A Linear Estimator for Factor-Augmented Fixed-T Panels With Endogenous Regressors

A novel method-of-moments approach is proposed for the estimation of factor-augmented panel data models with endogenous regressors whenTis 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 fixedTpanels, such as local minima-related problems, a sensitivity to particular normalization 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.

Automated summary

A novel method is proposed for estimating factor-augmented panel data models with endogenous regressors. The method approximates the unobserved common factors using observed factor proxies and employs linear moment conditions to address issues with existing estimators. Application to empirical data on urban water demand confirms the good performance of the proposed method.