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Modeling and predicting U.S. recessions using machine learning techniques

Vrontos, Spyridon and Galakis, John and Vrontos, Ioannis (2021) 'Modeling and predicting U.S. recessions using machine learning techniques.' International Journal of Forecasting, 37 (2). pp. 647-671. ISSN 0169-2070

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The most representative machine learning techniques are implemented for modeling and forecasting U.S. economic activity and recessions in particular. An elaborate, comprehensive, and comparative framework is employed in order to estimate U.S. recession probabilities. The empirical analysis explores the predictive content of numerous well-followed macroeconomic and financial indicators, but also introduces a set of less studied predictors. The predictive ability of the underlying models is evaluated using a plethora of statistical evaluation metrics. The results strongly support the application of machine learning over more standard econometric techniques in the area of recession prediction. Specifically, the analysis indicates that penalized Logit regression models, k-nearest neighbors, and Bayesian generalized linear models largely outperform ‘original’ Logit/Probit models in the prediction of U.S. recessions, as they achieve higher predictive accuracy across long-, medium-, and short-term forecast horizons.

Item Type: Article
Uncontrolled Keywords: Forecasting; Recession; Probit/Logit; Penalized likelihood Models; Classification and Regression Trees
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 24 Sep 2020 13:17
Last Modified: 10 Feb 2022 17:11

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