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Bayesian inference for nonlinear structural time series models
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Hall, Jamie, Pitt, Michael K. and Kohn, Robert (2014) Bayesian inference for nonlinear structural time series models. Journal of Econometrics, 179 (2). pp. 99-111. doi:10.1016/j.jeconom.2013.10.016 ISSN 0304-4076.
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Official URL: http://dx.doi.org/10.1016/j.jeconom.2013.10.016
Abstract
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.
Item Type: | Journal Article | ||||||||||
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Subjects: | H Social Sciences > HB Economic Theory Q Science > QA Mathematics |
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Divisions: | Faculty of Social Sciences > Economics | ||||||||||
Library of Congress Subject Headings (LCSH): | Econometric models, Bayesian statistical decision theory, Markov processes | ||||||||||
Journal or Publication Title: | Journal of Econometrics | ||||||||||
Publisher: | Elsevier BV * North-Holland | ||||||||||
ISSN: | 0304-4076 | ||||||||||
Official Date: | April 2014 | ||||||||||
Dates: |
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Volume: | 179 | ||||||||||
Number: | 2 | ||||||||||
Page Range: | pp. 99-111 | ||||||||||
DOI: | 10.1016/j.jeconom.2013.10.016 | ||||||||||
Status: | Peer Reviewed | ||||||||||
Publication Status: | Published | ||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||
Date of first compliant deposit: | 6 August 2018 | ||||||||||
Date of first compliant Open Access: | 6 August 2018 |
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