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Heteroskedasticity And Autocorrelation Consistent Covariance Matrix Estimation, Donald W.K. Andrews
Heteroskedasticity And Autocorrelation Consistent Covariance Matrix Estimation, Donald W.K. Andrews
Cowles Foundation Discussion Papers
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.
Heteroskedasticity And Autocorrelation Consistent Covariance Matrix Estimation, Donald W.K. Andrews
Heteroskedasticity And Autocorrelation Consistent Covariance Matrix Estimation, Donald W.K. Andrews
Cowles Foundation Discussion Papers
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 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, …