Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/100989 
Year of Publication: 
2004
Series/Report no.: 
Working Paper No. 2004-27
Publisher: 
Federal Reserve Bank of Atlanta, Atlanta, GA
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.
Document Type: 
Working Paper

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