Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support

Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation

Donald W. K. Andrews
Econometrica
Vol. 59, No. 3 (May, 1991), pp. 817-858
Published by: The Econometric Society
DOI: 10.2307/2938229
Stable URL: http://www.jstor.org/stable/2938229
Page Count: 42
  • Read Online (Free)
  • Download ($10.00)
  • Subscribe ($19.50)
  • Cite this Item
If you need an accessible version of this item please contact JSTOR User Support
Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
Preview not available

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. Results in the literature provide a condition on the growth rate of the lag truncation parameter as T → ∞ that is sufficient for consistency. No results are available, however, regarding the choice of lag truncation parameter for a fixed sample size, regarding data-dependent automatic lag truncation parameters, or regarding the choice of weighting scheme. In consequence, available estimators are not entirely operational and the relative merits of the estimators are unknown. This paper addresses these problems. The asymptotic truncated mean squared errors of estimators in a given class are determined and compared. Asymptotically optimal kernel/weighting scheme and bandwidth/lag truncation parameters are obtained using an asymptotic truncated mean squared error criterion. Using these results, data-dependent automatic bandwidth/lag truncation parameters are introduced. The finite sample properties of the estimators are analyzed via Monte Carlo simulation.

Page Thumbnails

  • Thumbnail: Page 
817
    817
  • Thumbnail: Page 
818
    818
  • Thumbnail: Page 
819
    819
  • Thumbnail: Page 
820
    820
  • Thumbnail: Page 
821
    821
  • Thumbnail: Page 
822
    822
  • Thumbnail: Page 
823
    823
  • Thumbnail: Page 
824
    824
  • Thumbnail: Page 
825
    825
  • Thumbnail: Page 
826
    826
  • Thumbnail: Page 
827
    827
  • Thumbnail: Page 
828
    828
  • Thumbnail: Page 
829
    829
  • Thumbnail: Page 
830
    830
  • Thumbnail: Page 
831
    831
  • Thumbnail: Page 
832
    832
  • Thumbnail: Page 
833
    833
  • Thumbnail: Page 
834
    834
  • Thumbnail: Page 
835
    835
  • Thumbnail: Page 
836
    836
  • Thumbnail: Page 
837
    837
  • Thumbnail: Page 
838
    838
  • Thumbnail: Page 
839
    839
  • Thumbnail: Page 
840
    840
  • Thumbnail: Page 
841
    841
  • Thumbnail: Page 
842
    842
  • Thumbnail: Page 
843
    843
  • Thumbnail: Page 
844
    844
  • Thumbnail: Page 
845
    845
  • Thumbnail: Page 
846
    846
  • Thumbnail: Page 
847
    847
  • Thumbnail: Page 
848
    848
  • Thumbnail: Page 
849
    849
  • Thumbnail: Page 
850
    850
  • Thumbnail: Page 
851
    851
  • Thumbnail: Page 
852
    852
  • Thumbnail: Page 
853
    853
  • Thumbnail: Page 
854
    854
  • Thumbnail: Page 
855
    855
  • Thumbnail: Page 
856
    856
  • Thumbnail: Page 
857
    857
  • Thumbnail: Page 
858
    858