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使用结构方程模型对工业预警系统模拟进行制造周期预测:印度尼西亚案例
Tirta Wisnu Permana1, Gatot Yudoko1, Eko Agus Prasetio1
1School of Business and Management, Institute of Technology Bandung (ITB), Bandung, Indonesia.
Heliyon
|January 27, 2025
概括
整合短期,中期和长期复合领先指数 (CLI) 提高了印度尼西亚制造业周期 (ManC) 的预测. 相互连接的CLI提供了优越的预测能力比单个指数,通过部分最小平方-结构方程建模验证.
科学领域:
- 经济学 经济学 经济学
- 计量经济学 计量经济学
- 经济预测 经济预测
背景情况:
- 综合领先指数 (CLI) 对于经济周期预测至关重要.
- 现有的研究往往侧重于个别的CLI,可能会限制预测准确性.
- 了解短期,中期和长期CLI的相互作用对于稳健的经济预测至关重要.
研究的目的:
- 整合短期,中期和长期复合领先指数 (CLI) 以提高预测能力.
- 调查CLI之间的关系,以预测印度尼西亚制造业周期 (ManC).
- 为了验证部分最小平方-结构方程建模 (PLS-SEM) 在ManC预测中的应用.
主要方法:
- 利用了2010年第一季度至2022年第二季度的季度数据.
- 用于分析的部分最小平方-结构方程建模 (PLS-SEM).
- 纳入了五个构造,代表着影响制造业周期的关键经济部门,包括短期领先经济指数 (SLEI),国际贸易道 (ITC),财政周期 (FC) 和货币周期 (MC).
主要成果:
- 证明相互连接的CLI与单个CLI相比,提供了增强的预测能力.
- 通过使用集成CLI方法,成功预测了印度尼西亚的制造业周期 (ManC).
- 验证了PLS-SEM在分析复杂的经济关系以进行预测方面的有效性.
结论:
- 多种综合领先指数的整合显著提高了经济周期预测的准确性.
- PLS-SEM是一种适合模拟经济指标中的复杂关系以预测制造周期的方法.
- 这项研究为在经济政策和规划中利用相互连接的CLI提供了一个强大的框架.
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