模型平均估计面板数据模型的估计,使用许多仪器和增强
Hao Hao1, Bai Huang2, Tae-Hwy Lee3
1Global Data, Insight & Analytics, Ford Motor Company, Dearborn, MI, USA.
本研究介绍了促进面板数据模型的规范化,以解决固定效果双阶最小平方 (FE-2SLS) 中太多仪器的问题. 提出了一个新的模型平均估计器,改进了现有的FE和FE-2SLS方法.
科学领域:
- 计量经济学 计量经济学
- 统计建模 统计建模
- 面板数据分析数据分析
背景情况:
- 固定效果双阶最小方程 (FE-2SLS) 广泛用于面板数据.
- 对于FE-2SLS来说,它面临着众多仪器和弱固体性的挑战.
- 现有的方法可能会出现不一致或收益有限的情况.
研究的目的:
- 为面板数据模型提出一个促进规范化程序.
- 为了解决FE-2SLS中的"许多仪器"问题.
- 开发一个类似斯坦的模型平均估计器,将FE和FE-2SLS-Boosting结合起来.
主要方法:
- 为面板数据开发了一个Boosting规范化程序.
- 构建一个类似斯坦的模型平均估计器.
- 蒙特卡洛模拟和经验应用用于评估.
主要成果:
- 提议的促进规范化有效地处理了许多工具的问题.
- 模型平均估计器利用FE和FE-2SLS-Boosting的优势.
- 检查有限样本属性,证明估计器的性能.
结论:
- 提升规范化程序在FE-2SLS.LS中为"许多仪器"提供了解决方案.
- 类似斯坦的模型平均估计器为面板数据分析提供了改进的方法.
- 通过模拟和实际应用来验证这些发现.
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