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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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Official URL: http://dx.doi.org/10.1111/j.1467-9892.2009.00614.x

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.

Item Type: Article
Keyword(s) / Subject(s): Complexity-penalized likelihood criteria, nonlinear models, Monte Carlo experiments
School or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Economics, Mathematics and Statistics
Depositing User: Administrator
Date Deposited: 01 Feb 2011 09:20
Last Modified: 25 Apr 2013 13:36
URI: http://eprints.bbk.ac.uk/id/eprint/1962

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