Predicting Recessions with Leading Indicators: Model Averaging and Selection Over the Business Cycle

Posted: 9 Aug 2013 Last revised: 28 Nov 2015

See all articles by Travis J. Berge

Travis J. Berge

Board of Governors of the Federal Reserve System

Date Written: January 2014

Abstract

Four model selection methods are applied to the problem of predicting business cycle turning points: equally-weighted forecasts, Bayesian model averaged forecasts, and two models produced by the machine learning algorithm boosting. The model selection algorithms condition on different economic indicators at different forecast horizons. Models produced by BMA and boosting outperform equally-weighted forecasts, even out of sample. Nonlinear models also appear to outperform their linear counterparts. Although the forecast ability of the yield curve endures, additional conditioning variables improves forecast ability. The findings highlight several important features of the business cycle.

Keywords: Business cycle turning points; recessions; variable selection; boosting; Bayesian model averaging; probabilistic forecasts

JEL Classification: C4, C5, C25, C53, E32

Suggested Citation

Berge, Travis J., Predicting Recessions with Leading Indicators: Model Averaging and Selection Over the Business Cycle (January 2014). Federal Reserve Bank of Kansas City Working Paper No. 13-05, Available at SSRN: https://ssrn.com/abstract=2307681 or http://dx.doi.org/10.2139/ssrn.2307681

Travis J. Berge (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

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