Forecasting with factor-augmented error correction models |
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Authors: | Anindya Banerjee Massimiliano Marcellino Igor Masten |
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Affiliation: | 1. Banque de France, France;2. Department of Economics, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom;3. European University Institute, Via della Piazzuola 43, 50133 Florence, Italy;4. Bocconi University, Italy;5. CEPR, United Kingdom;6. University of Ljubljana, Faculty of Economics, Kardeljeva pl. 17, 1000 Ljubljana, Slovenia |
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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 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. |
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Keywords: | Forecasting Dynamic factor models Error correction models Cointegration Factor-augmented error correction models FAVAR |
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