Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices
Conditions are derived for the consistency of kernel estimators of the covariance matrix of a sum of vectors of dependent heterogeneous random variables, which match those of the currently best-known conditions for the central limit theorem, as required for a unified theory of asymptotic inference. These include finite moments of order no more than 2 + for > 0, trending variances, and variables which are near-epoch dependent on a mixing process, but not necessarily mixing. The results are also proved for the case of sample-dependent bandwidths.
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Volume (Year): 68 (2000)
Issue (Month): 2 (March)
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"Automatic Lag Selection in Covariance Matrix Estimation,"
9220, Wisconsin Madison - Social Systems.
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NBER Technical Working Papers
0055, National Bureau of Economic Research, Inc.
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- Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-708, May.
- Davidson, James, 1993. "The Central Limit Theorem for Globally Nonstationary Near-Epoch Dependent Functions of Mixing Processes: The Asymptotically Degenerate Case," Econometric Theory, Cambridge University Press, vol. 9(03), pages 402-412, June.
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