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Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices

de Jong,R.M.
; 
Davidson,J.
Abstract
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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Pagination: 21
Date
1996
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Volume Title
Publisher
Econometrics
Collections
Tilburg University Research Output Collection
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52.pdf
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Keywords
kernel estimator, matrices
Citation
de Jong, R M & Davidson, J 1996 'Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices' CentER Discussion Paper, vol. 1996-52, Econometrics, Tilburg.
URI
https://hdl.handle.net/20.500.14602/26337
License
info:eu-repo/semantics/restrictedAccess
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