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Measuring business cycle features

Author

Listed:
  • Gregory D. Hess
  • Shigeru Iwata

Abstract

Since the extensive work by Burns and Mitchell (1947), many economists have interpreted economic fluctuations in terms of business cycle phases. Given this, we argue that in addition to usual model selection criteria currently used in the profession, the adequacy of a univariate macroeconomic time series model should be based on its ability to replicate two most important business cycle features of the U.S. data--duration and amplitude. We propose a number of checks for whether univariate statistical models generate business cycle features observed in US GDP and find that many popular non-linear models for the log of real GDP are no better at replicating the duration and amplitude features of the data than a simple ARIMA(1,1,0).

Suggested Citation

  • Gregory D. Hess & Shigeru Iwata, 1995. "Measuring business cycle features," Research Working Paper 95-10, Federal Reserve Bank of Kansas City.
    • Handle: RePEc:fip:fedkrw:95-10
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    Citations

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    Cited by:

    1. Clements, Michael P & Krolzig, Hans-Martin, 2003. "Business Cycle Asymmetries: Characterization and Testing Based on Markov-Switching Autoregressions," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 196-211, January.
    2. Bertrand Candelon & Luis A. Gil-Alana, 2004. "Fractional integration and business cycle features," Empirical Economics, Springer, vol. 29(2), pages 343-359, May.
    3. Hans-Martin Krolzig & Juan Toro, 2004. "Classical and modern business cycle measurement: The European case," Spanish Economic Review, Springer;Spanish Economic Association, vol. 7(1), pages 1-21, January.

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