Oil-Price Uncertainty and the U.K. Unemployment Rate: A Forecasting Experiment with Random Forests Using 150 Years of Data

Rangan Gupta, Christian Pierdzioch and Afees Salisu

No 202095, Working Papers from University of Pretoria, Department of Economics

Abstract: We analyze the predictive role of oil-price uncertainty for changes in the UK unemployment rate using more than a century of monthly data covering the period from 1859 to 2020. To this end, we use a machine-learning technique known as random forests. Random forests render it possible to model the potentially nonlinear link between oil-price uncertainty and subsequent changes in the unemployment rate in an entirely data-driven way, where it is possible to control for the impact of several other macroeconomic variables and other macroeconomic and financial uncertainties. Upon estimating random forests on rolling-estimation windows, we find evidence that oil-price uncertainty predicts out-ofsample changes in the unemployment rate, where the relative importance of oil-price uncertainty has undergone substantial swings during the history of the modern petroleum industry that started with the drilling of the first oil well at Titusville (Pennsylvania, United States) in 1859.

Keywords: Machine learning; Random forests; Oil uncertainty; Macroeconomic and financial uncertainties; Unemployment rate; United Kingdom (search for similar items in EconPapers)
JEL-codes: C22 C53 E24 E43 F31 G10 Q02 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2020-10
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-his and nep-mac
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Citations: View citations in EconPapers (1)

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Journal Article: Oil-price uncertainty and the U.K. unemployment rate: A forecasting experiment with random forests using 150 years of data (2022)
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