Convergence properties of the likelihood of computed dynamic models

Published

Journal Article

This paper studies the econometrics of computed dynamic models. Since these models generally lack a closed-form solution, their policy functions are approximated by numerical methods. Hence, the researcher can only evaluate an approximated likelihood associated with the approximated policy function rather than the exact likelihood implied by the exact policy function. What are the consequences for inference of the use of approximated likelihoods¿ First, we find conditions under which, as the approximated policy function converges to the exact policy, the approximated likelihood also converges to the exact likelihood. Second, we show that second order approximation errors in the policy function, which almost always are ignored by researchers, have first order effects on the likelihood function. Third, we discuss convergence of Bayesian and classical estimates. Finally, we propose to use a likelihood ratio test as a diagnostic device for problems derived from the use of approximated likelihoods. © The Econometric Society 2006.

Full Text

Cited Authors

  • Fernández-Villaverde, J; Rubio-Ramírez, JF; Santos, MS

Published Date

  • January 1, 2006

Published In

Volume / Issue

  • 74 / 1

Start / End Page

  • 93 - 119

Electronic International Standard Serial Number (EISSN)

  • 1468-0262

International Standard Serial Number (ISSN)

  • 0012-9682

Digital Object Identifier (DOI)

  • 10.1111/j.1468-0262.2006.00650.x

Citation Source

  • Scopus