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A Nonlinear Forecasting Model of GDP Growth

Author

Listed:
  • David N. DeJong

    (University of Pittsburgh)

  • Roman Liesenfeld

    (Universität Kiel)

  • Jean-François Richard

    (University of Pittsburgh)

Abstract

We develop a model of GDP growth under which regime changes are triggered stochastically by an observable tension index, constructed as the geometric sum of deviations of actual GDP growth from a corresponding sustainable rate. Within expansionary regimes, the tension index tends to increase, which heightens the probability of a regime change. Given a regime change, the process becomes reversed, and the tension index begins to decline along a newly established path. Linking the behavior of the tension index to GDP growth enables us to capture floor and ceiling effects. © 2005 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • David N. DeJong & Roman Liesenfeld & Jean-François Richard, 2005. "A Nonlinear Forecasting Model of GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 697-708, November.
    • Handle: RePEc:tpr:restat:v:87:y:2005:i:4:p:697-708
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    Cited by:

    1. Duncan, Roberto, 2016. "Does the US current account show a symmetric behavior over the business cycle?," International Review of Economics & Finance, Elsevier, vol. 41(C), pages 202-219.
    2. Marta Boczon, 2018. "Balanced Growth Approach to Forecasting Recessions," Working Paper 6487, Department of Economics, University of Pittsburgh.
    3. Christian Glocker & Philipp Wegmueller, 2020. "Business cycle dating and forecasting with real-time Swiss GDP data," Empirical Economics, Springer, vol. 58(1), pages 73-105, January.
    4. Marta Boczoń & Jean-François Richard, 2020. "Balanced Growth Approach to Tracking Recessions," Econometrics, MDPI, vol. 8(2), pages 1-35, April.
    5. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    6. Golosnoy, Vasyl & Hogrefe, Jens, 2009. "Sequential methodology for signaling business cycle turning points," Kiel Working Papers 1528, Kiel Institute for the World Economy (IfW Kiel).
    7. Chan, Wai-Sum, 2022. "On temporal aggregation of some nonlinear time-series models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 38-49.

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