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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.

Suggested Citation

  • James Morley & Jeremy M. Piger, 2005. "The importance of nonlinearity in reproducing business cycle features," Working Papers 2004-032, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:2004-032
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    Cited by:

    1. 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.
    2. Shushanik Papanyan, 2015. "Digitization and Productivity: Measuring Cycles of Technological Progress," Working Papers 15/33, BBVA Bank, Economic Research Department.
    3. Pérez-Quirós, Gabriel & Gadea Rivas, Maria Dolores & Gomez-Loscos, Ana, 2014. "The Two Greatest. Great Recession vs. Great Moderation," CEPR Discussion Papers 10092, C.E.P.R. Discussion Papers.
    4. Levin, Andrew T. & David López-Salido, J. & Nelson, Edward & Yun, Tack, 2008. "Macroeconometric equivalence, microeconomic dissonance, and the design of monetary policy," Journal of Monetary Economics, Elsevier, vol. 55(Supplemen), pages 48-62, October.
    5. Nissilä, Wilma, 2020. "Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland," BoF Economics Review 7/2020, Bank of Finland.
    6. Maximo Camacho & Gabriel Perez‐Quiros & Pilar Poncela, 2015. "Extracting Nonlinear Signals from Several Economic Indicators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1073-1089, November.
    7. Frédérick Demers & Ryan Macdonald, 2007. "The Canadian Business Cycle: A Comparison of Models," Staff Working Papers 07-38, Bank of Canada.
    8. 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., vol. 23(2), pages 257-278.
    9. Giordani, Paolo & Kohn, Robert & van Dijk, Dick, 2007. "A unified approach to nonlinearity, structural change, and outliers," Journal of Econometrics, Elsevier, vol. 137(1), pages 112-133, March.
    10. Viktor Winschel, 2005. "Solving, Estimating and Selecting Nonlinear Dynamic Economic Models without the Curse of Dimensionality," GE, Growth, Math methods 0507014, University Library of Munich, Germany.
    11. 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 2005-12, Board of Governors of the Federal Reserve System (U.S.).
    12. 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.
    13. David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Jean-Francois Richard, 2008. "Exploiting Non-Linearities in GDP Growth for Forecasting and Anticipating Regime Changes," Working Paper 367, Department of Economics, University of Pittsburgh, revised Sep 2008.

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