Balcilar, MGabauer, DavidGupta, RPierdzioch, C2025-03-122022-01-072025-03-122022-090277-66933M5VN (isidoc)https://hdl.handle.net/10182/18263Utilizing 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 12 regions of the UK 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.pp.1049-1064en© 2022 The Authors. Journal of Forecasting published by John Wiley & Sons Ltd.forecastingmachine learningregional output growthuncertaintyUKJournal Article10.1002/for.28511099-131XANZSRC::3802 EconometricsANZSRC::4905 Statisticshttps://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives