使用混合整数优化的最佳子集仪表变量选择方法,并将其应用于与健康有关的生活质量和教育工资分析
Muhammad Qasim1,2, Kristofer Månsson1, Narayanaswamy Balakrishnan2
1Department of Economics, Finance and Statistics, Jönköping International Business School, Jönköping University, Sweden.
选择最佳子集的计算挑战是通过混合整数优化 (MIO) 来解决的. 使用MIO的新最佳子集工具变量 (BSIV) 方法提供了可靠的因果效应估计,优于现有技术.
科学领域:
- 计量经济学 计量经济学 计量经济学
- 统计建模 统计建模
- 因果推理的原因推理.
背景情况:
- 经典的最佳子集选择在计算上是难以处理的 (NP-hard).
- 仪表变量 (IV) 回归面临无效IV的挑战,导致偏差.
- 现有的IV方法可能对违反IV假设的情况敏感.
研究的目的:
- 通过混合整数优化 (MIO) 将最佳子集选择扩展到仪器变量回归.
- 开发一个强大的最佳子集仪表变量 (BSIV) 估计器用于因果推理.
- 评估拟议的BSIV方法的性能与已建立的IV技术相比.
主要方法:
- 将混合整数优化 (MIO) 算法纳入用于IV回归的最佳子集选择框架.
- 最好的子集工具变量 (BSIV) 估计器的开发.
- 蒙特卡罗模拟将BSIV与两阶段最小平方 (2SLS),拉索型IV和两样本估计器进行比较.
- 应用到与健康有关的生活质量和教育与工资关系的现实世界数据集.
主要成果:
- 与2SLS,拉索型IV和两样本估计器相比,BSIV方法在蒙特卡洛模拟中表现出更高的性能.
- 在估计因果效应方面,BSIV显示偏差减少和相对效率提高.
- 该方法在处理潜在无效仪表变量的情况下被证明是有效的.
结论:
- 提议的BSIV方法,利用MIO,为仪表变量回归提供了一个强大的,在计算上可行的方法.
- BSIV为因果效应估计提供了有价值的替代方案,特别是当IV有效性不确定时.
- 通过对健康和经济数据集的经验分析,BSIV的实用性得到了证实.
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