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Forecasting with Factor-augmented Error Correction

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Author Info

  • Anindya Banerjee
  • Massimiliano Marcellino
  • Igor Masten

Abstract

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 speci cation in dfferences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simula- tions 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.

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File URL: ftp://ftp.bham.ac.uk/pub/RePEc/pdf/09-06R.pdf
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Bibliographic Info

Paper provided by Department of Economics, University of Birmingham in its series Discussion Papers with number 09-06r.

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Length: 42 pages
Date of creation: Jan 2010
Date of revision:
Handle: RePEc:bir:birmec:09-06r

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Web page: http://www.economics.bham.ac.uk
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Keywords: Forecasting with Factor-augmented Error Correction;

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