Solution and Estimation Methods for DSGE Models

245 Pages Posted: 12 Jan 2016

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

Frank Schorfheide

University of Pennsylvania - Department of Economics; Centre for Economic Policy Research (CEPR); National Bureau of Economic Research (NBER); University of Pennsylvania - The Penn Institute for Economic Research (PIER)

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Date Written: December 2015

Abstract

This paper provides an overview of solution and estimation techniques for dynamic stochastic general equilibrium (DSGE) models. We cover the foundations of numerical approximation techniques as well as statistical inference and survey the latest developments in the field.

Keywords: approximation error analysis, Bayesian inference, DSGE model, frequentist inference, GMM estimation, impulse response function matching, likelihood-based inference, Metropolis-Hastings algorithm, minimum distance estimation, particle filter, perturbation methods, projection methods, sequential Monte Carlo

JEL Classification: C11, C13, C32, C52, C61, C63, E32, E52

Suggested Citation

Fernández-Villaverde, Jesús and Rubio-Ramirez, Juan Francisco and Schorfheide, Frank, Solution and Estimation Methods for DSGE Models (December 2015). CEPR Discussion Paper No. DP11032, Available at SSRN: https://ssrn.com/abstract=2714292

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

Frank Schorfheide

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
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HOME PAGE: http://www.econ.upenn.edu/~schorf

Centre for Economic Policy Research (CEPR)

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United Kingdom

National Bureau of Economic Research (NBER) ( email )

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University of Pennsylvania - The Penn Institute for Economic Research (PIER) ( email )

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United States

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