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Residual XGBoost regression—Based individual moving range control chart for Gross Domestic Product growth monitoring

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  • Rahida Rihhadatul Aisy
  • Latifatuz Zulfa
  • Yolanda Rahim
  • Muhammad Ahsan

Abstract

Accurate and reliable Gross Domestic Product (GDP) forecasting is indispensable for informed economic policymaking and risk management. Autocorrelation, a prevalent characteristic of macroeconomic time series, poses significant challenges to traditional forecasting methodologies and statistical process control. This study introduces a novel approach to GDP forecasting and monitoring by integrating XGBoost regression, a robust machine learning algorithm, with Individual and Moving Range (I-MR) control charts. By effectively capturing complex nonlinear relationships and mitigating autocorrelation, the proposed model offers enhanced predictive accuracy compared to conventional methods. Empirical results demonstrate the model’s efficacy in phase I, aligning closely with actual GDP values. However, phase II analysis reveals discrepancies, suggesting the need for further model refinement and the potential incorporation of additional economic indicators to improve forecast precision.

Suggested Citation

  • Rahida Rihhadatul Aisy & Latifatuz Zulfa & Yolanda Rahim & Muhammad Ahsan, 2025. "Residual XGBoost regression—Based individual moving range control chart for Gross Domestic Product growth monitoring," PLOS ONE, Public Library of Science, vol. 20(5), pages 1-21, May.
    • Handle: RePEc:plo:pone00:0321660
    DOI: 10.1371/journal.pone.0321660
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    References listed on IDEAS

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    1. Hill, Hal & Menon, Jayant, 2010. "ASEAN Economic Integration: Features, Fulfillments, Failures and the Future," Working Papers on Regional Economic Integration 69, Asian Development Bank.
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