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Forecasting with Factor-augmented Error Correction Models

Anindya Banerjee (), Massimiliano Marcellino and Igor Masten

No 7677, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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 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.

Keywords: Cointegration; Dynamic Factor Models; Error Correction Models; Factor-augmented Error Correction Models; FAVAR; Forecasting (search for similar items in EconPapers)
JEL-codes: C32 E17 (search for similar items in EconPapers)
Date: 2010-02
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Related works:
Journal Article: Forecasting with factor-augmented error correction models (2014) Downloads
Working Paper: Forecasting with Factor-Augmented Error Correction Models (2009) Downloads
Working Paper: Forecasting with Factor-augmented Error Correction Models (2009) Downloads
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