Forecasting large datasets with reduced rank multivariate models

Arbeitspapier

The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance with the most promising existing alternatives, namely, factor models, large scale bayesian VARs, and multivariate boosting. Specifically, we focus on classical reduced rank regression, a two-step procedure that applies, in turn, shrinkage and reduced rank restrictions, and the reduced rank bayesian VAR of Geweke (1996). As a result, we found that using shrinkage and rank reduction in combination rather than separately improves substantially the accuracy of forecasts, both when the whole set of variables is to be forecast, and for key variables such as industrial production growth, inflation, and the federal funds rate.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Working Paper ; No. 617

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Bayesian Analysis: General ; Estimation: General ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models ; Forecasting Models; Simulation Methods
• Thema: Bayesian VARs ; Factor models ; Forecasting ; Reduced rank ; Multivariate Analyse ; Prognoseverfahren ; Ranking-Verfahren ; Bayes-Statistik

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Carriero, Andrea ; Kapetanios, George ; Marcellino, Massimiliano
• Ereignis: Veröffentlichung
• (wer): Queen Mary University of London, Department of Economics
• (wo): London
• (wann): 2007

Weitere Informationen

• Handle: http://hdl.handle.net/10419/62799
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