Nowcasting and forecasting US recessions: Evidence from the Super Learner

Benedikt Maas

MPRA Paper from University Library of Munich, Germany

Abstract: This paper introduces the Super Learner to nowcast and forecast the probability of a US economy recession in the current quarter and future quarters. The Super Learner is an algorithm that selects an optimal weighted average from several machine learning algorithms. In this paper, elastic net, random forests, gradient boosting machines and kernel support vector machines are used as underlying base learners of the Super Learner, which is trained with real-time vintages of the FRED-MD database as input data. The Super Learner’s ability to categorise future time periods into recessions versus expansions is compared with eight different alternatives based on probit models. The relative model performance is evaluated based on receiver operating characteristic (ROC) curves. In summary, the Super Learner predicts a recession very reliably across all forecast horizons, although it is defeated by different individual benchmark models on each horizon.

Keywords: Machine Learning; Nowcasting; Forecasting; Business cycle analysis (search for similar items in EconPapers)
JEL-codes: C32 C53 C55 E32 (search for similar items in EconPapers)
Date: 2019-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:96408

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