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Measuring business cycle features

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  • 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).

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Bibliographic Info

Paper provided by Federal Reserve Bank of Kansas City in its series Research Working Paper with number 95-10.

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Date of creation: 1995
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Handle: RePEc:fip:fedkrw:95-10

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Keywords: Business cycles ; Random walks (Mathematics);

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Cited by:
  1. Hans-Martin Krolzig & Michael Clements, 2000. "Business Cycle Asymmetries: Characterisation and Testing based on Markov-Switching Autoregressions," Economics Series Working Papers 2000-W32, University of Oxford, Department of Economics.
  2. Krolzig, H.-M. & Toro, J., 2001. "Classical And Modern Business Cycle Measurement: The European Case," Economics Series Working Papers 9960, University of Oxford, Department of Economics.
  3. Bertrand Candelon & Luis A. Gil-Alana, 2004. "Fractional integration and business cycle features," Empirical Economics, Springer, vol. 29(2), pages 343-359, 05.

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