Forecasting The Economic Growth of Sverdlovsk Region: A Comparative Analysis of Machine Learning, Linear Regression and Autoregressive Models

Daniel Musafiri Balungu and Avinash Kumar

Journal of Applied Economic Research, 2024, vol. 23, issue 3, 674-695

Abstract: The relevance of accurate economic forecasting cannot be overstated in today's rapidly changing global economy. Decision-makers in both the public and private sectors rely heavily on reliable forecasts to make informed decisions about resource allocation, investment strategies, and policy development. In this context, the importance of leveraging advanced analytical techniques, such as machine learning, to improve the accuracy of economic forecasts has become increasingly apparent. The purpose of this study is to explore the use of different forecasting models in predicting the dynamics of the GRP of the Sverdlovsk region in Russia, with a focus on the potential benefits of integrating machine learning techniques. The central hypothesis underlying this study is that machine learning models have the potential to outperform traditional autoregressive models in predicting economic growth. By leveraging a rich dataset that includes yearly GRP data and macroeconomic indicators from 2005 to 2022, the research procedure involved a comprehensive comparative analysis of different modeling approaches. The main findings of this study highlight the superior performance of the random forest model in forecasting GRP growth compared to the traditional SARIMAX model. These results not only provide valuable insights into the predictive power of machine learning algorithms in economic forecasting but also underscore the potential benefits of adopting advanced analytical techniques in decision-making processes. By demonstrating the superiority of machine learning models in predicting economic indicators like GRP, this study contributes to the growing body of literature on the application of data-driven approaches in economic analysis. Ultimately, the theoretical and practical significance of these findings lies in their implications for improving the accuracy and reliability of economic forecasts, thereby enabling more informed decision-making in a rapidly evolving economic landscape.

Keywords: economic growth; gross regional product; regional economy; machine learning; time series (search for similar items in EconPapers)
JEL-codes: C68 R15 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:aiy:jnjaer:v:23:y:2024:i:3:p:674-695

DOI: 10.15826/vestnik.2024.23.3.027

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