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Using an Ensemble of Machine Learning Algorithms to Predict Economic Recession
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Version 1
: Received: 26 July 2024 / Approved: 29 July 2024 / Online: 31 July 2024 (18:56:20 CEST)
How to cite: Omolo, L.; Nguyen, N. Using an Ensemble of Machine Learning Algorithms to Predict Economic Recession. Preprints 2024, 2024072620. https://doi.org/10.20944/preprints202407.2620.v1 Omolo, L.; Nguyen, N. Using an Ensemble of Machine Learning Algorithms to Predict Economic Recession. Preprints 2024, 2024072620. https://doi.org/10.20944/preprints202407.2620.v1
Abstract
The Covid19 pandemic and the current wars in some countries have put incredible pressures on the global economy. Challenges for the U.S. include not only economic factors, major disruptions and reorganizations of the supply chains, but also those of national security and global geopolitics. This unprecedented situation makes predicting economic crisis for the coming years crucial yet challenging. In this paper, we propose a method based on various machine-learning models to predict the probability of a recession for the US economy in the next year. We collect the U.S’ monthly macroeconomics indicators and recession data from January of 1983 to December of 2023 to predict the probability of an economic recession in 2024. The performance of the individual economics indicator for the coming year was predicted separately, and then all of the the predicted indicators were used to forecast a possible economic recession. Our results showed that the U.S. will face a high probability of being in a recession period in the last quarter of 2024.
Keywords
Economic crisis, machine learning, ensemble model, recession probability .
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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