IDEAS home Printed from https://ideas.repec.org/p/eca/wpaper/2013-331192.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://dipot.ulb.ac.be/dspace/bitstream/2013/331192/3/2021-20-BARIGOZZI_HALLIN_LUCIANI_ZAFFARONI-inferential.pdf
    File Function: Œuvre complète ou partie de l'œuvre
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eca:wpaper:2013/331192. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/arulbbe.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Benoit Pauwels (email available below). General contact details of provider: https://edirc.repec.org/data/arulbbe.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.