Estimating Dynamic Equilibrium Models with Stochastic Volatility

71 Pages Posted: 15 Sep 2012 Last revised: 10 Oct 2024

See all articles by Jesús Fernández-Villaverde

Jesús Fernández-Villaverde

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

Pablo Guerrón-Quintana

Federal Reserve Banks - Federal Reserve Bank of Philadelphia

Juan Francisco Rubio-Ramirez

Federal Reserve Bank of Atlanta - Research Department

Multiple version iconThere are 2 versions of this paper

Date Written: September 2012

Abstract

We propose a novel method to estimate dynamic equilibrium models with stochastic volatility. First, we characterize the properties of the solution to this class of models. Second, we take advantage of the results about the structure of the solution to build a sequential Monte Carlo algorithm to evaluate the likelihood function of the model. The approach, which exploits the profusion of shocks in stochastic volatility models, is versatile and computationally tractable even in large-scale models, such as those often employed by policy-making institutions. As an application, we use our algorithm and Bayesian methods to estimate a business cycle model of the U.S. economy with both stochastic volatility and parameter drifting in monetary policy. Our application shows the importance of stochastic volatility in accounting for the dynamics of the data.

Suggested Citation

Fernández-Villaverde, Jesús and Guerron-Quintana, Pablo and Rubio-Ramirez, Juan Francisco, Estimating Dynamic Equilibrium Models with Stochastic Volatility (September 2012). NBER Working Paper No. w18399, Available at SSRN: https://ssrn.com/abstract=2147109

Jesús Fernández-Villaverde (Contact Author)

University of Pennsylvania - Department of Economics ( email )

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Pablo Guerron-Quintana

Federal Reserve Banks - Federal Reserve Bank of Philadelphia ( email )

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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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