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    Selecting nonlinear time series models using information criteria

    Psaradakis, Zacharias and Sola, Martin and Spagnolo, F. and Spagnolo, N. (2009) Selecting nonlinear time series models using information criteria. Journal of Time Series Analysis 30 (4), pp. 369-394. ISSN 0143-9782.

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    Abstract

    This article considers the problem of selecting among competing nonlinear time series models by using complexity-penalized likelihood criteria. An extensive simulation study is undertaken to assess the small-sample performance of several popular criteria in selecting among nonlinear autoregressive models belonging to some families that have been popular with practitioners.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Complexity-penalized likelihood criteria, nonlinear models, Monte Carlo experiments
    School: Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School
    Research Centres and Institutes: Applied Macroeconomics, Birkbeck Centre for
    Depositing User: Administrator
    Date Deposited: 01 Feb 2011 09:20
    Last Modified: 02 Aug 2023 16:51
    URI: https://eprints.bbk.ac.uk/id/eprint/1962

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