IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i2p241-d320277.html
   My bibliography  Save this article

A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study

Author

Listed:
  • Giovanni Cicceri

    (Department of Engineering, University of Messina, 98166 Messina, Italy)

  • Giuseppe Inserra

    (Department of Economics, University of Messina, 98122 Messina, Italy)

  • Michele Limosani

    (Department of Economics, University of Messina, 98122 Messina, Italy)

Abstract

In economic activity, recessions represent a period of failure in Gross Domestic Product (GDP) and usually are presented as episodic and non-linear. For this reason, they are difficult to predict and appear as one of the main problems in macroeconomics forecasts. A classic example turns out to be the great recession that occurred between 2008 and 2009 that was not predicted. In this paper, the goal is to give a different, although complementary, approach concerning the classical econometric techniques, and to show how Machine Learning (ML) techniques may improve short-term forecasting accuracy. As a case study, we use Italian data on GDP and a few related variables. In particular, we evaluate the goodness of fit of the forecasting proposed model in a case study of the Italian GDP. The algorithm is trained on Italian macroeconomic variables over the period 1995:Q1-2019:Q2. We also compare the results using the same dataset through Classic Linear Regression Model. As a result, both statistical and ML approaches are able to predict economic downturns but higher accuracy is obtained using Nonlinear Autoregressive with exogenous variables (NARX) model.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:2:p:241-:d:320277
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/2/241/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/2/241/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Max Gillman & Michal Kejak, 2011. "Inflation, Investment and Growth: a Money and Banking Approach," Economica, London School of Economics and Political Science, vol. 78(310), pages 260-282, April.
    2. Panizza, Ugo & Presbitero, Andrea F., 2014. "Public debt and economic growth: Is there a causal effect?," Journal of Macroeconomics, Elsevier, vol. 41(C), pages 21-41.
    3. Zhe Wu & Panagiotis D. Christofides, 2019. "Economic Machine-Learning-Based Predictive Control of Nonlinear Systems," Mathematics, MDPI, vol. 7(6), pages 1-20, June.
    4. Laura Jaramillo & Mr. Carlo Cottarelli, 2012. "Walking Hand in Hand: Fiscal Policy and Growth in Advanced Economies," IMF Working Papers 2012/137, International Monetary Fund.
    5. Song Han & Casey B. Mulligan, 2008. "Inflation and the size of government," Review, Federal Reserve Bank of St. Louis, vol. 90(May), pages 245-267.
    6. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    7. Mishkin, Frederic S., 1990. "What does the term structure tell us about future inflation?," Journal of Monetary Economics, Elsevier, vol. 25(1), pages 77-95, January.
    8. Frederic S. Mishkin, 1990. "The Information in the Longer Maturity Term Structure about Future Inflation," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 105(3), pages 815-828.
    9. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    10. Wang, Lei & Li, Shouwei & Wu, Chaoqun, 2019. "Risk contagion in inter-firm credit guarantee network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    11. Ferreira da Silva, Gisele, 2002. "The impact of financial system development on business cycles volatility: cross-country evidence," Journal of Macroeconomics, Elsevier, vol. 24(2), pages 233-253, June.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. 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.
    14. Scharler, Johann, 2008. "Do bank-based financial systems reduce macroeconomic volatility by smoothing interest rates?," Journal of Macroeconomics, Elsevier, vol. 30(3), pages 1207-1221, September.
    15. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
    16. Thomas Laubach, 2009. "New Evidence on the Interest Rate Effects of Budget Deficits and Debt," Journal of the European Economic Association, MIT Press, vol. 7(4), pages 858-885, June.
    17. Rickard Nyman & Paul Ormerod, 2017. "Predicting Economic Recessions Using Machine Learning Algorithms," Papers 1701.01428, arXiv.org.
    18. Cochrane, John H., 2011. "Understanding policy in the great recession: Some unpleasant fiscal arithmetic," European Economic Review, Elsevier, vol. 55(1), pages 2-30, January.
    19. Arturo Estrella & Mary R. Trubin, 2006. "The yield curve as a leading indicator: some practical issues," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 12(Jul).
    20. Lioui, Abraham & Poncet, Patrice, 2008. "Monetary non-neutrality in the Sidrauski model under uncertainty," Economics Letters, Elsevier, vol. 100(1), pages 22-26, July.
    21. 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.
    22. Lorenzo Codogno & Carlo Favero & Alessandro Missale, 2003. "Yield spreads on EMU government bonds [‘Fiscal policy events and interest rate swap spreads: some evidence from the EU’]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 18(37), pages 503-532.
    23. Xiaohui Liu & Peter Sinclair, 2008. "Does the linkage between stock market performance and economic growth vary across Greater China?," Applied Economics Letters, Taylor & Francis Journals, vol. 15(7), pages 505-508.
    24. Fama, Eugene F, 1990. "Stock Returns, Expected Returns, and Real Activity," Journal of Finance, American Finance Association, vol. 45(4), pages 1089-1108, September.
    25. Teles, Vladimir K. & Cesar Mussolini, Caio, 2014. "Public debt and the limits of fiscal policy to increase economic growth," European Economic Review, Elsevier, vol. 66(C), pages 1-15.
    26. repec:oup:ecpoli:v:18:y:2003:i:37:p:503-532 is not listed on IDEAS
    27. Checherita-Westphal, Cristina, 2019. "Interest rate-growth differential and government debt dynamics," Economic Bulletin Boxes, European Central Bank, vol. 2.
    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. Chih-Te Yang & Yensen Ni & Mu-Hsiang Yu & Yuhsin Chen & Paoyu Huang, 2023. "Decoding the Profitability of Insurance Products: A Novel Approach to Evaluating Non-Participating and Participating Insurance Policies," Mathematics, MDPI, vol. 11(13), pages 1-16, June.
    2. Franck Ramaharo & Gerzhino Rasolofomanana, 2023. "Nowcasting Madagascar's real GDP using machine learning algorithms," Papers 2401.10255, arXiv.org.
    3. Alena Vagaská & Miroslav Gombár & Antonín Korauš, 2022. "Mathematical Modeling and Nonlinear Optimization in Determining the Minimum Risk of Legalization of Income from Criminal Activities in the Context of EU Member Countries," Mathematics, MDPI, vol. 10(24), pages 1-25, December.
    4. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    5. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).

