Dynamic Factor Models

Catherine Doz () and Peter Fuleky
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Catherine Doz: PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École nationale des ponts et chaussées - CNRS - Centre National de la Recherche Scientifique, 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

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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.

Keywords: dynamic factor models; big data; two-step estimation; time domain; frequency domain; structural breaks (search for similar items in EconPapers)
Date: 2019-07
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02262202v1
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Dynamic Factor Models (2020)
Working Paper: Dynamic Factor Models (2020)
Working Paper: Dynamic Factor Models (2019)
Working Paper: Dynamic Factor Models (2019)
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