MCMC Estimation of Restricted Covariance Matrices
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Chan, Chi Chun (Joshua); Jeliazkov, Ivan
Description
This article is motivated by the difficulty of applying standard simulation techniques when identification constraints or theoretical considerations induce covariance restrictions in multivariate models. To deal with this difficulty, we build upon a decomposition of positive definite matrices and show that it leads to straightforward Markov chain Monte Carlo samplers for restricted covariance matrices.We introduce the approach by reviewing results for multivariate Gaussian models without...[Show more]
dc.contributor.author | Chan, Chi Chun (Joshua) | |
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dc.contributor.author | Jeliazkov, Ivan | |
dc.date.accessioned | 2015-12-08T22:22:33Z | |
dc.identifier.issn | 1061-8600 | |
dc.identifier.uri | http://hdl.handle.net/1885/32610 | |
dc.description.abstract | This article is motivated by the difficulty of applying standard simulation techniques when identification constraints or theoretical considerations induce covariance restrictions in multivariate models. To deal with this difficulty, we build upon a decomposition of positive definite matrices and show that it leads to straightforward Markov chain Monte Carlo samplers for restricted covariance matrices.We introduce the approach by reviewing results for multivariate Gaussian models without restrictions, where standard conjugate priors on the elements of the decomposition induce the usual Wishart distribution on the precision matrix and vice versa. The unrestricted case provides guidance for constructing efficient Metropolis-Hastings and accept-reject Metropolis-Hastings samplers in more complex settings, and we describe in detail how simulation can be performed under several important constraints. The proposed approach is illustrated in a simulation study and two applications in economics. Supplemental materials for this article (appendixes, data, and computer code) are available online. | |
dc.publisher | American Statistical Association | |
dc.source | Journal of Computational and Graphical Statistics | |
dc.subject | Keywords: Accept-reject metropolis-hastings algorithm; Bayesian estimation; Cholesky decomposition; Correlation matrix; Markov chain Monte Carlo; Metropolis- hastings algorithm; Multinomial probit; Multivariate probit; Unconstrained parameterization; Wishart distri | |
dc.title | MCMC Estimation of Restricted Covariance Matrices | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.identifier.citationvolume | 18 | |
dc.date.issued | 2009 | |
local.identifier.absfor | 140302 - Econometric and Statistical Methods | |
local.identifier.ariespublication | U9501697xPUB93 | |
local.type.status | Published Version | |
local.contributor.affiliation | Chan, Chi Chun (Joshua), College of Business and Economics, ANU | |
local.contributor.affiliation | Jeliazkov, Ivan, University of California | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.issue | 2 | |
local.bibliographicCitation.startpage | 457 | |
local.bibliographicCitation.lastpage | 480 | |
local.identifier.doi | 10.1198/jcgs.2009.08095 | |
local.identifier.absseo | 970101 - Expanding Knowledge in the Mathematical Sciences | |
dc.date.updated | 2016-02-24T12:00:51Z | |
local.identifier.scopusID | 2-s2.0-77950659074 | |
local.identifier.thomsonID | 000270063800012 | |
Collections | ANU Research Publications |
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