In this paper, we consider a cointegrated panel data model with non-stationary common factors, which, because of its appeal in many economic applications, has received much attention in the recent literature. By deriving a Granger-type representation theorem, we obtain several equivalent model...

Practitioners are generally well aware of the fact that most standard approaches for estimation and inference in panel data regressions are based on assuming that the cross-sectional units are independent of each other, an assumption that is surely mistaken in applications, especially in...

In an influential paper, Pesaran [Pesaran, M.H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967–1012] proposes a very simple estimator of factor-augmented regressions that has since then become very popular. In this note...

In spite of the increased use of factor-augmented regressions in recent years, little is known regarding the relative merits of the two main approaches to estimation and inference, namely, the cross-sectional average and principal component estimators. By providing a formal comparison of the...

This paper considers a cointegrated panel data model with common factors. Starting from the triangular representation of the model as used by Bai et al. (2008) a Granger type representation theorem is derived. The conditional error correction representation is obtained, which is used as a basis...

This paper illustrates analytically the effects of cross-unit cointegration using as an example the conventional pooled least squares estimate in the spurious panel regression case. The results suggest that the usual result of asymptotic normality depends critically on the absence of cross-unit...