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
Full text not available from this repository.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 |
| Divisions: | Economics |
| Subjects: | H Social Sciences > HA Statistics H Social Sciences > HB Economic Theory |
| JEL classification: | 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 |
| Date Deposited: | 27 Apr 2007 |
| Last Modified: | 13 Sep 2024 20:53 |
| URI: | http://eprints.lse.ac.uk/id/eprint/1211 |
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