The Common Correlated Effects (CCE) approach by Pesaran2006 is a popular method for estimating panel data models with interactive effects. Due to its simplicity, i.e. unobserved common factors are approximated with cross-section averages of the observables, the estimator is highly flexible and...

In this paper we re-visit a recent theoretical idea introduced by Phillips and Lee (2015). They examine an empirically relevant situation when multiple time series under consideration exhibit different degrees of non-stationarity. By bridging the asymptotic theory of the local to unity and...

A recent study proposed by Westerlund (CCE in Panels with General Unknown Factors, Econometrics Journal, 21, 264-276, 2018) showed that a very popular Common Correlated Effects (CCE) estimator is significantly more applicable than it was thought before. Contrary to the usual stationarity...

The Common Correlated Effects (CCE) methodology is now well established for the analysis of factor-augmented panel models. Yet, it is often neglected that the pooled variant is biased unless the cross-section dimension (N) of the dataset dominates the time series length (T). This is problematic...

The Common Correlated Effects (CCE) estimator is a popular method to estimate panel data regression models with interactive effects. Due to its simplicity in approximating the common factors with cross-section averages of the observables, it lends itself to a wide range of applications. They...