There is now a very large literature on dynamic models in marketing. In a narrow sense, dynamics can be understood as a mechanism whereby past product purchases affect a person's current evaluation of the utility he/she will obtain from buying a product. Most of the prior literature has focussed...

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...

We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the...