论文标题
在具有一个或两个因素的低维因子模型中识别弱
Weak Identification in Low-Dimensional Factor Models with One or Two Factors
论文作者
论文摘要
本文介绍了如何用一个或两个因素对低维因子模型进行重新聚集，以适合为矩的通用方法开发的弱识别理论。这里称为“插件”测试的一些识别式测试需要重新聚集，以将弱标识的参数与强识别的参数区分开。本文的重新聚集化使插入式测试可用于具有一个或两个因素的低维因子模型中的亚矢量假设。仿真表明，插件测试不如使用原始参数化的标识射击测试保守。包括对儿童父母投资的因素模型的经验应用。
This paper describes how to reparameterize low-dimensional factor models with one or two factors to fit weak identification theory developed for generalized method of moments models. Some identification-robust tests, here called "plug-in" tests, require a reparameterization to distinguish weakly identified parameters from strongly identified parameters. The reparameterizations in this paper make plug-in tests available for subvector hypotheses in low-dimensional factor models with one or two factors. Simulations show that the plug-in tests are less conservative than identification-robust tests that use the original parameterization. An empirical application to a factor model of parental investments in children is included.
