Forecasting U.S. Recessions with a Large Set of Predictors

Paolo Fornaro

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper, I use a large set of macroeconomic and financial predictors to forecast U.S. recession periods. I adopt Bayesian methodology with shrinkage in the parameters of the probit model for the binary time series tracking the state of the economy. The in-sample and out-of-sample results show that utilizing a large cross-section of indicators yields superior U.S. recession forecasts in comparison to a number of parsimonious benchmark models. Moreover, data rich models with shrinkage manage to beat the forecasts obtained with the factor-augmented probit model employed in past research

Keywords: Bayesian shrinkage; Business Cycles; Probit model; large cross-sections (search for similar items in EconPapers)
JEL-codes: C11 C25 E32 E37 (search for similar items in EconPapers)
Date: 2015-03-15
New Economics Papers: this item is included in nep-ecm and nep-for
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Citations: View citations in EconPapers (8)

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