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Bayesian inference for nonlinear structural time series models
- Publication Year :
- 2012
-
Abstract
- This article discusses a partially adapted particle filter for estimating the likelihood of a nonlinear structural econometric state space models whose state transition density cannot be expressed in closed form. The filter generates the disturbances in the state transition equation and allows for multiple modes in the conditional disturbance distribution. The particle filter produces an unbiased estimate of the likelihood and so can be used to carry out Bayesian inference in a particle Markov chain Monte Carlo framework. We show empirically that when the signal to noise ratio is high, the new filter can be much more efficient than the standard particle filter, in the sense that it requires far fewer particles to give the same accuracy. The new filter is applied to several simulated and real examples and in particular to a dynamic stochastic general equilibrium model.<br />Typo correction
- Subjects :
- FOS: Computer and information sciences
Economics and Econometrics
Mathematical optimization
Markov chain
State-space representation
Applied Mathematics
HB
Inference
Conditional probability distribution
Bayesian inference
Statistics - Applications
Methodology (stat.ME)
Feature (machine learning)
Applications (stat.AP)
QA
Particle filter
Auxiliary particle filter
Statistics - Methodology
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 03044076
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....40c79a18ad88507c6f838d0db7c54477