A popular approach to factor-augmented panel regressions is the common correlated effects (CCE) estimator of Pesaran (2006). This paper points to a problem with the CCE approach that appears in the empirically relevant case when the number of factors is strictly less than the number of observables used in their estimation. Specifically, the use of too many observables causes the second moment matrix of the estimated factors to become asymptotically singular, an issue that has not yet been appropriately accounted for. The purpose of the present paper is to fill this gap in the literature.
- Factor-augmented panel regression
- CCE estimation
- Moore–Penrose inverse