MCMC, likelihood estimation and identifiability problems in DLM models
AbstractIn 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.
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Bibliographic InfoPaper provided by GEMF - Faculdade de Economia, Universidade de Coimbra in its series GEMF Working Papers with number 2010-12.
Length: 22 pages
Date of creation: Jun 2010
Date of revision:
Bayesian Statistics; DLM Models; Markov chain Monte Carlo; Maximum Likelihood; Model Identification.;
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- Durbin, James & Koopman, Siem Jan, 2001.
"Time Series Analysis by State Space Methods,"
Oxford University Press, number 9780198523543, September.
- Tom Doan, . "SEASONALDLM: RATS procedure to create the matrices for the seasonal component of a DLM," Statistical Software Components RTS00251, Boston College Department of Economics.
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