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Forecasting with Factor-augmented Error Correction Models

Listed author(s):
  • Igor Masten
  • Massimiliano Marcellino
  • Anindya Banerjeey

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 di¤erences. 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 o¤ers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.

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File URL: http://hdl.handle.net/1814/11765
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Paper provided by European University Institute in its series RSCAS Working Papers with number 2009/32.

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Date of creation: 25 Jun 2009
Handle: RePEc:rsc:rsceui:2009/32
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