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Business cycle turning points: two empirical business cycle model approaches

Author

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  • Andrew J. Filardo
  • Stephen F. Gordon

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).

Suggested Citation

  • Andrew J. Filardo & Stephen F. Gordon, 1995. "Business cycle turning points: two empirical business cycle model approaches," Research Working Paper 95-15, Federal Reserve Bank of Kansas City.
  • Handle: RePEc:fip:fedkrw:95-15
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    Citations

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    Cited by:

    1. Kim, Chang-Jin & Nelson, Charles R, 2001. "A Bayesian Approach to Testing for Markov-Switching in Univariate and Dynamic Factor Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(4), pages 989-1013, November.
    2. Francis X. Diebold & Glenn D. Rudebusch, 2001. "Five questions about business cycles," Economic Review, Federal Reserve Bank of San Francisco, pages 1-15.
    3. Randal J. Verbrugge, 1998. "A cross-country investigation of macroeconomic asymmetries," Macroeconomics 9809017, EconWPA, revised 30 Sep 1998.
    4. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.

    More about this item

    Keywords

    Business cycles;

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