DP7677 Forecasting with Factor-augmented Error Correction Models

Author(s): Anindya Banerjee, Massimiliano Marcellino, Igor Masten
Publication Date: February 2010
Keyword(s): Cointegration, Dynamic Factor Models, Error Correction Models, Factor-augmented Error Correction Models, FAVAR, Forecasting
JEL(s): C32, E17
Programme Areas: International Macroeconomics
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=7677

As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter's specification in differences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that relative to the FAVAR, FECM generally offers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.