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Kernel estimation and interpolation for time series containing missing observations

By Peter Robinson

Abstract

Kernel estimators of conditional expectations are adapted for use in the analysis of stationary time series containing missing observations. Estimators of conditional expectations at fixed points are shown to have an asymptotic distribution with a relatively simple variance-covariance structure. The kernel method is also used to interpolate missing observations, and is shown to converge in probability to the least squares predictor. The results are established under the strong mixing condition and moment conditions, and the methods are applied to a real data set

Topics: HA Statistics, HB Economic Theory
Publisher: Springer Netherlands
Year: 1984
DOI identifier: 10.1007/BF02481979
OAI identifier: oai:eprints.lse.ac.uk:1211
Provided by: LSE Research Online
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