Inferential Theory for Generalized Dynamic Factor Models

Matteo Barigozzi, Marc Hallin, Matteo Luciani and Paolo Zaffaroni

No 2021-20, Working Papers ECARES from ULB -- Universite Libre de Bruxelles

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.

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)
Pages: 44 p.
Date: 2021-08
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-isf
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Journal Article: Inferential theory for generalized dynamic factor models (2024)
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