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 com- mon 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.

Inferential theory for generalized dynamic factor models

Barigozzi, Matteo;
2023

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 com- mon 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.
2023
Barigozzi, Matteo; Hallin, Marc; Luciani, Matteo; Zaffaroni, Paolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/920911
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