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

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Summary

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.

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Authors and Affiliations

  1. London School of Economics and Political Science, London, UK

    P. M. Robinson

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  1. P. M. Robinson
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This research was supported by the ESRC.

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Robinson, P.M. On the consistency and finite-sample properties of nonparametric kernel time series regression, autoregression and density estimators. Ann Inst Stat Math 38, 539–549 (1986). https://doi.org/10.1007/BF02482541

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  • DOI: https://doi.org/10.1007/BF02482541

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