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Dynamic Factor Models

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  • Catherine Doz

    () (PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - 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 des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - ENPC - École des Ponts ParisTech - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique - EHESS - École des hautes études en sciences sociales - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

  • Peter Fuleky

    (University of Hawaii)

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.

Suggested Citation

  • Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
  • Handle: RePEc:hal:psewpa:halshs-02262202
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-02262202
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    Cited by:

    1. Cimadomo, Jacopo & Giannone, Domenico & Lenza, Michele & Sokol, Andrej & Monti, Francesca, 2020. "Nowcasting with large Bayesian vector autoregressions," Working Paper Series 2453, European Central Bank.
    2. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.

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    Keywords

    dynamic factor models; big data; two-step estimation; time domain; frequency domain; structural breaks;

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