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.
Barigozzi, M., Hallin, M., Luciani, M., Zaffaroni, P. (2023). Inferential theory for generalized dynamic factor models. JOURNAL OF ECONOMETRICS, on line first, 1-41 [10.1016/j.jeconom.2023.02.003].
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.File | Dimensione | Formato | |
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