Factor-augmented Error Correction Models

This paper brings together several important strands of the econometrics literature: errorcorrection,
cointegration and dynamic factor models. It introduces the Factor-augmented Error
Correction Model (FECM), where the factors estimated from a large set of variables in levels
are jointly modelled with a few key economic variables of interest. With respect to the standard
ECM, the FECM protects, at least in part, from omitted variable bias and the dependence of
cointegration analysis on the specific limited set of variables under analysis. It may also be in
some cases a refinement of the standard Dynamic Factor Model (DFM), since it allows us to
include the error correction terms into the equations, and by allowing for cointegration prevent
the errors from being non-invertible moving average processes. In addition, the FECM is a
natural generalization of factor augmented VARs (FAVAR) considered by Bernanke, Boivin and
Eliasz (2005) inter alia, which are specified in first differences and are therefore misspecified in
the presence of cointegration. The FECM has a vast range of applicability. A set of Monte Carlo
experiments and two detailed empirical examples highlight its merits in finite samples relative to
standard ECM and FAVAR models. The analysis is conducted primarily within an in-sample
framework, although the out-of-sample implications are also explored.