Forecasting with factor-augmented error correction models

Anindya Banerjee, Massimiliano Marcellino, Igor Masten

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)


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 the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its 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 FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.
Original languageEnglish
Pages (from-to)589-612
JournalInternational Journal of Forecasting
Issue number3
Early online date6 Jun 2013
Publication statusPublished - 2014


  • Forecasting
  • Dynamic factor models
  • Error correction models
  • Cointegration
  • Factor-augmented error correction models


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