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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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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
Library of Congress subject classification: H Social Sciences > HA Statistics
H Social Sciences > HB Economic Theory
Journal of Economic Literature Classification System: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation
C - Mathematical and Quantitative Methods > C3 - Econometric Methods: Multiple; Simultaneous Equation Models; Multiple Variables; Endogenous Regressors > C32 - Time-Series Models
Sets: Departments > Economics
Collections > Economists Online
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 27 Apr 2007
URL: http://eprints.lse.ac.uk/1211/

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