    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. Eberhardt, Markus & Presbitero, Andrea F., 2015. "Public debt and growth: Heterogeneity and non-linearity," Journal of International Economics, Elsevier, vol. 97(1), pages 45-58.
    2. Panizza, Ugo & Presbitero, Andrea F., 2014. "Public debt and economic growth: Is there a causal effect?," Journal of Macroeconomics, Elsevier, vol. 41(C), pages 21-41.
    3. Panizza, Ugo & Fatás, Antonio & Ghosh, Atish R. & ,, 2019. "The Motives to Borrow," CEPR Discussion Papers 13735, C.E.P.R. Discussion Papers.
    4. Helder Ferreira de Mendonça & Ytallo Brito, 2021. "The link between public debt and investment: an empirical assessment from emerging markets," Applied Economics, Taylor & Francis Journals, vol. 53(50), pages 5864-5876, October.
    5. Abbas Valadkhani & George Chen, 2014. "An empirical analysis of the US stock market and output growth volatility spillover effects on three Anglo-Saxon countries," International Review of Applied Economics, Taylor & Francis Journals, vol. 28(3), pages 323-335, May.
    6. Yutaka KURIHARA, 2015. "Debt and Economic Growth: The Case of Japan," Journal of Economics Library, KSP Journals, vol. 2(2), pages 45-52, June.
    7. Nikolaos Antonakakis, 2014. "Sovereign Debt and Economic Growth Revisited: The Role of (Non-)Sustainable Debt Thresholds," Department of Economics Working Papers wuwp187, Vienna University of Economics and Business, Department of Economics.
    8. Maitra, Biswajit, 2019. "Macroeconomic impact of public debt and foreign aid in Sri Lanka," Journal of Policy Modeling, Elsevier, vol. 41(2), pages 372-394.
    9. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    10. Puonti, Päivi, 2022. "Public Debt and Economic Growth," ETLA Reports 127, The Research Institute of the Finnish Economy.
    11. Roberto Martino & Phu Nguyen-Van, 2014. "Labour market regulation and fiscal parameters: A structural model for European regions," Working Papers of BETA 2014-19, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    12. 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.
    13. Lorenzo Carbonari & Alessio Farcomeni & Filippo Maurici & Giovanni Trovato, 2023. "On the output effect of fiscal consolidation plans: a causal analysis," Working Paper series 23-18, Rimini Centre for Economic Analysis.
    14. İbrahim Özmen & Mihai Mutascu, 2024. "Public Debt and Growth: New Insights," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 8706-8736, June.
    15. Yutaka Kurihara, 2016. "Term Structure of Interest Rates under Zero or Low Bound: The Recent Japanese Case," Economy, Asian Online Journal Publishing Group, vol. 3(1), pages 19-23.
    16. Asit Mohanty & Suresh Kumar Patra & Satyendra Kumar & Avipsa Mohanty, 2016. "An Empirical Analysis of Effect of Public Debt on Economic Growth in India in the Post-Reform Era," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 6(11), pages 692-705, November.
    17. Pawel Dlotko & Simon Rudkin, 2019. "The Topology of Time Series: Improving Recession Forecasting from Yield Spreads," Working Papers 2019-02, Swansea University, School of Management.
    18. Cimadomo, Jacopo & Claeys, Peter & Poplawski-Ribeiro, Marcos, 2016. "How do experts forecast sovereign spreads?," European Economic Review, Elsevier, vol. 87(C), pages 216-235.
    19. Glocker, Christian & Kaniovski, Serguei, 2020. "Structural modeling and forecasting using a cluster of dynamic factor models," MPRA Paper 101874, University Library of Munich, Germany.
    20. Cremers, Martijn & Fleckenstein, Matthias & Gandhi, Priyank, 2021. "Treasury yield implied volatility and real activity," Journal of Financial Economics, Elsevier, vol. 140(2), pages 412-435.

    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:gam:jmathe:v:8:y:2020:i:2:p:241-:d:320277. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.