CFDP 877R

Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation

Author(s): 

Publication Date: July 1988

Revision Date: July 1989

Pages: 62

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

JEL Classification Codes:  211

See CFDP Version(s): CFDP 877

See CFP: 780