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Model selection in hidden Markov models : a simulation study

Author

Listed:
  • Michele Costa

    (Università di Bologna)

  • Luca De angelis

    (Università di Bologna)

Abstract

A review of model selection procedures in hidden Markov models reveals contrasting evidence about the reliability and the precision of the most commonly used methods. In order to evaluate and compare existing proposals, we develop a Monte Carlo experiment which allows a powerful insight on the behaviour of the most widespread model selection methods. We find that the number of observations, the conditional state-dependent probabilities, and the latent transition matrix are the main factors influencing information criteria and likelihood ratio test results. We also find evidence that, for shorter univariate time series, AIC strongly outperforms BIC.

Suggested Citation

  • Michele Costa & Luca De angelis, 2010. "Model selection in hidden Markov models : a simulation study," Quaderni di Dipartimento 7, Department of Statistics, University of Bologna.
    • Handle: RePEc:bot:quadip:wpaper:104
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    File URL: http://amsacta.cib.unibo.it/2909
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    Cited by:

    1. S. Bacci & S. Pandolfi & F. Pennoni, 2014. "A comparison of some criteria for states selection in the latent Markov model for longitudinal data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(2), pages 125-145, June.
    2. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.

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