NDLI: A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models

Content Provider	Springer Nature Link
Author	Ching, Andrew T. Imai, Susumu Ishihara, Masakazu Jain, Neelam
Copyright Year	2012
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.
Starting Page	151
Ending Page	196
Page Count	46
File Format	PDF
ISSN	15707156
Journal	Quantitative Marketing and Economics
Volume Number	10
Issue Number	2
e-ISSN	1573711X
Language	English
Publisher	Springer US
Publisher Date	2012-02-19
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian estimation Dynamic programming Discrete choice models Rewards programs Statistics for Business/Economics/Mathematical Finance/Insurance Marketing Economic Theory
Content Type	Text
Resource Type	Article
Subject	Economics, Econometrics and Finance Marketing

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