Model Evaluation Performance.

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.

精准可靠的国内生产总值（Gross Domestic Product，GDP）预测，对于科学制定经济政策与开展风险管理而言不可或缺。自相关性作为宏观经济时序数据的普遍特征，给传统预测方法与统计过程控制带来了显著挑战。本研究提出一种全新的GDP预测与监测方法：将性能优异的机器学习算法XGBoost回归，与单值-移动极差（Individual and Moving Range，I-MR）控制图相结合。该方法能够有效捕捉复杂非线性关联并缓解自相关性问题，相较传统方法具备更优异的预测精度。实证结果表明，该模型在第一阶段（Phase I）的表现出色，预测结果与实际GDP数值高度契合。但第二阶段（Phase II）的分析结果显示存在偏差，这表明需要对模型进行进一步优化，同时可考虑纳入更多经济指标以提升预测精度。