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Inferential Theory for Generalized Dynamic Factor Models

Author

Listed:
  • Matteo Barigozzi
  • Marc Hallin
  • Matteo Luciani
  • Paolo Zaffaroni

Abstract

We provide the asymptotic distributional theory for the so-called General or Generalized Dynamic Factor Model (GDFM), laying the foundations for an inferential approach in the GDFM analysis of high-dimensional time series. Our results are exploiting the duality between common shocksand dynamic loadings under a random cross-section approach to derive the asymptotic distribution of a class of estimators for common shocks, dynamic loadings, common components, and impulse response functions. An empirical application aimed at the construction of a “core” inflation indicator for the U.S. economy is presented, empirically demonstrating the superiority of the GDFM-based indicator over the most commonly adopted approaches, outperforming, in particular, the one based on Principal Components.

Suggested Citation

  • Matteo Barigozzi & Marc Hallin & Matteo Luciani & Paolo Zaffaroni, 2021. "Inferential Theory for Generalized Dynamic Factor Models," Working Papers ECARES 2021-20, ULB -- Universite Libre de Bruxelles.
    • Handle: RePEc:eca:wpaper:2013/331192
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    References listed on IDEAS

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    More about this item

    Keywords

    High-dimensional time series; Generalized Dynamic Factor Models; One-sided representations of dynamic factor models; Asymptotic distribution; Confidence intervals;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • E0 - Macroeconomics and Monetary Economics - - General

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