Forecasting with factor-augmented error correction models
International journal of forecasting, 2014, Vol. 30, No. 3, pp. 589-612
BANERJEE, Anindya, MARCELLINO, Massimiliano, MASTEN, Igor, Forecasting with factor-augmented error correction models, International journal of forecasting, 2014, Vol. 30, No. 3, pp. 589-612 - https://hdl.handle.net/1814/33911
Retrieved from Cadmus, EUI Research Repository
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.
Cadmus permanent link: https://hdl.handle.net/1814/33911
Full-text via DOI: 10.1016/j.ijforecast.2013.01.009
ISSN: 0169-2070; 1872-8200
Publisher: Elsevier Science Bv
Keyword(s): Forecasting Dynamic factor models Error correction models Cointegration Factor-augmented error correction models FAVAR Government bond yields principal components monetary-policy time-series inflation number trends
Initial version: http://hdl.handle.net/1814/11765
Version: Published version of EUI RSCAS WP 2009/32
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