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Yield-Curve Based Probit Models for Forecasting U.S. Recessions: Stability and Dynamics

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  • Heikki Kauppi

    (Department of Economics, University of Turku)

Abstract

Recent research provides controversial evidence on the stability of yield-curve based binary probit models for forecasting U.S. recessions. This paper reviews so far applied specifications and presents new procedures for examining the stability of selected probit models. It finds that a yield-curve based probit model that treats the binary response (a recession dummy) as a nonhomogeneous Markov chain produces superior in-sample and out-of-sample probability forecasts for U.S. recessions and that this model specification is stable over time. Thus, the failure of yieldcurve based forecasts to signal the 1990-1991 and 2001 recessions should not be attributed to parameter instability, instead the evidence suggests that these events were inherently uncertain.

Suggested Citation

  • Heikki Kauppi, 2008. "Yield-Curve Based Probit Models for Forecasting U.S. Recessions: Stability and Dynamics," Discussion Papers 31, Aboa Centre for Economics.
    • Handle: RePEc:tkk:dpaper:dp31
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    Cited by:

    1. Christiansen, Charlotte & Eriksen, Jonas N. & Møller, Stig V., 2019. "Negative house price co-movements and US recessions," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 382-394.
    2. Henri Nyberg, 2010. "Testing an autoregressive structure in binary time series models," Economics Bulletin, AccessEcon, vol. 30(2), pages 1460-1473.
    3. Ng, Eric C.Y., 2012. "Forecasting US recessions with various risk factors and dynamic probit models," Journal of Macroeconomics, Elsevier, vol. 34(1), pages 112-125.
    4. Moysiadis, Theodoros & Fokianos, Konstantinos, 2014. "On binary and categorical time series models with feedback," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 209-228.
    5. Christiansen, Charlotte & Eriksen, Jonas Nygaard & Møller, Stig Vinther, 2014. "Forecasting US recessions: The role of sentiment," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 459-468.
    6. Henri Nyberg, 2010. "Dynamic probit models and financial variables in recession forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 215-230.
    7. Sander, Magnus, 2018. "Market timing over the business cycle," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 130-145.
    8. Fokianos, Konstantinos & Moysiadis, Theodoros, 2017. "Binary time series models driven by a latent process," Econometrics and Statistics, Elsevier, vol. 2(C), pages 117-130.
    9. Ratcliff, Ryan, 2013. "The “probability of recession”: Evaluating probabilistic and non-probabilistic forecasts from probit models of U.S. recessions," Economics Letters, Elsevier, vol. 121(2), pages 311-315.

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    More about this item

    Keywords

    recession forecast; yield curve; dynamic probit models; parameter stability;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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