Beyhum, Jad and Gautier, Éric (2023) Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors. Journal of Business and Economic Statistics, vol. 41 (n° 1). pp. 270-281.

Abstract

This article considers linear panel data models where the dependence of the regressors and the unobservables is modeled through a factor structure. The number of time periods and the sample size both go to infinity. Unlike in most existing methods for the estimation of this type of models, nonstrong factors are allowed and the number of factors can grow to infinity with the sample size. We study a class of two-step estimators of the regression coefficients. In the first step, factors and factor loadings are estimated. Then, the second step corresponds to the panel regression of the outcome on the regressors and the estimates of the factors and the factor loadings from the first step. The estimators enjoy double robustness. Different methods can be used in the first step while the second step is unique. We derive sufficient conditions on the first-step estimator and the data generating process under which the two-step estimator is asymptotically normal. Assumptions under which using an approach based on principal components analysis in the first step yields an asymptotically normal estimator are also given. The two-step procedure exhibits good finite sample properties in simulations. The approach is illustrated by an empirical application on fiscal policy.

Available Versions of this Item

• Factor and factor loading augmented estimators for panel regression. (deposited 27 May 2021 13:12)
  • Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors. (deposited 22 Feb 2024 14:44) [Currently Displayed]