Dynamic Factor Models

Catherine Doz () and Peter Fuleky
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Catherine Doz: PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: Dynamic factor models are parsimonious representations of relationships among time series variables. With the surge in data availability, they have proven to be indispensable in macroeconomic forecasting. This chapter surveys the evolution of these models from their pre-big-data origins to the large-scale models of recent years. We review the associated estimation theory, forecasting approaches, and several extensions of the basic framework.

Date: 2020-11-29
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Citations: View citations in EconPapers (6)

Published in Peter Fuleky. Macroeconomic Forecasting in the Era of Big Data, Springer, pp.27-64, 2020, ⟨10.1007/978-3-030-31150-6_2⟩

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Related works:
Working Paper: Dynamic Factor Models (2020)
Working Paper: Dynamic Factor Models (2019)
Working Paper: Dynamic Factor Models (2019)
Working Paper: Dynamic Factor Models (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:pseptp:halshs-02491811

DOI: 10.1007/978-3-030-31150-6_2

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