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

Abstract

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 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.

Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects

“…The model above has been employed in a wide variety of fields, including in economics and finance. Estimation of this model has been studied by Pesaran (2006), Bai and Li (2014), Westerlund and Urbain (2015), Juodis and Sarafidis (2020), Cui et al (2020) to mention a few.…”

Recent Developments on Factor Models and its Applications in Econometric Learning

“…Alternatively, we can also use transformations of the initial observation x t for t = 0, which was considered by Juodis and Sarafidis (2020). If y 0 is independent of {u t : t ≥ 1}, we can apply w i,k = φ k (y i,0 ) .…”

Estimation of a Factor-Augmented Linear Model with Applications Using Student Achievement Data

“…While the estimators proposed for panels with large dimensions have been widely popular, other methods developed for panels with small, or fixed, T have not been frequently adopted by practitioners conducting empirical academic research. One reason, as mentioned in Juodis and Sarafidis (2020) and illustrated in Attanasio, Meghir, and Nix (2020) and Del Bono, Kinsler, and Pavan (2020), is that identification of the factor model requires normalization restrictions that matter for the interpretation of results (Agostinelli and Wiswall, 2016). In some cases identification is achieved through the use of dedicated measurements, where a priori knowledge is used to associate certain measurements uniquely with specific factors (for example a test can be associated uniquely with a given skill e.g.…”