Bayesian inference for nonlinear structural time series models

We consider efficient methods for likelihood inference applied to structural models. In particular, we introduce a particle filter method which concentrates upon disturbances in the Markov state of the approximating solution to the structural model. A particular feature of such models is that the co...

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Bibliographic Details
Published inJournal of econometrics Vol. 179; no. 2; pp. 99 - 111
Main Authors Hall, Jamie, Pitt, Michael K., Kohn, Robert
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.04.2014
Elsevier Sequoia S.A
Subjects
Auxiliary particle filter
Bayesian analysis
Bayesian method
DSGE model
Estimation
Growth models
Markov analysis
Multi-modal
Neoclassical economics
Non-linear models
State space model
Structural analysis
Studies
Time series
Auxiliary particle filter
State space model
DSGE model
Multi-modal
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Summary:We consider efficient methods for likelihood inference applied to structural models. In particular, we introduce a particle filter method which concentrates upon disturbances in the Markov state of the approximating solution to the structural model. A particular feature of such models is that the conditional distribution of interest for the disturbances is often multimodal. We provide a fast and effective method for approximating such distributions. We estimate a neoclassical growth model using this approach. An asset pricing model with persistent habits is also considered. The methodology we employ allows many fewer particles to be used than alternative procedures for a given precision.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2013.10.016