在基于EM算法的Heckman选择模型中的影响诊断
Marcos S Oliveira1, Marcos O Prates2, Christian E Galarza3
1Department of Mathematics and Statistics, Federal University of Sao Joao Del Rei, Sao Joao Del Rei, MG, Brazil.
这项研究为赫克曼选择模型引入了新的诊断方法,这对计量经济学至关重要. 这些技术在HeckmanEM R包中实施,在统计分析中有效识别有影响力的数据点.
科学领域:
- 计量经济学 计量经济学
- 统计建模 统计建模
背景情况:
- 赫克曼选择模型对于解决统计估计中的选择偏差至关重要.
- 现有的诊断技术可能无法完全捕捉这些模型中具有影响力的观察结果.
研究的目的:
- 为通过EM算法估计的Heckman选择模型开发新的诊断技术.
- 提供可靠的方法来识别在选择t和正常模型中具有影响力的观测.
主要方法:
- 利用EM算法来估计赫克曼选择模型.
- 开发了基于完整数据日志概率的全球和本地影响分析.
- 探索了四个扰动方案用于局部影响分析.
主要成果:
- 建议的诊断措施有效地识别了有影响力的观察.
- 模拟研究和真实数据应用验证了该方法.
- R包HeckmanEM包含了开发的算法和诊断.
结论:
- 新的诊断技术提高了赫克曼选择模型估计的可靠性.
- 赫克曼EM R包为应用研究人员提供了一个实用的工具.
- 准确识别影响点对于强大的计量经济学分析至关重要.
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