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Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

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Volume 24 (2020)

Uncertainty and Forecasts of U.S. Recessions

Christian Pierdzioch / Rangan Gupta
Published Online: 2019-07-25 | DOI: https://doi.org/10.1515/snde-2018-0083


We estimate Boosted Regression Trees (BRT) on a sample of monthly data that extends back to 1889 to recover the predictive value of disaggregated news-based uncertainty indexes for U.S recessions. We control for widely-studied standard predictors and use out-of-sample metrics to assess forecast performance. We find that war-related uncertainty is among the top five predictors of recessions at three different forecast horizons (3, 6, and 12 months). The predictive value of war-related uncertainty has fallen in the second half of the 20th century. Uncertainty regarding the state of securities markets has gained in relative importance. The probability of a recession is a nonlinear function of war-related and securities-markets uncertainty. Receiver-operating-characteristic curves show that uncertainty improves out-of-sample forecast performance at the longer forecast horizons. A dynamic version of the BRT approach sheds light on the importance of various lags of government-related uncertainty for recession forecasting at the long forecast horizon.

This article offers supplementary material which is provided at the end of the article.

Keywords: boosted regression trees; forecasting; recessions; ROC curves; uncertainty

JEL Classification: C53; E32; E37


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About the article

Published Online: 2019-07-25

Funding Source: Deutsche Forschungsgemeinschaft

Award identifier / Grant number: 275693836

Deutsche Forschungsgemeinschaft. Funder ID: http://dx.doi.org/10.13039/501100001659 (Grant number: 275693836)

Citation Information: Studies in Nonlinear Dynamics & Econometrics, 20180083, ISSN (Online) 1558-3708, DOI: https://doi.org/10.1515/snde-2018-0083.

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