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Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood

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  • Jesus Fernandez-Villaverde

    ()
    (Department of Economics, University of Pennsylvania)

  • Juan F. Rubio-Ramirez

    ()
    (Federal Reserve Bank of Atlanta)

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.

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Bibliographic Info

Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 04-005.

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Length: 37 pages
Date of creation: 20 Jan 2004
Date of revision:
Handle: RePEc:pen:papers:04-005

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Keywords: Likelihood-Based Inference; Dynamic Equilibrium Economies; Nonlinear Filtering; Kalman Filter; Sequential Monte Carlo;

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