Bayesian Estimation of Macro-Finance DSGE Models with Stochastic Volatility

48 Pages Posted: 23 Oct 2019 Last revised: 23 Mar 2020

See all articles by David Rapach

David Rapach

Research Department, Federal Reserve Bank of Atlanta; Washington University in St. Louis

Fei Tan

Saint Louis University

Date Written: March 17, 2020

Abstract

We develop a Bayesian Markov chain Monte Carlo algorithm for estimating risk premia in dynamic stochastic general equilibrium (DSGE) models with stochastic volatility. Our approach is fully Bayesian and employs an affine solution strategy that makes estimation of large-scale DSGE models computationally feasible. We use our algorithm to estimate the US equity risk premium in a DSGE model that includes time-preference, technology, investment, and volatility shocks. Time-preference and technology shocks are primarily responsible for the sizable equity risk premium in the estimated DSGE model. The estimated historical stochastic volatility and equity risk premium series display pronounced countercyclical fluctuations.

Keywords: Stochastic volatility, Affine solution, Gibbs sampler, Equity risk premium, Structural shocks, Business cycle

JEL Classification: C11, C32, C58, E32, E44, G12

Suggested Citation

Rapach, David and Tan, Fei, Bayesian Estimation of Macro-Finance DSGE Models with Stochastic Volatility (March 17, 2020). Available at SSRN: https://ssrn.com/abstract=3469356 or http://dx.doi.org/10.2139/ssrn.3469356

David Rapach (Contact Author)

Research Department, Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States

Washington University in St. Louis

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://https://sites.google.com/slu.edu/daverapach

Fei Tan

Saint Louis University ( email )

3674 Lindell Boulevard
Saint Louis, MO 63108
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
160
Abstract Views
918
Rank
276,579
PlumX Metrics