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Inferential theory for generalized dynamic factor models

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  • Barigozzi, Matteo
  • Hallin, Marc
  • Luciani, Matteo
  • Zaffaroni, Paolo

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. By exploiting the duality between common shocks and dynamic loadings, we derive the asymptotic distribution and associated standard errors for a class of estimators for common shocks, dynamic loadings, common components, and impulse response functions. We present an empirical application aimed at constructing a “core” inflation indicator for the U.S. economy, which demonstrates the superiority of the GDFM-based indicator over the most common approaches, particularly the one based on Principal Components.

Suggested Citation

  • Barigozzi, Matteo & Hallin, Marc & Luciani, Matteo & Zaffaroni, Paolo, 2024. "Inferential theory for generalized dynamic factor models," Journal of Econometrics, Elsevier, vol. 239(2).
    • Handle: RePEc:eee:econom:v:239:y:2024:i:2:s0304407623000593
    DOI: 10.1016/j.jeconom.2023.02.003
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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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