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Nonparametric likelihood inference for general autoregressive models

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Abstract

This paper shows how nonparametric likelihood inference for autoregressive models can be based on the family of “empirical” Cressie–Read statistics. The results of the paper apply to possibly nonstationary autoregressive models with innovations that form a martingale difference sequence, and can accommodate multiple and complex unit roots, as well as deterministic components. As an application, the paper considers nonparametric likelihood-based tests for seasonal unit roots and for double unit roots. Monte Carlo evidence seems to suggest that the proposed tests have competitive finite sample properties.

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Authors and Affiliations

  1. Department of Economics and Related Studies, University of York, York, YO10 5DD, UK

    Francesco Bravo

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  1. Francesco Bravo
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Correspondence to Francesco Bravo.

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Bravo, F. Nonparametric likelihood inference for general autoregressive models. Stat Methods Appl 19, 79–106 (2010). https://doi.org/10.1007/s10260-009-0122-3

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