Exact inference for continuous time markov chain models

John Geweke; Robert C. Marshall; Gary A. Zarkin

doi:10.2307/2297610

Exact inference for continuous time markov chain models

John Geweke
, Robert C. Marshall
, Gary A. Zarkin

Economics

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

Methods for exact Bayesian inference under a uniform diffuse prior are set forth for the continuous time homogeneous Markov chain model. It is shown how the exact posterior distribution of any function of interest may be computed using Monte Carlo integration. The solution handles the problems of embeddability in a very natural way, and provides (to our knowledge) the only solution that systematically takes this problem into account. The methods are illustrated using several sets of data.

Original language	English (US)
Pages (from-to)	653-669
Number of pages	17
Journal	Review of Economic Studies
Volume	53
Issue number	4
DOIs	https://doi.org/10.2307/2297610
State	Published - Aug 1986

All Science Journal Classification (ASJC) codes

Economics and Econometrics

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10.2307/2297610

Cite this

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title = "Exact inference for continuous time markov chain models",

abstract = "Methods for exact Bayesian inference under a uniform diffuse prior are set forth for the continuous time homogeneous Markov chain model. It is shown how the exact posterior distribution of any function of interest may be computed using Monte Carlo integration. The solution handles the problems of embeddability in a very natural way, and provides (to our knowledge) the only solution that systematically takes this problem into account. The methods are illustrated using several sets of data.",

author = "John Geweke and Marshall, \{Robert C.\} and Zarkin, \{Gary A.\}",

note = "Funding Information: We wish to acknowledge helpful discussions with Halina Frydman and Burton Singer while accepting responsibility for any remaining errors. Gewecke acknowledges financial support from National Science Foundation grants SES-8318778 and SES-8605867. Marshall acknowledges financial support from NSF grant SES-8509693. A grant from the Pew Foundation provided financial support for computing. The methodology described in this paper was developed by the first author, in jointly undertaking the applications in the last section which were proposed by the other authors. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.",

year = "1986",

month = aug,

doi = "10.2307/2297610",

language = "English (US)",

volume = "53",

pages = "653--669",

journal = "Review of Economic Studies",

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AU - Geweke, John

AU - Marshall, Robert C.

AU - Zarkin, Gary A.

N1 - Funding Information: We wish to acknowledge helpful discussions with Halina Frydman and Burton Singer while accepting responsibility for any remaining errors. Gewecke acknowledges financial support from National Science Foundation grants SES-8318778 and SES-8605867. Marshall acknowledges financial support from NSF grant SES-8509693. A grant from the Pew Foundation provided financial support for computing. The methodology described in this paper was developed by the first author, in jointly undertaking the applications in the last section which were proposed by the other authors. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.

PY - 1986/8

Y1 - 1986/8

N2 - Methods for exact Bayesian inference under a uniform diffuse prior are set forth for the continuous time homogeneous Markov chain model. It is shown how the exact posterior distribution of any function of interest may be computed using Monte Carlo integration. The solution handles the problems of embeddability in a very natural way, and provides (to our knowledge) the only solution that systematically takes this problem into account. The methods are illustrated using several sets of data.

AB - Methods for exact Bayesian inference under a uniform diffuse prior are set forth for the continuous time homogeneous Markov chain model. It is shown how the exact posterior distribution of any function of interest may be computed using Monte Carlo integration. The solution handles the problems of embeddability in a very natural way, and provides (to our knowledge) the only solution that systematically takes this problem into account. The methods are illustrated using several sets of data.

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JF - Review of Economic Studies

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ER -

Exact inference for continuous time markov chain models

Abstract

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