Oil-price uncertainty and the U.K. unemployment rate : a forecasting experiment with random forests using 150 years of data

dc.contributor.authorGupta, Rangan
dc.contributor.authorPierdzioch, Christian
dc.contributor.authorSalisu, Afees A.
dc.contributor.emailrangan.gupta@up.ac.zaen_US
dc.date.accessioned2023-01-18T12:35:16Z
dc.date.available2023-01-18T12:35:16Z
dc.date.issued2022-08
dc.description.abstractWe 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 (when the drilling of the first oil well started at Titusville, Pennsylvania, United States) 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. We estimate random forests on rolling-estimation windows and find evidence that oil-price uncertainty predicts out-of-sample changes in the unemployment rate, especially at longer (six and twelve months) forecast horizons. Moreover, the relative importance of oil-price uncertainty has undergone substantial swings during the history of the modern petroleum industry. Relative importance was high in the 1970s and the 1980s, and it was higher than the relative importance of changes in the oil price itself for most of the sample period. We also find that oil-price uncertainty has predictive value for changes in the unemployment rate when we use a Lasso estimator, where random forests have a superior forecasting performance relative to the Lasso forecasts.en_US
dc.description.departmentEconomicsen_US
dc.description.librarianhj2023en_US
dc.description.urihttp://www.elsevier.com/locate/resourpolen_US
dc.identifier.citationGupta, R., Pierdzioch, C. & Salisu, A.A. 2022, 'Oil-price uncertainty and the U.K. unemployment rate: a forecasting experiment with random forests using 150 years of data', Resources Policy, vol. 77, art. 102662, pp. 1-7, doi : 10.1016/j.resourpol.2022.102662.en_US
dc.identifier.issn0301-4207 (print)
dc.identifier.issn1873-7641 (online)
dc.identifier.other10.1016/j.resourpol.2022.102662
dc.identifier.urihttps://repository.up.ac.za/handle/2263/88880
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2022 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was submitted for publication in Resources Policy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms are not been reflected in this document. A definitive version was subsequently published in Resources Policy, vol. 77, art. 102662, pp. 1-7, 2022, doi : 10.1016/j.resourpol.2022.102662.en_US
dc.subjectMachine learningen_US
dc.subjectRandom forestsen_US
dc.subjectOil uncertaintyen_US
dc.subjectMacroeconomic uncertaintyen_US
dc.subjectFinancial uncertaintyen_US
dc.subjectUnemployment rateen_US
dc.subjectForecastingen_US
dc.subjectUnited Kingdom (UK)en_US
dc.titleOil-price uncertainty and the U.K. unemployment rate : a forecasting experiment with random forests using 150 years of dataen_US
dc.typePreprint Articleen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Gupta_OilPrice_2022.pdf
Size:
328.61 KB
Format:
Adobe Portable Document Format
Description:
Preprint Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description: