Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood

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 compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a Sequential Monte Carlo filter proposed by Fernández-Villaverde and Rubio-Ramírez (2004) and the Kalman filter. The Sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. We report two main results. First, both for simulated and for real data, the Sequential Monte Carlo filter delivers a substantially better fit of the model to the data as measured by the marginal likelihood. This is true even for a nearly linear case. Second, the differences in terms of point estimates, even if relatively small in absolute values, have important effects on the moments of the model. We conclude that the nonlinear filter is a superior procedure for taking models to the data.

Keywords: Likelihood-Based Inference; Dynamic Equilibrium Economies; Nonlinear Filtering; Kalman Filter; Sequential Monte Carlo (search for similar items in EconPapers)
JEL-codes: C10 C11 C13 C15 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2004-01-20
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 (12)

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Related works:
Journal Article: Estimating dynamic equilibrium economies: linear versus nonlinear likelihood (2005)
Working Paper: Estimating dynamic equilibrium economies: linear versus nonlinear likelihood (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:04-005

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