Factor-augmented regression models with structural change

This paper considers a factor-augmented regression model in the presence of structural change. We propose a two-step procedure to estimate the coefficients of explanatory variables. We show that when the number of units (N) and the number of periods (T) are large and comparable, the proposed two-step estimator is T-consistent and has the same limiting distribution as if the unobservable factors were observed. Monte Carlo simulations confirm our theoretical results and show good finite sample performance of the two-step estimator.