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On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
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Pitt, Michael K., Silva, Ralph dos Santos, Giordani, Paolo and Kohn, Robert (2012) On some properties of Markov chain Monte Carlo simulation methods based on the particle filter. Journal of Econometrics, Vol.171 (No.2). pp. 134-151. doi:10.1016/j.jeconom.2012.06.004 ISSN 0304-4076.
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Official URL: http://dx.doi.org/10.1016/j.jeconom.2012.06.004
Abstract
Andrieu et al. (2010) prove that Markov chain Monte Carlo samplers still converge to the correct posterior distribution of the model parameters when the likelihood estimated by the particle filter (with a finite number of particles) is used instead of the likelihood. A critical issue for performance is the choice of the number of particles. We add the following contributions. First, we provide analytically derived, practical guidelines on the optimal number of particles to use. Second, we show that a fully adapted auxiliary particle filter is unbiased and can drastically decrease computing time compared to a standard particle filter. Third, we introduce a new estimator of the likelihood based on the output of the auxiliary particle filter and use the framework of Del Moral (2004) to provide a direct proof of the unbiasedness of the estimator. Fourth, we show that the results in the article apply more generally to Markov chain Monte Carlo sampling schemes with the likelihood estimated in an unbiased manner. © 2012 Elsevier B.V. All rights reserved.
| Item Type: | Journal Article | ||||
|---|---|---|---|---|---|
| Divisions: | Faculty of Social Sciences > Economics | ||||
| Journal or Publication Title: | Journal of Econometrics | ||||
| Publisher: | Elsevier BV * North-Holland | ||||
| ISSN: | 0304-4076 | ||||
| Official Date: | December 2012 | ||||
| Dates: |
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| Volume: | Vol.171 | ||||
| Number: | No.2 | ||||
| Page Range: | pp. 134-151 | ||||
| DOI: | 10.1016/j.jeconom.2012.06.004 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Access rights to Published version: | Restricted or Subscription Access |
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