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Kernel estimation and interpolation for time series containing missing observations

Robinson, Peter (1984) Kernel estimation and interpolation for time series containing missing observations. Annals of the Institute of Statistical Mathematics, 36 (1). pp. 403-417. ISSN 0020-3157

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Identification Number: 10.1007/BF02481979

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.

Item Type: Article
Official URL: http://www.springer.com/statistics/journal/10463
Additional Information: © 1984 Institute of Statistical Mathematics
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
Sets: Departments > Economics
Collections > Economists Online
Date Deposited: 27 Apr 2007
Last Modified: 02 May 2012 12:24
URI: http://eprints.lse.ac.uk/id/eprint/1211

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