Econometrica: Nov 2009, Volume 77, Issue 6

Bayesian Estimation of Dynamic Discrete Choice Models

https://doi.org/10.3982/ECTA5658
p. 1865-1899

Susumu Imai, Neelam Jain, Andrew Ching

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 parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though the number of grid points on the state variable is small per solution‐estimation iteration, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the “curse of dimensionality.” We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values.

Log In To View Full Content

Supplemental Material

Supplement to "Bayesian Estimation of Dynamic Discrete Choice Models"

PDF file that contains three appendices that include results of experiments and proofs.

Read More View PDF


Supplement to "Bayesian Estimation of Dynamic Discrete Choice Models"

A zip file containing a readme.txt providing details of how the results can be replicated using the other files provided.

Read More View ZIP


Back