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

By Peter Robinson


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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