Forecasting U.S. Recessions Using Over 150 Years of Data: Stock-Market Moments versus Oil-Market Moments

Elie Bouri (), Rangan Gupta, Christian Pierdzioch and Onur Polat ()
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Elie Bouri: Adnan Kassar School of Business, Lebanese American University, Lebanon
Onur Polat: Department of Public Finance, Bilecik Seyh Edebali University, Bilecik, Turkiye

No 202435, Working Papers from University of Pretoria, Department of Economics

Abstract: Using monthly data from 1871 to 2024 and logistic models with shrinkage estimators, we compare the contribution of stock and oil-market moments (returns, volatility, skewness, and kurtosis) to the accuracy of out-of-sample forecasts of U.S. recessions at various forecast horizons, while controling for various standard macroeconomic predictors and the total connectedness indexes of the moments. Adding stock-market moments to the potential predictors improves significantly the accuracy of out-of-sample forecasts at the long forecast horizon, whereas oil-market moments and connectedness indexes do not contribute much. The lagged recession dummy, the term spread, and stock returns are found to be the top predictors of recessions.

Keywords: Recessions; Stock-market and oil-market moments; Forecasting; Shrinkage estimators; AUC statistics (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 G17 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2024-08
New Economics Papers: this item is included in nep-fmk, nep-his and nep-rmg
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Journal Article: Forecasting U.S. recessions using over 150 years of data: Stock-market moments versus oil-market moments (2024)
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