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Business cycle turning points: two empirical business cycle model approaches

Andrew Filardo and Stephen Gordon

No 95-15, Research Working Paper from 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).

Keywords: Business; cycles (search for similar items in EconPapers)
Date: 1995, Revised 1995
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Handle: RePEc:fip:fedkrw:95-15