Business Cycle Turning Points: Two Empirical Business Cycle Model Approaches

Andrew J. Filardo
Stephen F. Gordon
December 1995
RWP 95-15
Research Division
Federal Reserve Bank of Kansas City

ABSTRACT

This paper compares a set of non-nested empirical business cycle models. The alternative linear models include a VAR and Stock and Watson's (1991) unobserved components model. The alternative nonlinear models include the time-varying transition probability Markov switching model (Filardo 1993) and an integration of the Markov switching model with the Stock and Watson model as proposed by Diebold and Rudebusch (1994) and Chauvet (1994). Generally, this paper finds that no one model dominates in a predictive sense at all times. The nonlinear models, however, tend to outperform the linear models around business cycle turning points. Econometrically, this paper applies the general model comparison methodology of Geweke (1994).

Andrew J. Filardo is a senior economist at the Federal Reserve Bank of Kansas City. Stephen F. Gordon is an assistant professor at University Laval. The views expressed herein are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Kansas City or of the Federal Reserve System.

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