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Forecasting US recessions with various risk factors and dynamic probit models

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  • Ng, Eric C.Y.

Abstract

This paper extends probit recession forecasting models by incorporating various recession risk factors and using the advanced dynamic probit modeling approaches. The proposed risk factors include financial market expectations of a gloomy economic outlook, credit or liquidity risks in the general economy, the risks of negative wealth effects resulting from the bursting of asset price bubbles, and signs of deteriorating macroeconomic fundamentals. The model specifications include three different dynamic probit models and the standard static model. The out-of-sample analysis suggests that the four probit models with the proposed risk factors can generate more accurate forecasts for the duration of recessions than the conventional static models with only yield spread and equity price index as the predictors. Among the four probit models, the dynamic and dynamic autoregressive probit models outperform the static and autoregressive models in terms of predicting the recession duration. With respect to forecasting the business cycle turning points, the static probit model is as good as the dynamic probit models by being able to flag an early warning signal of a recession.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:jmacro:v:34:y:2012:i:1:p:112-125
    DOI: 10.1016/j.jmacro.2011.11.001
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Schreiber, Sven & Soldatenkova, Natalia, 2016. "Anticipating business-cycle turning points in real time using density forecasts from a VAR," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 166-187.
    3. Proaño, Christian R. & Theobald, Thomas, 2014. "Predicting recessions with a composite real-time dynamic probit model," International Journal of Forecasting, Elsevier, vol. 30(4), pages 898-917.
    4. repec:wly:jforec:v:36:y:2017:i:5:p:469-482 is not listed on IDEAS
    5. Harri Pönkä, 2017. "Predicting the direction of US stock markets using industry returns," Empirical Economics, Springer, vol. 52(4), pages 1451-1480, June.
    6. repec:eee:empfin:v:46:y:2018:i:c:p:130-145 is not listed on IDEAS
    7. Anna Pestova, 2015. "Leading Indicators of the Business Cycle: Dynamic Logit Models for OECD Countries and Russia," HSE Working papers WP BRP 94/EC/2015, National Research University Higher School of Economics.
    8. Schreiber, Sven, 2013. "Forecasting business-cycle turning points with (relatively large) linear systems in real time," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79709, Verein für Socialpolitik / German Economic Association.
    9. Harri Ponka, 2017. "The Role of Credit in Predicting US Recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(5), pages 469-482, August.
    10. Matthew C. Li, 2014. "The US zero-coupon yield spread as a predictor of excess daily stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 24(13), pages 889-906, July.
    11. Chen Shiu-Sheng & Chou Yu-Hsi & Yen Chia-Yi, 2016. "Predicting US recessions with stock market illiquidity," The B.E. Journal of Macroeconomics, De Gruyter, vol. 16(1), pages 93-123, January.
    12. Goodness C. Aye & Christina Christou & Luis A. Gil-Alana & Rangan Gupta, 2016. "Forecasting the Probability of Recessions in South Africa: The Role of Decomposed Term-Spread and Economic Policy Uncertainty," Working Papers 201680, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    Recession forecasts; Recession risks; Dynamic probit models;

    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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