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Modeling and predicting US recessions using machine learning techniques
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DOI：10.1016/j.ijforecast.2020.08.005.png)
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摘要
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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. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
Forecasting
Recession
Binary Probit/Logit
Classification and regression trees
Penalized likelihood models
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Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle
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