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Forecasting US recessions with macro factors

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  • Sebastian Fossati

Abstract

Dynamic factors estimated from panels of macroeconomic indicators are used to predict future recessions using probit models. Three factors are considered: a bond and exchange rates factor, a stock market factor and a real activity factor. Three results emerge. First, models that use only financial indicators exhibit a large deterioration in fit after 2005. Second, models that use factors yield better fit than models that use indicators directly. Out-of-sample forecasting exercises confirm these results for 3-, 6- and 12-month horizons using both ex-post revised data and real-time data. Third, results show evidence that data revisions affect factors less than individual indicators.

Suggested Citation

  • Sebastian Fossati, 2015. "Forecasting US recessions with macro factors," Applied Economics, Taylor & Francis Journals, vol. 47(53), pages 5726-5738, November.
    • Handle: RePEc:taf:applec:v:47:y:2015:i:53:p:5726-5738
    DOI: 10.1080/00036846.2015.1058904
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    References listed on IDEAS

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    1. Maximo Camacho & Gabriel Perez‐Quiros & Pilar Poncela, 2015. "Extracting Nonlinear Signals from Several Economic Indicators," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1073-1089, November.
    2. Jonathan H. Wright, 2006. "The yield curve and predicting recessions," Finance and Economics Discussion Series 2006-07, Board of Governors of the Federal Reserve System (U.S.).
    3. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, April.
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    Cited by:

    1. Barış Soybilgen, 2020. "Identifying US business cycle regimes using dynamic factors and neural network models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 827-840, August.
    2. Marius M. Mihai, 2020. "Do credit booms predict US recessions?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 887-910, September.
    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. Fossati, Sebastian, 2017. "Testing for State-Dependent Predictive Ability," Working Papers 2017-9, University of Alberta, Department of Economics.
    5. Baumann, Ursel & Gomez-Salvador, Ramon & Seitz, Franz, 2019. "Detecting turning points in global economic activity," Working Paper Series 2310, European Central Bank.
    6. Soybilgen, Baris, 2018. "Identifying US business cycle regimes using dynamic factors and neural network models," MPRA Paper 94715, University Library of Munich, Germany.

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    More about this item

    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

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