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On the consistency and finite-sample properties of nonparametric kernel time series regression, autoregression and density estimators

By Peter Robinson

Abstract

Kernel estimators of conditional expectations and joint probability densities are studied in the context of a vector-valued stationary time series. Weak consistency is established under minimal moment conditions and under a hierarchy of weak dependence and bandwidth conditions. Prompted by these conditions, some finite-sample theory explores the effect of serial dependence on variability of estimators, and its implications for choice of bandwidth

Topics: HA Statistics, HB Economic Theory
Publisher: Springer Netherlands
Year: 1986
DOI identifier: 10.1007/BF02482541
OAI identifier: oai:eprints.lse.ac.uk:1405
Provided by: LSE Research Online
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