Estimating Nonlinear Dynamic Equilibrium economies: A Likelihood Approach

Jesus Fernandez-Villaverde and Juan F Rubio-Ramirez

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: This paper presents a framework to undertake likelihood-based inference in nonlinear dynamic equilibrium economies. We develop a Sequential Monte Carlo algorithm that delivers an estimate of the likelihood function of the model using simulation methods. This likelihood can be used for parameter estimation and for model comparison. The algorithm can deal both with nonlinearities of the economy and with the presence of non-normal shocks. We show consistency of the estimate and its good performance in finite simulations. This new algorithm is important because the existing empirical literature that wanted to follow a likelihood approach was limited to the estimation of linear models with Gaussian innovations. We apply our procedure to estimate the structural parameters of the neoclassical growth model.

Keywords: Likelihood-Based Inference; Dynamic Equilibrium Economies; Nonlinear Filtering; Sequential Monte Carlo) (search for similar items in EconPapers)
JEL-codes: C10 C11 C13 C15 (search for similar items in EconPapers)
Pages: 56 pages
Date: 2004-01-06
New Economics Papers: this item is included in nep-cmp and nep-dge
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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Working Paper: Estimating nonlinear dynamic equilibrium economies: a likelihood approach (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:04-001

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