Estimating Dynamic Equilibrium Economies: Linear Versus Nonlinear Likelihood

37 Pages Posted: 4 Feb 2004

See all articles by Jesús Fernández-Villaverde

Jesús Fernández-Villaverde

University of Pennsylvania - Department of Economics; National Bureau of Economic Research (NBER)

Juan Francisco Rubio-Ramirez

Federal Reserve Bank of Atlanta - Research Department

Date Written: January 20, 2004

Abstract

This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a Sequential Monte Carlo filter proposed by Fernandez-Villaverde and Rubio-Ramirez (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

JEL Classification: C10, C11, C13, C15

Suggested Citation

Fernández-Villaverde, Jesús and Rubio-Ramirez, Juan Francisco, Estimating Dynamic Equilibrium Economies: Linear Versus Nonlinear Likelihood (January 20, 2004). Available at SSRN: https://ssrn.com/abstract=491163 or http://dx.doi.org/10.2139/ssrn.491163

Jesús Fernández-Villaverde (Contact Author)

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Juan Francisco Rubio-Ramirez

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