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.
Original language | English (US) |
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Pages (from-to) | 151-196 |
Number of pages | 46 |
Journal | Quantitative Marketing and Economics |
Volume | 10 |
Issue number | 2 |
DOIs | |
State | Published - Jun 2012 |
Externally published | Yes |
Keywords
- Bayesian estimation
- Discrete choice models
- Dynamic programming
- Rewards programs
ASJC Scopus subject areas
- Economics, Econometrics and Finance (miscellaneous)
- Marketing