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Uncertainty and forecastability of regional output growth in the UK: Evidence from machine learning

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  • Mehmet Balcilar
  • David Gabauer
  • Rangan Gupta
  • Christian Pierdzioch

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 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.

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  • Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2022. "Uncertainty and forecastability of regional output growth in the UK: Evidence from machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1049-1064, September.
    • Handle: RePEc:wly:jforec:v:41:y:2022:i:6:p:1049-1064
    DOI: 10.1002/for.2851
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    2. Eren Bas & Erol Eğrioğlu, 2023. "A new recurrent pi‐sigma artificial neural network inspired by exponential smoothing feedback mechanism," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 802-812, July.

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