Forecasting with factor-augmented error correction models
Title: Forecasting with factor-augmented error correction models
Publisher: Elsevier Science Bv
Citation: International journal of forecasting, 2014, Vol. 30, No. 3, pp. 589-612
ISSN: 0169-2070; 1872-8200
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.
Subject: 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
This article is based on EUI RSCAS WP 2009/32
Initial version: http://hdl.handle.net/1814/11765
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