Uncertainty and Forecasts of U.S. Recessions

Christian Pierdzioch and Rangan Gupta

Studies in Nonlinear Dynamics & Econometrics, 2020, vol. 24, issue 4, 20

Abstract: 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.

Keywords: boosted regression trees; forecasting; recessions; ROC curves; uncertainty (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Working Paper: Uncertainty and Forecasts of U.S. Recessions (2017)
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DOI: 10.1515/snde-2018-0083

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