Inferential theory for generalized dynamic factor models

Matteo Barigozzi, Marc Hallin, Matteo Luciani and Paolo Zaffaroni

Journal of Econometrics, 2024, vol. 239, issue 2

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.

Keywords: High-dimensional time series; Generalized dynamic factor models; One-sided representations of dynamic factor models; Asymptotic distribution; Confidence intervals (search for similar items in EconPapers)
JEL-codes: C0 C01 E0 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407623000593
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Inferential Theory for Generalized Dynamic Factor Models (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:239:y:2024:i:2:s0304407623000593

DOI: 10.1016/j.jeconom.2023.02.003

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().