Factor-Augmented VARMA Models With Macroeconomic Applications
AbstractWe study the relationship between vector autoregressive moving-average (VARMA) and factor representations of a vector stochastic process. We observe that, in general, vector time series and factors cannot both follow finite-order VAR models. Instead, a VAR factor dynamics induces a VARMA process, while a VAR process entails VARMA factors. We propose to combine factor and VARMA modeling by using factor-augmented VARMA (FAVARMA) models. This approach is applied to forecasting key macroeconomic aggregates using large U.S. and Canadian monthly panels. The results show that FAVARMA models yield substantial improvements over standard factor models, including precise representations of the effect and transmission of monetary policy.
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Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Journal of Business & Economic Statistics.
Volume (Year): 31 (2013)
Issue (Month): 4 (October)
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Web page: http://www.tandfonline.com/UBES20
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- Helmut Lütkepohl, 2014.
"Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey,"
SFB 649 Discussion Papers
SFB649DP2014-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," Discussion Papers of DIW Berlin 1351, DIW Berlin, German Institute for Economic Research.
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