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The importance of nonlinearity in reproducing business cycle features

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  • James Morley
  • Jeremy M. Piger

Abstract

This paper considers the ability of simulated data from linear and nonlinear time-series models to reproduce features in U.S. real GDP data related to business cycle phases. We focus our analysis on a number of linear ARIMA models and nonlinear Markov-switching models. To determine the timing of business cycle phases for the simulated data, we present a model-free algorithm that is more successful than previous methods at matching NBER dates and associated features in the postwar data. We find that both linear and Markov-switching models are able to reproduce business cycle features such as the average growth rate in recessions, the average length of recessions, and the total number of recessions. However, we find that Markov-switching models are better than linear models at reproducing the variability of growth rates in different business cycle phases. Furthermore, certain Markov-switching specifications are able to reproduce high-growth recoveries following recessions and a strong correlation between the severity of a recession and the strength of the subsequent recovery. Thus, we conclude that nonlinearity is important in reproducing business cycle features.

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Bibliographic Info

Paper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 2004-032.

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Date of creation: 2005
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Publication status: Published in Nonlinear Time Series Analysis of Business Cycles, edited by C. Milas, P. Rothman, and D. van Dijk. Elsevier Science, Amsterdam, 2006
Handle: RePEc:fip:fedlwp:2004-032

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Keywords: Business cycles;

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References

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  1. Jeremy Piger & James Morley & Chang-Jin Kim, 2005. "Nonlinearity and the permanent effects of recessions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 20(2), pages 291-309.
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Citations

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Cited by:
  1. Antonio Matas-Mir & Denise R. Osborn & Marco J. Lombardi, 2008. "The effect of seasonal adjustment on the properties of business cycle regimes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 23(2), pages 257-278.
  2. Morley James & Piger Jeremy & Tien Pao-Lin, 2013. "Reproducing business cycle features: are nonlinear dynamics a proxy for multivariate information?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 483-498, December.
  3. David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Jean-Francois Richard, 2008. "Exploiting Non-Linearities in GDP Growth for Forecasting and Anticipating Regime Changes," Working Papers, University of Pittsburgh, Department of Economics 367, University of Pittsburgh, Department of Economics, revised Sep 2008.
  4. James Morley & Jeremy Piger & Pao-Lin Tien, 2009. "Reproducing Business Cycle Features: How Important Is Nonlinearity Versus Multivariate Information?," Wesleyan Economics Working Papers 2009-003, Wesleyan University, Department of Economics.
  5. Mark W. French, 2005. "A nonlinear look at trend MFP growth and the business cycle: result from a hybrid Kalman/Markov switching model," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 2005-12, Board of Governors of the Federal Reserve System (U.S.).
  6. Andrew T. Levin & J. David López-Salido & Edward Nelson & Tack Yun, 2008. "Macroeconometric equivalence, microeconomic dissonance, and the design of monetary policy," Working Papers, Federal Reserve Bank of St. Louis 2008-035, Federal Reserve Bank of St. Louis.
  7. Frédérick Demers & Ryan Macdonald, 2007. "The Canadian Business Cycle: A Comparison of Models," Working Papers 07-38, Bank of Canada.

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