Missing dependent variables in fixed-effects models.
Missing dependent variables in fixed-effects models.
复制标题
固定效应模型中缺少因变量。
DOI：
10.1016/j.jeconom.2018.12.011
复制
发表时间：
2019
影响因子：
6.3
通讯作者：
Abrevaya,Jason
中科院分区：
文献类型：
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作者：
Abrevaya,Jason
本文考虑了因变量可能缺失的线性固定效应模型的估计。对于缺少因变量的横截面单位，使用所有时间段的协变量信息可以提供相对于完整数据方法的效率增益。为静态固定效应模型提出了基于 Chamberlain（1982、1984）的经典最小距离（CMD）估计器，该估计器在随机缺失（MAR）类型假设下是一致的。在某些情况下，模型参数的识别甚至不需要因变量的“内部”变化。 CMD 估计方法扩展到具有滞后因变量的（自回归）固定效应模型的情况。蒙特卡洛模拟研究了 CMD 方法相对于现有方法的性能。还讨论了具有顺序外生性和缺失协变量的模型的扩展。
This paper considers estimation of linear fixed-effects models in which the dependent variable may be missing. For cross-sectional units with dependent variables missing, use of covariate information from all time periods can provide efficiency gains relative to complete-data methods. A classical minimum distance (CMD) estimator based upon Chamberlain (1982, 1984), which is consistent under a missing-at-random (MAR) type assumption, is proposed for the static fixed-effects model. In certain circumstances, it is shown that “within” variation in the dependent variable is not even required for identification of the model parameters. The CMD estimation approach is extended to the case of (autoregressive) fixed-effects models with lagged dependent variables. Monte Carlo simulations investigate the performance of the CMD approach relative to existing methods. Extensions to models with sequential exogeneity and missing covariates are also discussed.