一个GMM方法来处理关于回归器的缺失数据.
Jason Abrevaya1, Stephen G Donald1
1University of Texas.
概括
本研究引入了一个新的通用时刻方法 (GMM) 框架,以解决线性回归中缺少的数据. 拟议的GMM估计器为处理缺失的解释变量提供了有效的解决方案,优于传统方法.
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
- 数据分析 数据分析
背景情况:
- 解释变量中缺少数据是经验研究中普遍存在的问题.
- 现有的方法,如线性归算,完整案例分析和虚拟变量方法都有局限性.
研究的目的:
- 为处理线性回归中的缺失值提出一个通用的通用时刻方法 (GMM) 框架和估计器.
- 为了提供一个在标准归算假设下一致的高效估计器.
- 开发一种方法,包括对缺失假设的规范测试.
主要方法:
- 开发一种新的GMM估计器,用于缺少解释变量的线性回归.
- 拟议的GMM估计器与完整数据,线性归算和虚拟变量方法进行比较.
- 在各种缺失数据场景下分析估计器的一致性和效率.
主要成果:
- 根据线性归算方法的一致性所需的假设,GMM估计器被证明是有效的.
- 假变量方法被发现通常不一致,即使数据完全随机丢失.
- 当一致时,模拟变量方法的效率可能低于完整数据方法.
结论:
- 拟议的GMM框架提供了一个强大的和有效的方法来解决线性回归中缺少的数据.
- 该GMM估计器为现有方法提供了有价值的替代方案,提供了更好的一致性和效率.
- 研究人员可以利用这种GMM方法在缺少解释变量的情况下进行更可靠的经验分析.
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