Research Article

Impact of Energy Prices and Macroeconomic Variables on GDP Prediction UK: Machine Learning Approach

Authors

  • Piyumi Perera PhD Student, Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK
  • Anil Fernando Professor, Department of Computer and Information Sciences, University of Strathclyde, Glasgow, UK

Abstract

Gross Domestic Product (GDP) is one of the critical indicators of an economy. This study aims to predict the GDP of the United Kingdom using vital macroeconomic variables from 1990 to 2018 as predictors, which include energy prices, unemployment rate, Real Effective Exchange Rate (REER) inflation and net migration. Several machine learning models, namely Support Vector Regression (SVR), Random Forest (RF) and Gradient Boosting Machines (GBM), were implemented, analysed and compared. The models were trained on both scaled and unscaled data, with hyperparameter tuning applied to optimise performance. The models’ performances and accuracy were analysed by employing evaluation metrics Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). As per the findings, after hyperparameter tuning, the SVR model performed best in GDP prediction, followed by GBM. The results of this study underscore the critical role of macroeconomic variables in GDP prediction while highlighting the potential of machine learning models to produce valuable and reliable insight into economic forecasting.

Article information

Journal

Journal of Business and Management Studies

Volume (Issue)

6 (5)

Pages

113-124

Published

2024-10-03

How to Cite

Impact of Energy Prices and Macroeconomic Variables on GDP Prediction UK: Machine Learning Approach. (2024). Journal of Business and Management Studies, 6(5), 113-124. https://doi.org/10.32996/jbms.2024.6.5.13

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Keywords:

GDP Prediction, Economic Forecasting, Machine Learning, Energy Prices, Macroeconomic variables