Asymptotic distribution of factor augmented estimators for panel regression

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 T / N → 0 and N / T 3 → 0 under regularity. Monte Carlo studies verify the asymptotic theory.