使用机器学习方法预测消费者价格指数:来自美国的证据
Tien-Thinh Nguyen1, Hong-Giang Nguyen2, Jen-Yao Lee1
1Department of International Business, National Kaohsiung University of Science and Technology, Kaohsiung City, 807618, Taiwan.
Heliyon
|October 16, 2023
概括
准确预测美国消费者价格指数 (CPI) 对经济稳定至关重要. 与其他模型相比,多变量适应回归线 (MARS) 在预测CPI方面表现出更高的准确性,有助于经济政策决策.
科学领域:
- 经济学 经济学 经济学
- 计量经济学 计量经济学
- 计算经济学计算经济学
背景情况:
- 消费者价格指数 (CPI) 是通货膨胀和经济健康的一个关键指标.
- 准确的CPI预测对于有效的经济政策和国家发展至关重要.
- 现有的预测模型需要进行评估,以提高预测能力.
研究的目的:
- 使用各种机器学习模型预测美国消费者价格指数 (CPI).
- 为了比较多变量线性回归 (MLR),支向量回归 (SVR),自行回归分布式滞后 (ARDL) 和多变量自适应回归支柱 (MARS) 的准确性,用于CPI预测.
- 确定最准确的美国消费者价格指数预测模型以支持经济政策.
主要方法:
- 利用了2017年1月至2022年2月美国消费者消费指数的时间序列数据.
- 使用MLR,SVR,ARDL和MARS模型进行预测.
- 数据被分为80%用于培训和20%用于测试;模型性能使用MAPE,MAE,RMSE,R-squared和确定性相关性等指标进行评估.
主要成果:
- 所有测试的模型 (MLR,SVR,ARDL,MARS) 在预测美国消费者消费指数方面都取得了很高的准确性.
- 与MLR,SVR和ARDL相比,MARS算法在测试阶段表现出最高的准确性.
- 关键预测变量包括原油价格,世界黄金价格和联邦基金有效利率.
结论:
- 马斯是预测美国CPI的一种非常准确的方法.
- 准确的CPI预测可以显著帮助美国政府制定经济政策和国家发展.
- 这些发现为经济监督,部门管理和社会保障规划提供了宝贵的见解.
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