Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning

Mehmet Balcilar, David Gabauer, Rangan Gupta and Christian Pierdzioch

No 202111, Working Papers from University of Pretoria, Department of Economics

Abstract: Utilizing a machine-learning technique known as random forests, we study whether regional output growth uncertainty helps to improve the accuracy of forecasts of regional output growth for twelve regions of the United Kingdom using monthly data for the period from 1970 to 2020. We use a stochastic-volatility model to measure regional output growth uncertainty. We document the importance of interregional stochastic volatility spillovers and the direction of the transmission mechanism. Given this, our empirical results shed light on the contribution to forecast performance of own uncertainty associated with a particular region, output growth uncertainty of other regions, and output growth uncertainty as measured for London as well. We find that output growth uncertainty significantly improves forecast performance in several cases, where we also document cross-regional heterogeneity in this regard.

Keywords: Regional Output Growth; Uncertainty; United Kingdom; Forecasting; Machine Learning (search for similar items in EconPapers)
JEL-codes: C22 C53 D8 E32 E37 R11 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2021-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-geo, nep-mac, nep-ore and nep-ure
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202111

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