Weak Identification in Low-Dimensional Factor Models with One or Two Factors
提出一种重新参数化方法，将一或两个因子的低维因子模型转化为适用于广义矩方法弱识别理论的形式，使“插件”检验可用于子向量假设，模拟显示其比原参数化的识别稳健检验更少保守，并应用于父母对子女投资的因子模型。
Abstract 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.