Robinson, Peter (1986) On the consistency and finite-sample properties of nonparametric kernel time series regression, autoregression and density estimators. Annals of the Institute of Statistical Mathematics, 38 (1). pp. 539-549. ISSN 0020-3157
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Identification Number: 10.1007/BF02482541
Abstract
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.
| Item Type: | Article |
|---|---|
| Official URL: | http://www.springer.com/statistics/journal/10463 |
| Additional Information: | © 1986 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 > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods |
| Date Deposited: | 27 Apr 2007 |
| Last Modified: | 11 Dec 2024 21:52 |
| URI: | http://eprints.lse.ac.uk/id/eprint/1405 |
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