Evaluating Real Business Cycle Models Using Likelihood Methods

35 Pages Posted: 13 Apr 2001

See all articles by John Landon-Lane

John Landon-Lane

Rutgers University, New Brunswick/Piscataway - Faculty of Arts and Sciences-New Brunswick/Piscataway - Department of Economics

Date Written: October 2000

Abstract

This paper develops a method that uses a likelihood approach to directly compare two or more non-nested dynamic, stochastic general equilibrium (DSGE) models. It is shown how DSGE models can be compared across the whole sample and how this measure can be decomposed across individual observations thus allowing models to be compared across any sub-sample of the data. The method is applied to the problem of determining whether the technology shock process in a standard Real Business Cycle model should consist of permanent or temporary, albeit persistent, shocks. Overall, a permanent shock model has a better prediction performance than the temporary shock model. However, the model with the temporary shock performs much better for the part of the sample that includes the most of the 1980's and the 1990's.

Keywords: Real Business Cycles, Model Evaluation, Markov chain Monte Carlo, Bayes Factor

JEL Classification: C11, C52, E32

Suggested Citation

Landon-Lane, John, Evaluating Real Business Cycle Models Using Likelihood Methods (October 2000). Available at SSRN: https://ssrn.com/abstract=264908 or http://dx.doi.org/10.2139/ssrn.264908

John Landon-Lane (Contact Author)

Rutgers University, New Brunswick/Piscataway - Faculty of Arts and Sciences-New Brunswick/Piscataway - Department of Economics ( email )

75 Hamilton Street
New Brunswick, NJ 08901
United States

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