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A Nonlinear Model of the Business Cycle

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  • Simon M. Potter
  • Edward E. Leamer

Abstract

The usual index of leading indicators has constant weights on its components and is therefore implicitly premised on the assumption that the dynamical properties of the economy remain the same over time and across phases of the business cycle. We explore the possibility that the business cycle has phases, for example, recessions, recoveries and normal growth, each with its unique dynamics. Based on this possibility we develop a nonlinear model of the business cycle that combines a number of previous approaches. We model the state of the economy as a latent variable with a threshold autoregression structure. In addition to dependence on its own lags the latent variable is also determined by observed economic and financial variables. In turn these variables are modeled as following a nonlinear vector autoregression with regimes defined by the latent business cycle variable. A Markov Chain Monte Carlo algorithm is developed to estimate the model. Special attention is paid to specification of prior distributions given the large dimension of the model. We also investigate using the business cycle chronology of the NBER to aid in the classification of the latent variable. The two main empirical objectives of the model are to provide more accurate predictions of economic variables particularly at turning points and to describe how the dynamics differ across business cycle phases

Suggested Citation

  • Simon M. Potter & Edward E. Leamer, 2004. "A Nonlinear Model of the Business Cycle," Econometric Society 2004 North American Winter Meetings 490, Econometric Society.
    • Handle: RePEc:ecm:nawm04:490
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    Cited by:

    1. Ryo Horii & Yoshiyasu Ono, 2006. "Learning, Inflation Cycles, and Depression," Discussion Papers in Economics and Business 06-14, Osaka University, Graduate School of Economics.
    2. Ryo Horii & Yoshiyasu Ono, 2022. "Financial crisis and slow recovery with Bayesian learning agents," International Journal of Economic Theory, The International Society for Economic Theory, vol. 18(4), pages 578-606, December.
    3. Marcelle, Chauvet & Simon, Potter, 2007. "Monitoring Business Cycles with Structural Breaks," MPRA Paper 15097, University Library of Munich, Germany, revised 31 Apr 2009.
    4. Horii, Ryo & Ono, Yoshiyasu, 2009. "Information Cycles and Depression in a Stochastic Money-in-Utility Model," MPRA Paper 13485, University Library of Munich, Germany.
    5. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
    6. Ryo Horii & Yoshiyasu Ono, 2005. "Financial Crisis and Recovery: Learning-based Liquidity Preference Fluctuations," Macroeconomics 0504016, University Library of Munich, Germany.
    7. Ryo Horii & Yoshiyasu Ono, 2004. "Learning, Liquidity Preference, and Business Cycle," ISER Discussion Paper 0601, Institute of Social and Economic Research, Osaka University.

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    More about this item

    Keywords

    nonlinear; business cycle; Bayesian;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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