在有序响应模型中选择随机系数:用于检测家庭调查异质性的框架
1Federal Reserve Bank of Kansas City, Kansas City, MO, USA.
Journal of applied statistics
|March 13, 2024
概括
本研究引入了贝叶斯的方法来识别有序响应模型中的不同关系. 该方法有效地检测了影响家庭层面财务期望的因素的异质性.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 心理测量 心理测量 心理测量
背景情况:
- 订制响应模型被广泛使用,但通常假设均性.
- 检测共变量-结果关系中的异质性对于准确的推断至关重要.
- 对于线性模型的现有方法并不直接适用于有序结果.
研究的目的:
- 开发一种贝叶斯方法来检测有序响应模型中的异质性.
- 将随机系数选择方法扩展到有序设置.
- 为在模型不确定性下估计和评估边际效应提供一个框架.
主要方法:
- 一个带有马尔科夫链蒙特卡洛 (MCMC) 算法的等级贝叶斯模型.
- 切割点的高效估计,解决识别和订单的限制.
- 一个评估边际效应的框架,结合固定和随机效应,结合模型不确定性.
主要成果:
- 该方法成功检测到当前存在的随机效应.
- 准确的参数估计和高效的后端采样,低自相关性.
- 对调查数据的应用揭示了财务状况关系中家庭层面的显著异质性.
结论:
- 开发的贝叶斯方法有效地检测了有序响应模型中的异质性.
- 该方法提供了对边际效应的可靠估计,考虑到模型不确定性.
- 在家庭的财务期望中存在显著的异质性,这突显了个人层面分析的重要性.
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