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Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach

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  • Zhihong Chen
  • Azhar Iqbal
  • Huiwen Lai

Abstract

Quantifying the probability of U.S. recessions has become increasingly important since August 2007. In a data-rich environment, this paper is the first to apply a Probit model to common factors extracted from a large set of explanatory variables to model and forecast recession probability. The results show the advantages of the proposed approach over many existing models. Simulated real-time analysis captures all recessions since 1980. The proposed model also detects a significant jump in the next six-month recession probability based on data up to November 2007, one year before the formal declaration of the recent recession by the NBER.

Suggested Citation

  • Zhihong Chen & Azhar Iqbal & Huiwen Lai, 2011. "Forecasting the probability of US recessions: a Probit and dynamic factor modelling approach," Canadian Journal of Economics, Canadian Economics Association, vol. 44(2), pages 651-672, May.
  • Handle: RePEc:cje:issued:v:44:y:2011:i:2:p:651-672
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    Citations

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    Cited by:

    1. Nataša Erjavec & Petar Sorić & Mirjana Čižmešija, 2016. "Predicting the probability of recession in Croatia: Is economic sentiment the missing link?," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics, vol. 34(2), pages 555-579.
    2. Fornaro, Paolo, 2015. "Forecasting U.S. Recessions with a Large Set of Predictors," MPRA Paper 62973, University Library of Munich, Germany.
    3. Baris Soybilgen, 2017. "Identifying Us Business Cycle Regimes Using Factor Augmented Neural Network Models," Working Papers 1703, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
    4. Bellégo, C. & Ferrara, L., 2012. "Macro-financial linkages and business cycles: A factor-augmented probit approach," Economic Modelling, Elsevier, vol. 29(5), pages 1793-1797.
    5. 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.
    6. Davig, Troy A. & Smalter Hall, Aaron, 2016. "Recession forecasting using Bayesian classification," Research Working Paper RWP 16-6, Federal Reserve Bank of Kansas City, revised 01 Feb 2017.
    7. repec:wly:jforec:v:36:y:2017:i:5:p:469-482 is not listed on IDEAS
    8. Kevin Moran & Simplice Aime Nono, 2016. "Using Confidence Data to Forecast the Canadian Business Cycle," Cahiers de recherche 1606, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    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.

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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