Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity

Published

Journal Article

We adapt the expectation-maximization algorithm to incorporate unobserved heterogeneity into conditional choice probability (CCP) estimators of dynamic discrete choice problems. The unobserved heterogeneity can be time-invariant or follow a Markov chain. By developing a class of problems where the difference in future value terms depends on a few conditional choice probabilities, we extend the class of dynamic optimization problems where CCP estimators provide a computationally cheap alternative to full solution methods. Monte Carlo results confirm that our algorithms perform quite well, both in terms of computational time and in the precision of the parameter estimates. © 2011 The Econometric Society.

Full Text

Duke Authors

Cited Authors

  • Arcidiacono, P; Miller, RA

Published Date

  • November 1, 2011

Published In

Volume / Issue

  • 79 / 6

Start / End Page

  • 1823 - 1867

Electronic International Standard Serial Number (EISSN)

  • 1468-0262

International Standard Serial Number (ISSN)

  • 0012-9682

Digital Object Identifier (DOI)

  • 10.3982/ECTA7743

Citation Source

  • Scopus