Estimating Macroeconomic Models: A Likelihood Approach

56 Pages Posted: 6 Jun 2006

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

Multiple version iconThere are 2 versions of this paper

Date Written: March 2006

Abstract

This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. The models can be nonlinear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing preferences and technology, and to compare different economies. Both tasks can be implemented from either a classical or a Bayesian perspective. We illustrate the technique by estimating a business cycle model with investment-specific technological change, preference shocks, and stochastic volatility.

Keywords: Dynamic macroeconomic models, particle filtering, nonlinear and/or non-normal models, business cycle, stochastic volatility

JEL Classification: C11, C5, E10, E32

Suggested Citation

Fernández-Villaverde, Jesús and Rubio-Ramirez, Juan Francisco, Estimating Macroeconomic Models: A Likelihood Approach (March 2006). CEPR Discussion Paper No. 5513. Available at SSRN: https://ssrn.com/abstract=906785

Jesús Fernández-Villaverde (Contact Author)

University of Pennsylvania - Department of Economics ( email )

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National Bureau of Economic Research (NBER)

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Juan Francisco Rubio-Ramirez

Federal Reserve Bank of Atlanta - Research Department ( email )

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HOME PAGE: http://www.econ.umn.edu/~rubio

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