This paper extends Pesaranís (2006) work on common correlated effects (CCE)
estimators for large heterogeneous panels with a general multifactor error structure
by allowing for unknown common structural breaks. Structural breaks due to new
policy implementation or major technological shocks, are more likely to occur over
a longer time span. Consequently, ignoring structural breaks may lead to inconsistent
estimation and invalid inference. We propose a general framework that includes
heterogeneous panel data models and structural break models as special cases. The
least squares method proposed by Bai (1997a, 2010) is applied to estimate the common
change points, and the consistency of the estimated change points is established.
We find that the CCE estimator have the same asymptotic distribution as if the true
change points were known. Additionally, Monte Carlo simulations are used to verify
the main results of this paper
