Bayesian estimation of dynamic discrete choice models

Imai, S; Jain, N; Ching, A

Bayesian estimation of dynamic discrete choice models

Imai, S

Jain, N Ching, A

Permalink

Publication Type:: Journal Article
Citation:: Econometrica, 2009, 77 (6), pp. 1865 - 1899
Issue Date:: 2009-11-01

Closed Access

	Filename	Description	Size
	2012002059OK.pdf		295.81 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Imai, S https://orcid.org/0000-0001-9824-7922	en_US
dc.contributor.author	Jain, N	en_US
dc.contributor.author	Ching, A	en_US
dc.date.issued	2009-11-01	en_US
dc.identifier.citation	Econometrica, 2009, 77 (6), pp. 1865 - 1899	en_US
dc.identifier.issn	0012-9682	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29063
dc.description.abstract	We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the dynamic programming (DP) solution algorithm with the Bayesian Markov chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though the number of grid points on the state variable is small per solution-estimation iteration, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "curse of dimensionality." We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values. © 2009 The Econometric Society.	en_US
dc.relation.ispartof	Econometrica	en_US
dc.relation.isbasedon	10.3982/ECTA5658	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Bayesian estimation of dynamic discrete choice models	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	77	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	1401 Economic Theory	en_US
utslib.for	1402 Applied Economics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	77	en_US

Abstract:

We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the dynamic programming (DP) solution algorithm with the Bayesian Markov chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though the number of grid points on the state variable is small per solution-estimation iteration, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "curse of dimensionality." We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values. © 2009 The Econometric Society.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/29063