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Forecasting recessions: can we do better on MARS?

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  • Peter Sephton

Abstract

A number of recent articles have examined the ability of financial variables to predict recessions. In this article, Peter Sephton extends the literature by considering a non-linear, nonparametric approach to predicting the probability of recession using multivariate adaptive regression splines (MARS). The results suggest that this data-intensive approach to modeling is not a panacea for recession forecasting. Although it does well explaining the data within the sample, its out-of-sample forecasts do not improve upon the benchmark probit specification.

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  • Peter Sephton, 2001. "Forecasting recessions: can we do better on MARS?," Review, Federal Reserve Bank of St. Louis, vol. 83(Mar), pages 39-49.
    • Handle: RePEc:fip:fedlrv:y:2001:i:mar:p:39-49:n:v.83no.2
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    Cited by:

    1. Sergey V. Smirnov & Nikolay V. Kondrashov & Anna V. Petronevich, 2017. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(1), pages 53-73, May.
    2. David Bolder & Tiago Rubin, 2007. "Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis," Staff Working Papers 07-14, Bank of Canada.
    3. Kartal, Mustafa Tevfik, 2022. "The role of consumption of energy, fossil sources, nuclear energy, and renewable energy on environmental degradation in top-five carbon producing countries," Renewable Energy, Elsevier, vol. 184(C), pages 871-880.
    4. Sephton, Peter S., 2019. "El Niño, La Niña, and a cup of Joe," Energy Economics, Elsevier, vol. 84(C).
    5. Peter Sephton, 2005. "Forecasting inflation using the term structure and MARS," Applied Economics Letters, Taylor & Francis Journals, vol. 12(4), pages 199-202.
    6. Zaher Mundher Yaseen & Sujay Raghavendra Naganna & Zulfaqar Sa’adi & Pijush Samui & Mohammad Ali Ghorbani & Sinan Q. Salih & Shamsuddin Shahid, 2020. "Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1075-1091, February.
    7. Deo, Ravinesh C. & Şahin, Mehmet & Adamowski, Jan F. & Mi, Jianchun, 2019. "Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 235-261.
    8. Salcedo-Sanz, Sancho & Deo, Ravinesh C. & Cornejo-Bueno, Laura & Camacho-Gómez, Carlos & Ghimire, Sujan, 2018. "An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia," Applied Energy, Elsevier, vol. 209(C), pages 79-94.
    9. Pons Novell, J., 2002. "Ciclo de la economía española y contenido informativo de los tipos de interés," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 20, pages 583-598, Diciembre.
    10. David Jamieson Bolder & Yuliya Romanyuk, 2010. "Combining Canadian Interest Rate Forecasts," Palgrave Macmillan Books, in: Arjan B. Berkelaar & Joachim Coche & Ken Nyholm (ed.), Interest Rate Models, Asset Allocation and Quantitative Techniques for Central Banks and Sovereign Wealth Funds, chapter 1, pages 3-30, Palgrave Macmillan.
    11. Sephton, Peter & Mann, Janelle, 2013. "Further evidence of an Environmental Kuznets Curve in Spain," Energy Economics, Elsevier, vol. 36(C), pages 177-181.
    12. Serpil Kılıç Depren & Mustafa Tevfik Kartal, 2021. "Prediction on the volume of non‐performing loans in Turkey using multivariate adaptive regression splines approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 6395-6405, October.
    13. Pedro N. Rodriguez & Arnulfo Rodriguez, 2006. "Understanding and predicting sovereign debt rescheduling: a comparison of the areas under receiver operating characteristic curves," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(7), pages 459-479.
    14. Serhat Yüksel & Shahriyar Mukhtarov & Ceyhun Mahmudlu & Jeyhun I. Mikayilov & Anar Iskandarov, 2018. "Measuring International Migration in Azerbaijan," Sustainability, MDPI, vol. 10(1), pages 1-15, January.
    15. Sharda, V.N. & Patel, R.M. & Prasher, S.O. & Ojasvi, P.R. & Prakash, Chandra, 2006. "Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques," Agricultural Water Management, Elsevier, vol. 83(3), pages 233-242, June.
    16. K. Batu Tunay, 2010. "Banking Crises and Early Warning Systems: A Model Suggestion for Turkish Banking Sector," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 4(1), pages 9-46.
    17. Sephton, Peter & Mann, Janelle, 2018. "Gold and crude oil prices after the great moderation," Energy Economics, Elsevier, vol. 71(C), pages 273-281.
    18. Fabio Moneta, 2005. "Does the Yield Spread Predict Recessions in the Euro Area?," International Finance, Wiley Blackwell, vol. 8(2), pages 263-301, August.

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    Recessions; Forecasting;

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