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Forecasting Economic Recessions Using Machine Learning:An Empirical Study in Six Countries

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  • Andreas Psimopoulos

    (ETH Zurich, Switzerland)

Abstract
This paper proposes a methodology for forecasting economic recessions using Machine Learning algorithms. Among the methods examined are Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Random Forests. The datasets analysed refer to six countries (Australia, Germany, Japan, Mexico, UK, USA) and cover a time span of more than 40 years. All methods are compared against each other in terms of six evaluation metrics on their out-of-sample performance. In contrast to most similar empirical studies, the methodology developed focuses on the timepoints of the last four quarters before a recession begins rather than on those of a recession per se. It has been found that the SVM method tends to out-perform the others, as it classified correctly at least 75% of the pre-recessionary periods for half of the countries, with mean overall classification accuracy around 90% in these cases. Moreover, for all the countries under study, the traditional Logit and Probit models are always inferior to at least one Machine Learning-based model. Additionally, it turns out that macroeconomic variables representing a kind of debt - such as, household debt - are most frequently considered as important across the six datasets, in terms of the Mean Decrease Gini measure.

Suggested Citation

  • 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.
    • Handle: RePEc:seb:journl:v:18:y:2020:i:1:p:40-99
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    File URL: http://www.asecu.gr/Seeje/issue34/issue34-psimopoulos.pdf
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    References listed on IDEAS

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    Cited by:

    1. Leakey Omolo & Nguyet Nguyen, 2024. "Using an Ensemble of Machine Learning Algorithms to Predict Economic Recession," JRFM, MDPI, vol. 17(9), pages 1-26, September.

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    More about this item

    Keywords

    Forecasting recessions; Machine Learning-based Econometrics; Gini importance; Support Vector Machines;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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