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MCMC, likelihood estimation and identifiability problems in DLM models


  • António Alberto Santos

    () (GEMF/Faculdade de Economia, Universidade de Coimbra, Portugal)


In this article we deal with the identification problem within the Dynamic Linear Models family and show that using Bayesian estimation procedures we can deal better with these problems in comparison with the traditional Maximum Likelihood estimation approach. Using a Bayesian approach supported by Markov chain Monte Carlo techniques, we obtain the same results as the Maximum likelihood approach in the case of identifiable models, but in the case of non-identifiable models, we were able to estimate the parameters that are identifiable, as well as to pinpoint the troublesome parameters. Assuming a Bayesian approach, we also discuss the computational aspects, namely the ongoing discussion between single- versus multi-move samplers. Our aim is to give a clear example of the benefits of adopting a Bayesian approach to the estimation of high dimensional statistical models.

Suggested Citation

  • António Alberto Santos, 2010. "MCMC, likelihood estimation and identifiability problems in DLM models," GEMF Working Papers 2010-12, GEMF, Faculty of Economics, University of Coimbra.
  • Handle: RePEc:gmf:wpaper:2010-12

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    References listed on IDEAS

    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    2. Philippe Robert-Demontrond & R. Ringoot, 2004. "Introduction," Post-Print halshs-00081823, HAL.
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    Bayesian Statistics; DLM Models; Markov chain Monte Carlo; Maximum Likelihood; Model Identification.;

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