@techreport{482efe9537384a9fb833eb728c9119f9,
title = "Consistency of Kernel Estimators of Heteroscedastic and Autocorrelated Covariance Matrices",
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.",
keywords = "kernel estimator, matrices",
author = "{de Jong}, R.M. and J. Davidson",
note = "Pagination: 21",
year = "1996",
language = "English",
volume = "1996-52",
series = "CentER Discussion Paper",
publisher = "Econometrics",
type = "WorkingPaper",
institution = "Econometrics",
}