A Practitioner's Guide to Bayesian Estimation of Discrete Choice Dynamic Programming Models
AbstractThis paper provides a step-by-step guide to estimating discrete choice dynamic programming (DDP) models using the Bayesian Dynamic Programming algorithm developed by Imai Jain and Ching (2008) (IJC). The IJC method combines the DDP solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm, which solves the DDP model and estimates its structural parameters simultaneously. The main computational advantage of this estimation algorithm is the efficient use of information obtained from the past iterations. 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 Bayesian Dynamic Programming algorithm extensively uses the computational results obtained from the past iterations to help solving the DDP model at the current iterated parameter values. Consequently, it significantly alleviates the computational burden of estimating a DDP model. We carefully discuss how to implement the algorithm in practice, and use a simple dynamic store choice model to illustrate how to apply this algorithm to obtain parameter estimates.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Queen's University, Department of Economics in its series Working Papers with number 1201.
Length: 49 pages
Date of creation: Apr 2009
Date of revision:
Bayesian Dynamic Programming; Discrete Choice Dynamic Programming; Markov Chain Monte Carlo;
Other versions of this item:
- Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics, Springer, vol. 10(2), pages 151-196, June.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- M3 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-04-18 (All new papers)
- NEP-CBA-2009-04-18 (Central Banking)
- NEP-DCM-2009-04-18 (Discrete Choice Models)
- NEP-ECM-2009-04-18 (Econometrics)
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
- Zhou, Yiyi, 2012. "Failure to Launch in Two-Sided Markets: A Study of the U.S. Video Game Market," MPRA Paper 42002, University Library of Munich, Germany.
- Amoroso, S., 2013. "Heterogeneity of innovative, collaborative, and productive firm-level processes," Open Access publications from Tilburg University urn:nbn:nl:ui:12-5663713, Tilburg University.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mark Babcock).
If references are entirely missing, you can add them using this form.