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

Author

Listed:
  • Mehmet Balcilar

    (Eastern Mediterranean University, Famagusta, via Mersin 10, Northern Cyprus, Turkey)

  • David Gabauer

    (Data Analysis Systems, Software Competence Center Hagenberg, Austria)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Private Bag X20, Hatfield, 0028, South Africa)

  • Christian Pierdzioch

    (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

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.

Suggested Citation

  • Mehmet Balcilar & David Gabauer & Rangan Gupta & Christian Pierdzioch, 2021. "Uncertainty and Forecastability of Regional Output Growth in the United Kingdom: Evidence from Machine Learning," Working Papers 202111, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:202111
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    References listed on IDEAS

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    Cited by:

    1. Salisu, Afees A. & Gupta, Rangan & Karmakar, Sayar & Das, Sonali, 2022. "Forecasting output growth of advanced economies over eight centuries: The role of gold market volatility as a proxy of global uncertainty," Resources Policy, Elsevier, vol. 75(C).

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    More about this item

    Keywords

    Regional Output Growth; Uncertainty; United Kingdom; Forecasting; Machine Learning;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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