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Do leading indicators forecast U.S. recessions? A nonlinear re†evaluation using historical data

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  • Vasilios Plakandaras
  • Juncal Cunado
  • Rangan Gupta
  • Mark E. Wohar

Abstract

This paper analyses to what extent a selection of leading indicators is able to forecast U.S. recessions, by means of both dynamic probit models and Support Vector Machine (SVM) models, using monthly data from January 1871 to June 2016. The results suggest that the probit models predict U.S. recession periods more accurately than SVM models up to six months ahead, while the SVM models are more accurate over longer horizons. Furthermore, SVM models appear to distinguish between recessions and tranquil periods better than probit models do. Finally, the most accurate forecasting models are those that include oil, stock returns and the term spread as leading indicators.

Suggested Citation

  • Vasilios Plakandaras & Juncal Cunado & Rangan Gupta & Mark E. Wohar, 2017. "Do leading indicators forecast U.S. recessions? A nonlinear re†evaluation using historical data," International Finance, Wiley Blackwell, vol. 20(3), pages 289-316, December.
    • Handle: RePEc:bla:intfin:v:20:y:2017:i:3:p:289-316
    DOI: 10.1111/infi.12111
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    Cited by:

    1. André, Christophe & Caraiani, Petre & Gupta, Rangan, 2023. "Fiscal policy and stock markets at the effective lower bound," Finance Research Letters, Elsevier, vol. 58(PC).
    2. Gupta, Rangan & Sheng, Xin & Balcilar, Mehmet & Ji, Qiang, 2021. "Time-varying impact of pandemics on global output growth," Finance Research Letters, Elsevier, vol. 41(C).
    3. Sheng, Xin & Marfatia, Hardik A. & Gupta, Rangan & Ji, Qiang, 2021. "House price synchronization across the US states: The role of structural oil shocks," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    4. João Frois Caldeira & Rangan Gupta & Muhammad Tahir Suleman & Hudson S. Torrent, 2021. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4312-4329, December.
    5. Andreas Psimopoulos, 2020. "Forecasting Economic Recessions Using Machine Learning:An Empirical Study in Six Countries," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 18(1), pages 40-99.
    6. Riza Demirer & Rangan Gupta & Jacobus Nel & Christian Pierdzioch, 2020. "Effect of Rare Disaster Risks on Crude Oil: Evidence from El Nino from Over 140 Years of Data," Working Papers 2020104, University of Pretoria, Department of Economics.
    7. Bonato, Matteo & Gupta, Rangan & Lau, Chi Keung Marco & Wang, Shixuan, 2020. "Moments-based spillovers across gold and oil markets," Energy Economics, Elsevier, vol. 89(C).
    8. Gupta, Rangan & Huber, Florian & Piribauer, Philipp, 2020. "Predicting international equity returns: Evidence from time-varying parameter vector autoregressive models," International Review of Financial Analysis, Elsevier, vol. 68(C).
    9. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).
    10. Wang, Shixuan & Gupta, Rangan & Zhang, Yue-Jun, 2021. "Bear, Bull, Sidewalk, and Crash: The Evolution of the US Stock Market Using Over a Century of Daily Data," Finance Research Letters, Elsevier, vol. 43(C).
    11. Cepni, Oguzhan & Gupta, Rangan & Karahan, Cenk C. & Lucey, Brian, 2022. "Oil price shocks and yield curve dynamics in emerging markets," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 613-623.
    12. Marfatia, Hardik A. & Gupta, Rangan & Cakan, Esin, 2021. "Dynamic impact of the U.S. monetary policy on oil market returns and volatility," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 159-169.
    13. Ronald Ravinesh Kumar & Peter Josef Stauvermann & Hang Thi Thu Vu, 2021. "The Relationship between Yield Curve and Economic Activity: An Analysis of G7 Countries," JRFM, MDPI, vol. 14(2), pages 1-23, February.
    14. Oguzhan Cepni & Rangan Gupta & Qiang Ji, 2023. "Sentiment Regimes and Reaction of Stock Markets to Conventional and Unconventional Monetary Policies: Evidence from OECD Countries," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 24(3), pages 365-381, July.
    15. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    16. Gupta, Rangan & Sheng, Xin & van Eyden, Reneé & Wohar, Mark E., 2021. "The impact of disaggregated oil shocks on state-level real housing returns of the United States: The role of oil dependence," Finance Research Letters, Elsevier, vol. 43(C).
    17. Duan, Yunlong & Mu, Chang & Yang, Meng & Deng, Zhiqing & Chin, Tachia & Zhou, Li & Fang, Qifeng, 2021. "Study on early warnings of strategic risk during the process of firms’ sustainable innovation based on an optimized genetic BP neural networks model: Evidence from Chinese manufacturing firms," International Journal of Production Economics, Elsevier, vol. 242(C).
    18. Elie Bouri & Rangan Gupta & Shixuan Wang, 2019. "Contagion between Stock and Real Estate Markets: International Evidence from a Local Gaussian Correlation Approach," Working Papers 201917, University of Pretoria, Department of Economics.
    19. Salisu, Afees A. & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil prices over 150 years: The role of tail risks," Resources Policy, Elsevier, vol. 75(C).
    20. Jacobus Nel & Rangan Gupta & Mark E. Wohar & Christian Pierdzioch, 2022. "Climate Risks and Predictability of Commodity Returns and Volatility: Evidence from Over 750 Years of Data," Working Papers 202242, University of Pretoria, Department of Economics.
    21. Gupta, Rangan & Kanda, Patrick & Tiwari, Aviral Kumar & Wohar, Mark E., 2019. "Time-varying predictability of oil market movements over a century of data: The role of US financial stress," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).

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