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对于功能并发回归模型的仪器变量估计.

Justin Petrovich1, Bahaeddine Taoufik2, Zachary George Davis3

  • 1Department of Business Administration, Saint Vincent College, Latrobe, PA, USA.

Journal of applied statistics
|June 12, 2024
PubMed
概括

本研究引入了一种新的功能并发回归模型,用仪表变量来估计劳动力供应弹性. 该方法从稀疏的功能数据准确估计劳动力供应弹性,纠正工资内在性.

科学领域:

  • 计量经济学 计量经济学
  • 劳动经济学 劳动经济学
  • 统计建模 统计建模

背景情况:

  • 估计劳动力供应弹性对于经济政策至关重要.
  • 现有的功能回归模型经常与稀疏的数据和内源性作斗争.
  • 工资内质性是劳动力供应研究中的一个常见挑战.

研究的目的:

  • 为劳动力供应弹性估计提出一种新的功能并发回归模型.
  • 为了应对稀疏的功能数据和内生工资的挑战.
  • 为功能并发回归模型适应仪表变量方法.

主要方法:

  • 利用了1988-2014年当前人口调查数据.
  • 在功能并发回归框架内开发了一种两阶段最小平方 (2SLS) 方法.
  • 为稀疏的功能数据量身定制估计方法.

主要成果:

  • 拟议的2SLS功能并发回归模型有效地消除了来自天真模型的偏差.
  • 即使在适度的样本大小下,也可以获得准确的系数估计.
  • 证明了该模型适用于稀疏的功能数据的适用性.

结论:

关键词:
功能并发回归的功能回归这是一个仪器变量.劳动力供应弹性劳动力供应弹性功能数据很少,功能数据很少.

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  • 具有仪器变量的新型功能并发回归模型是一个显著的进步.
  • 这种方法提供了一个强大的方法来估计内生工资和稀疏数据的劳动力供应弹性.
  • 这些发现对经济研究和政策分析有影响.