A Linear Estimator for Factor-Augmented Fixed-T Panels With Endogenous Regressors
提出一种新的矩估计方法，用于估计固定T且存在内生回归变量的因子增强面板数据模型，通过观测因子代理逼近未观测的共同因子，得到参数的线性矩条件，避免了非线性估计量在固定T面板中存在的局部极小值、标准化敏感性和全局识别不足等问题。
A novel method-of-moments approach is proposed for the estimation of factor-augmented panel data models with endogenous regressors when T is fixed. The underlying methodology involves approximating the unobserved common factors using observed factor proxies. The resulting moment conditions are linear in the parameters. The proposed approach addresses several issues which arise with existing nonlinear estimators that are available in fixed T panels, such as local minima-related problems, a sensitivity to particular normalisation schemes, and a potential lack of global identification. We apply our approach to a large panel of households and estimate the price elasticity of urban water demand. A simulation study confirms that our approach performs well in finite samples.