

Keener, Robert W.
Kmenta, Jan
Weber, Neville C.

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Jan Kmenta

Abstract

This paper deals with the problem of estimating the covariance matrix of the least-squares regression coefficients under heteroskedasticity and/or autocorrelation of unknown form. We consider an estimator proposed by White [17] and give a relatively simple proof of its consistency. Our proof is based on more easily verifiable conditions than those of White. An alternative estimator with improved small sample properties is also presented.

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Publisher Info
Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 7 (1991)
Issue (Month): 01 (March)
Pages: 22-45
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Handle: RePEc:cup:etheor:v:7:y:1991:i:01:p:22-45_00

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Paper
- Keener, R.W. & Kmenta, J. & Weber, N.C., 1990. "Estimation of the Covariance Matrix of the Least Squares regression Coefficients when the Disturbance Covariance Matrix is of Unknown Form," Papers 90-19, Michigan - Center for Research on Economic & Social Theory.

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Yongmiao Hong & Jin Lee, 2000. "Wavelet-based Estimation for Heteroskedasticity and Autocorrelation Consistent Variance-Covariance Matrices," Econometric Society World Congress 2000 Contributed Papers 1211, Econometric Society. [Downloadable!]

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