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On the consistency and finite-sample properties of nonparametric kernel time series regression, autoregression and density estimators

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: 02 Jan 2024 21:12
URI: http://eprints.lse.ac.uk/id/eprint/1405

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