Forecasting with Factor-augmented Error Correction Models

Igor Masten, Massimiliano Marcellino and Anindya Banerjeey

No 2009/32, RSCAS Working Papers from European University Institute

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 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.

Keywords: Forecasting; Dynamic Factor Models; Error Correction Models; Cointegration; Factor-augmented Error Correction Models; FAVAR (search for similar items in EconPapers)
Date: 2009-06-25
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Citations: View citations in EconPapers (16)

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http://hdl.handle.net/1814/11765 Full text (application/pdf)

Related works:
Journal Article: Forecasting with factor-augmented error correction models (2014)
Working Paper: Forecasting with Factor-augmented Error Correction Models (2010)
Working Paper: Forecasting with Factor-Augmented Error Correction Models (2009)
Working Paper: Factor-augmented Error Correction Models (2008)
Working Paper: Factor-augmented Error Correction Models (2008)
Working Paper: Factor-augmented Error Correction Models (2008)
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