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Forecasting with Factor-augmented Error Correction Models (2009)
| Citations: | 11 - 1 self |
BibTeX
@MISC{Banerjee09forecastingwith,
author = {Anindya Banerjee and Massimiliano Marcellino and Igor Masten and Jel-codes C E},
title = {Forecasting with Factor-augmented Error Correction Models},
year = {2009}
}
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Abstract
As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2008) introduced the Factoraugmented 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 speci…cation 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.
Keyphrases
factor-augmented error correction model useful step forecasting performance analytical example standard ecm factor-augmented var favar model several conceptual advantage general mark factoraugmented error correction model latter speci cation error correction model forecasting precision large datasets long-run information di erences fecm combine error-correction dynamic factor model monte carlo simulation several empirical application
