短期基尼系数估计使用非线性自回归多层感知模型.
Megat Syahirul Amin Megat Ali1, Azlee Zabidi2, Nooritawati Md Tahir3
1Microwave Research Institute (MRI), Universiti Teknologi Mara (UiTM), Shah Alam, Malaysia.
Heliyon
|February 29, 2024
概括
这项研究引入了一种新的方法,用于短期预测金尼系数,金尼系数是收入不平等的关键指标. 开发的模型准确地预测了波动,为经济差异提供了及时的见解.
科学领域:
- 社会经济学 社会经济学
- 计量经济学 计量经济学
- 计算社会科学 计算社会科学
背景情况:
- 贫困是一个复杂的全球问题,与经济,政治和社会因素有关,需要像联合国可持续发展目标一样采取集体行动.
- 吉尼系数衡量收入不平等,这是一个关键指标,经常与贫困率相关. 传统的年度计算由于后勤挑战和数据转换速度缓慢而面临限制.
研究的目的:
- 通过开发短期预测方法来解决传统吉尼系数计算的局限性.
- 为即时了解收入不平等转移提供一个工具,特别是在快速经济转型期间,如吉格经济.
主要方法:
- 使用了系统识别 (SI) 原则,特别是非线性自动回归 (NAR) 模型,用多层感知器 (MLP) 增强.
- 测试了各种参数,包括输出滞后空间,隐藏单位和初始随机种子,以优化马来西亚吉尼系数 (1987-2015) 的模型.
- 通过一步前进 (OSA) 预测,剩余相关性分析和剩余组图验证了模型.
主要成果:
- 在28年的时间里,NAR-MLP模型在估计马来西亚的吉尼系数方面表现出了很高的效率.
- 通过1.14 × 10-7的平均平方误差 (MSE) 实现了优越的模型匹配.
- 通过无关联的残余值确认模型有效性,表明短期预测的可靠性.
结论:
- 拟议的NAR-MLP方法是预测吉尼系数短期变化的有效和有用工具.
- 这种方法在传统的手动方法上提供了显著的改进,通过在更小的时间步骤中实现预测.
- 该研究证实了该模型能够捕捉收入不平等的动态变化,从而有助于及时的政策干预.
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