A practitioner's guide to Bayesian estimation of discrete choice dynamic programming models

Ching, AT; Imai, S; Ishihara, M; Jain, N

A practitioner's guide to Bayesian estimation of discrete choice dynamic programming models

Ching, AT Imai, S

Ishihara, M Jain, N

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Publication Type:: Journal Article
Citation:: Quantitative Marketing and Economics, 2012, 10 (2), pp. 151 - 196
Issue Date:: 2012-06-01

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Field	Value	Language
dc.contributor.author	Ching, AT	en_US
dc.contributor.author	Imai, S https://orcid.org/0000-0001-9824-7922	en_US
dc.contributor.author	Ishihara, M	en_US
dc.contributor.author	Jain, N	en_US
dc.date.issued	2012-06-01	en_US
dc.identifier.citation	Quantitative Marketing and Economics, 2012, 10 (2), pp. 151 - 196	en_US
dc.identifier.issn	1570-7156	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28601
dc.description.abstract	This paper provides a step-by-step guide to estimating infinite horizon discrete choice dynamic programming (DDP) models using a new Bayesian estimation algorithm (Imai et al., Econometrica 77:1865-1899, 2009a) (IJC). In the conventional nested fixed point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the IJC algorithm extensively uses the computational results obtained from the past iterations to help solve the DDP model at the current iterated parameter values. Consequently, it has the potential to significantly alleviate the computational burden of estimating DDP models. To illustrate this new estimation method, we use a simple dynamic store choice model where stores offer "frequent- buyer" type rewards programs.Our Monte Carlo results demonstrate that the IJC method is able to recover the true parameter values of this model quite precisely. We also show that the IJC method could reduce the estimation time significantly when estimating DDP models with unobserved heterogeneity, especially when the discount factor is close to 1. © Springer Science+Business Media, LLC 2012.	en_US
dc.relation.ispartof	Quantitative Marketing and Economics	en_US
dc.relation.isbasedon	10.1007/s11129-012-9119-6	en_US
dc.subject.classification	Marketing	en_US
dc.title	A practitioner's guide to Bayesian estimation of discrete choice dynamic programming models	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	10	en_US
utslib.for	140206 Experimental Economics	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1505 Marketing	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	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

This paper provides a step-by-step guide to estimating infinite horizon discrete choice dynamic programming (DDP) models using a new Bayesian estimation algorithm (Imai et al., Econometrica 77:1865-1899, 2009a) (IJC). In the conventional nested fixed point algorithm, most of the information obtained in the past iterations remains unused in the current iteration. In contrast, the IJC algorithm extensively uses the computational results obtained from the past iterations to help solve the DDP model at the current iterated parameter values. Consequently, it has the potential to significantly alleviate the computational burden of estimating DDP models. To illustrate this new estimation method, we use a simple dynamic store choice model where stores offer "frequent- buyer" type rewards programs.Our Monte Carlo results demonstrate that the IJC method is able to recover the true parameter values of this model quite precisely. We also show that the IJC method could reduce the estimation time significantly when estimating DDP models with unobserved heterogeneity, especially when the discount factor is close to 1. © Springer Science+Business Media, LLC 2012.

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http://hdl.handle.net/10453/28601