Inferential theory for generalized dynamic factor models

Matteo Barigozzi

doi:10.1016/j.jeconom.2023.02.003

Inferential theory for generalized dynamic factor models

2024-02-01

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AI

DOI：10.1016/j.jeconom.2023.02.003

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Abstract

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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 coreinflation 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. (c) 2023 Elsevier B.V. All rights reserved.

Keywords:

High-dimensional time series

Generalized dynamic factor models

One-sided representations of dynamic

factor models

Asymptotic distribution

Confidence intervals

Inferential theory for generalized dynamic factor models

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