IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2001.02115.html
   My bibliography  Save this paper

Understanding the Great Recession Using Machine Learning Algorithms

Author

Listed:
  • Rickard Nyman
  • Paul Ormerod

Abstract

Nyman and Ormerod (2017) show that the machine learning technique of random forests has the potential to give early warning of recessions. Applying the approach to a small set of financial variables and replicating as far as possible a genuine ex ante forecasting situation, over the period since 1990 the accuracy of the four-step ahead predictions is distinctly superior to those actually made by the professional forecasters. Here we extend the analysis by examining the contributions made to the Great Recession of the late 2000s by each of the explanatory variables. We disaggregate private sector debt into its household and non-financial corporate components. We find that both household and non-financial corporate debt were key determinants of the Great Recession. We find a considerable degree of non-linearity in the explanatory models. In contrast, the public sector debt to GDP ratio appears to have made very little contribution. It did rise sharply during the Great Recession, but this was as a consequence of the sharp fall in economic activity rather than it being a cause. We obtain similar results for both the United States and the United Kingdom.

Suggested Citation

  • Rickard Nyman & Paul Ormerod, 2020. "Understanding the Great Recession Using Machine Learning Algorithms," Papers 2001.02115, arXiv.org.
    • Handle: RePEc:arx:papers:2001.02115
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2001.02115
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Susan Athey & Michael Luca, 2019. "Economists (and Economics) in Tech Companies," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 209-230, Winter.
    2. Victor Zarnowitz & Phillip Braun, 1993. "Twenty-two Years of the NBER-ASA Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 11-94, National Bureau of Economic Research, Inc.
    3. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    4. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    5. Fildes, Robert & Stekler, Herman, 2002. "Reply to the comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 503-505, December.
    6. Alessi, Lucia & Detken, Carsten, 2011. "Quasi real time early warning indicators for costly asset price boom/bust cycles: A role for global liquidity," European Journal of Political Economy, Elsevier, vol. 27(3), pages 520-533, September.
    7. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kian Tehranian, 2023. "Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments?," Papers 2308.16200, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rickard Nyman & Paul Ormerod, 2017. "Predicting Economic Recessions Using Machine Learning Algorithms," Papers 1701.01428, arXiv.org.
    2. Tara M. Sinclair & Fred Joutz & Herman O. Stekler, 2008. "Are 'unbiased' forecasts really unbiased? Another look at the Fed forecasts," Working Papers 2008-010, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    3. Dovern, Jonas & Jannsen, Nils, 2017. "Systematic errors in growth expectations over the business cycle," International Journal of Forecasting, Elsevier, vol. 33(4), pages 760-769.
    4. David Tucket & Antoine Mandel & Diana Mangalagiu & Allen Abramson & Jochen Hinkel & Konstantinos Katsikopoulos & Alan Kirman & Thierry Malleret & Igor Mozetic & Paul Ormerod & Robert Elliot Smith & To, 2015. "Uncertainty, Decision Science, and Policy Making: A Manifesto for a Research Agenda," SciencePo Working papers Main hal-02057279, HAL.
    5. repec:hal:pseose:hal-02057279 is not listed on IDEAS
    6. Lang, Jan Hannes & Izzo, Cosimo & Fahr, Stephan & Ruzicka, Josef, 2019. "Anticipating the bust: a new cyclical systemic risk indicator to assess the likelihood and severity of financial crises," Occasional Paper Series 219, European Central Bank.
    7. Ashiya, Masahiro, 2007. "Forecast accuracy of the Japanese government: Its year-ahead GDP forecast is too optimistic," Japan and the World Economy, Elsevier, vol. 19(1), pages 68-85, January.
    8. Giovanni Cicceri & Giuseppe Inserra & Michele Limosani, 2020. "A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    9. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    10. Ildeberta Abreu, 2011. "International organisations’ vs. private analysts’ forecasts: an evaluation," Working Papers w201120, Banco de Portugal, Economics and Research Department.
    11. Karsten Müller, 2022. "German forecasters’ narratives: How informative are German business cycle forecast reports?," Empirical Economics, Springer, vol. 62(5), pages 2373-2415, May.
    12. Higgins, Matthew L. & Mishra, Sagarika, 2014. "State dependent asymmetric loss and the consensus forecast of real U.S. GDP growth," Economic Modelling, Elsevier, vol. 38(C), pages 627-632.
    13. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    14. Rostami-Tabar, Bahman & Ali, Mohammad M. & Hong, Tao & Hyndman, Rob J. & Porter, Michael D. & Syntetos, Aris, 2022. "Forecasting for social good," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1245-1257.
    15. Ericsson, Neil R., 2016. "Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis," International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
    16. Heilemann, Ullrich & Stekler, Herman, 2007. "Introduction to "The future of macroeconomic forecasting"," International Journal of Forecasting, Elsevier, vol. 23(2), pages 159-165.
    17. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    18. Paulo Júlio & Pedro M. Esperança, 2012. "Evaluating the forecast quality of GDP components: An application to G7," GEE Papers 0047, Gabinete de Estratégia e Estudos, Ministério da Economia, revised Apr 2012.
    19. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    20. Crespo, Cristian, 2020. "Two become one: improving the targeting of conditional cash transfers with a predictive model of school dropout," LSE Research Online Documents on Economics 123139, London School of Economics and Political Science, LSE Library.
    21. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2001.02115. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.