Econ Papers Daily / 期刊论文
非线性模型中缺失协变量值的插补
Imputing Missing Covariate Values in Nonlinear Models
摘要
I propose a new imputation estimator for missing covariate values in nonlinear models. The estimator provides efficiency gains relative to just using the complete cases, and is consistent for any model that falls under M‐estimation. This is unlike the commonly used dummy variable method and regression imputation, which I show to be generally inconsistent in nonlinear models. The proposed estimator is straightforward to implement, and relies only on the commonly used assumptions on missingness. To test these assumptions, I provide a novel and simple variable addition test. I show how the framework applies to nonlinear models for fractional and nonnegative responses.
中文摘要
我提出了一种新的插补估计量，用于非线性模型中缺失协变量值的处理。该估计量相较于仅使用完整案例的方法能带来效率提升，并且对于任何属于M估计框架的模型均具有一致性。这与常用的虚拟变量方法和回归插补不同，我证明后者在非线性模型中通常不一致。所提出的估计量实施简便，仅依赖于关于缺失性的常用假设。为检验这些假设，我提供了一种新颖且简单的变量添加检验。我展示了该框架如何应用于分数响应和非负响应的非线性模型。