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, Jain and Ching, Econometrica 77:1865-1899, 2009) (IJC). In the conventional nested fixed point algorithm, most of the...

This paper provides a step-by-step guide to estimating discrete choice dynamic programming (DDP) models using the Bayesian Dynamic Programming algorithm developed in Imai, Jain and Ching (2008) (IJC). The IJC method combines the DDP solution algorithm with the Bayesian Markov Chain Monte Carlo...

For information/digital products, the used goods market has been viewed as a threat by producers. However, it is not clear if this view is justified because the used goods market also provides owners with an opportunity to sell their products. To investigate the impact of the used goods market...

We develop a Bayesian Markov chain Monte Carlo (MCMC) algorithm for estimating finite-horizon discrete choice dynamic programming (DDP) models. The proposed algorithm has the potential to reduce the computational burden significantly when some of the state variables are continuous. In a...

In "Marketing Information: A Competitive Analysis,'' Sarvary and Parker (1997) (S&P) [Marketing Science, 16(1): 24-38] argue that in part of the parameter space that they considered, a reduction in the price of one information product can lead to an increase in demand for another information...