Convergence properties of the likelihood of computed dynamic models

Jesus Fernandez-Villaverde (), Juan F Rubio-Ramirez () and Manuel Santos

No 2004-27, Working Paper from Federal Reserve Bank of Atlanta

Abstract: This paper studies the econometrics of computed dynamic models. Since these models generally lack a closed-form solution, economists approximate the policy functions of the agents in the model with numerical methods. But this implies that, instead of the exact likelihood function, the researcher can evaluate only an approximated likelihood associated with the approximated policy function. What are the consequences for inference of the use of approximated likelihoods? First, we show that as the approximated policy function converges to the exact policy, the approximated likelihood also converges to the exact likelihood. Second, we prove that the approximated likelihood converges at the same rate as the approximated policy function. Third, we find that the error in the approximated likelihood gets compounded with the size of the sample. Fourth, we discuss convergence of Bayesian and classical estimates. We complete the paper with three applications to document the quantitative importance of our results.

New Economics Papers: this item is included in nep-cmp, nep-dge and nep-ets
Date: 2004
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Related works:
Working Paper: Convergence Properties of the Likelihood of Computed Dynamic Models (2005)
Working Paper: Convergence Properties of the Likelihood of Computed Dynamic Models (2005)
Working Paper: Convergence Properties of the Likelihood of Computed Dynamic Models (2004)
Journal Article: Convergence Properties of the Likelihood of Computed Dynamic Models (2006)
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