In this paper we derive an asymptotic theory for linear panel regression augmented with estimated common factors. We give conditions under which the estimated factors can be used in place of the latent factors in the regression equation. For the principal components estimate of the factor space it is shown that these conditions are satisfied when TN→0 and N T3→0 under regularity. Monte Carlo studies verify the asymptotic theory.
- Cross section dependence
- Factor augmented estimator
- Factor augmented panel regression
- Interactive fixed effects
- Principal component augmented estimator
ASJC Scopus subject areas
- Economics and Econometrics