一种新的混合PSO-MIDAS模型及其应用于美国GDP预测
Feng Shen1,2, Xiaodong Yan1, Yuhuang Shang3
1School of Finance, Southwestern University of Finance and Economics, Chengdu, PR China.
PloS one
|December 11, 2024
概括
本研究介绍了一种机器学习方法,使用粒子群优化 (PSO) 来改进混合数据采样 (MIDAS) 回归用于GDP预测. 公共服务局-MIDAS模型显著提高了预测准确性,特别是在更长的时间范围内.
科学领域:
- 计量经济学 计量经济学
- 机器学习 机器学习
- 宏观经济预测 宏观经济预测
背景情况:
- 传统的混合数据采样 (MIDAS) 模型在选择最佳滞后结构进行预测时面临着挑战.
- 对于滞后结构选择的现有方法可能是不理想的,影响预测的准确性和效率.
研究的目的:
- 在MIDAS回归中用机器学习方法取代传统的滞后结构选择,使用粒子群优化 (PSO).
- 自动优化MIDAS模型滞后结构,以提高国内生产总值 (GDP) 预测准确度.
- 为了解决预测准确性和预测成本之间的权重问题.
主要方法:
- 实现了粒子群优化 (PSO) 算法,以优化MIDAS回归框架内的滞后结构.
- 开发了一个PSO-MIDAS模型,用于单变量和多变量GDP预测.
- 使用Diebold-Mariano测试来比较预测准确度与基准模型.
主要成果:
- 与基准模型相比,PSO-MIDAS模型在较长的预测时间范围内显示出明显优异的预测准确性.
- 经验结果显示,当预测时间超过两个季度时,单变量和多变量PSO-MIDAS模型的预测准确度平均提高了10%.
- 该PSO算法的优化效应比其他基准模型更为明显.
结论:
- 粒子群优化 (PSO) 算法有效地解决了传统MIDAS滞后结构选择中的局限性.
- 公共服务局-MIDAS模型为GDP预测提供了增强的预测潜力,特别是对于更长的预测时间.
- 这种基于机器学习的方法代表了混合频时间序列预测的创新进步.
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