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Selecting nonlinear time series models using information criteria

Author

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  • Zacharias Psaradakis
  • Martin Sola
  • Fabio Spagnolo
  • Nicola Spagnolo

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.

Suggested Citation

  • Zacharias Psaradakis & Martin Sola & Fabio Spagnolo & Nicola Spagnolo, 2009. "Selecting nonlinear time series models using information criteria," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(4), pages 369-394, July.
    • Handle: RePEc:bla:jtsera:v:30:y:2009:i:4:p:369-394
    DOI: 10.1111/j.1467-9892.2009.00614.x
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    References listed on IDEAS

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    1. Dueker, Michael J. & Psaradakis, Zacharias & Sola, Martin & Spagnolo, Fabio, 2011. "Multivariate contemporaneous-threshold autoregressive models," Journal of Econometrics, Elsevier, vol. 160(2), pages 311-325, February.
    2. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    3. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    4. Nishii, R., 1988. "Maximum likelihood principle and model selection when the true model is unspecified," Journal of Multivariate Analysis, Elsevier, vol. 27(2), pages 392-403, November.
    5. Dueker, Michael J. & Sola, Martin & Spagnolo, Fabio, 2007. "Contemporaneous threshold autoregressive models: Estimation, testing and forecasting," Journal of Econometrics, Elsevier, vol. 141(2), pages 517-547, December.
    6. Hansen, Bruce E, 1992. "The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 61-82, Suppl. De.
    7. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
    8. George Kapetanios, 2001. "Model Selection in Threshold Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(6), pages 733-754, November.
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    Citations

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    1. Michael J. Dueker & Zacharias Psaradakis & Martin Sola & Fabio Spagnolo, 2013. "State-Dependent Threshold Smooth Transition Autoregressive Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(6), pages 835-854, December.
    2. Dueker, Michael J. & Psaradakis, Zacharias & Sola, Martin & Spagnolo, Fabio, 2011. "Multivariate contemporaneous-threshold autoregressive models," Journal of Econometrics, Elsevier, vol. 160(2), pages 311-325, February.
    3. Francesco Giordano & Marcella Niglio & Cosimo Damiano Vitale, 2023. "Linear approximation of the Threshold AutoRegressive model: an application to order estimation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 27-56, March.
    4. Rickard Sandberg, 2018. "Unit Root Testing in Multiple Smooth Break Models with Nonlinear Dynamics," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 942-952, November.
    5. Line Elvstrøm Ekner & Emil Nejstgaard, 2013. "Parameter Identification in the Logistic STAR Model," Discussion Papers 13-07, University of Copenhagen. Department of Economics.
    6. Rinke Saskia & Sibbertsen Philipp, 2016. "Information criteria for nonlinear time series models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(3), pages 325-341, June.
    7. Michael Frömmel, 2010. "Volatility Regimes in Central and Eastern European Countries’ Exchange Rates," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 60(1), pages 2-21, February.
    8. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    9. Sandberg, Rickard, 2016. "Trends, unit roots, structural changes, and time-varying asymmetries in U.S. macroeconomic data: the Stock and Watson data re-examined," Economic Modelling, Elsevier, vol. 52(PB), pages 699-713.
    10. Diteboho Xaba & Ntebogang Dinah Moroke & Ishmael Rapoo, 2019. "Modeling Stock Market Returns of BRICS with a Markov-Switching Dynamic Regression Model," Journal of Economics and Behavioral Studies, AMH International, vol. 11(3), pages 10-22.
    11. Vladimir Andric & Dusko Bodroza & Mihajlo Djukic, 2024. "A Commentary on US Sovereign Debt Persistence and Nonlinear Fiscal Adjustment," Mathematics, MDPI, vol. 12(20), pages 1-33, October.
    12. Diteboho Xaba & Ntebogang Dinah Moroke & Johnson Arkaah & Charlemagne Pooe, 2016. "Modeling South African Banks closing stock prices: a Markov-Switching Approach," Journal of Economics and Behavioral Studies, AMH International, vol. 8(1), pages 36-40.
    13. Hee-Young Kim & Christian H. Weiß & Tobias A. Möller, 2020. "Models for autoregressive processes of bounded counts: How different are they?," Computational Statistics, Springer, vol. 35(4), pages 1715-1736, December.
    14. Rinke, Saskia, 2016. "The Influence of Additive Outliers on the Performance of Information Criteria to Detect Nonlinearity," Hannover Economic Papers (HEP) dp-575, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    15. Greta Goracci, 2021. "An empirical study on the parsimony and descriptive power of TARMA models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 109-137, March.

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