Graphical modelling of multivariate panel data models

  1. Celia Gil-Bermejo 1
  2. Jorge Onrubia 2
  3. A. Jesús Sánchez-Fuentes 3
  1. 1 Investigadora asociada al Instituto Complutense de Estudios Internacionales (ICEI-UCM)
  2. 2 Investigador adscrito al Instituto Complutense de Estudios Internacionales (ICEIUCM), FEDEA, EQUALITAS & GEN
  3. 3 Investigador adscrito al Instituto Complutense de Estudios Internacionales (ICEI-UCM) & GEN
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Revista:
Documentos de trabajo = Working Papers ( Instituto Complutense de Estudios Internacionales ): Nueva época

ISSN: 2339-9570

Año de publicación: 2022

Número: 2

Tipo: Documento de Trabajo

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Otras publicaciones en: Documentos de trabajo = Working Papers ( Instituto Complutense de Estudios Internacionales ): Nueva época

Resumen

In this paper, we propose a new approach to both test Granger Causality in a multivariate panel data environment and determine one ultimate “causality path” excluding those relationships which are redundant. For the sake of concreteness, we combine recent developments introduced to estimate Granger causality procedure based on Meta-analysis in heterogeneous mixed panels (Emirmahmutoglu and Kose, 2011 and Dumitrescu and Hurlin, 2012) and graphical models proposed in a growing literature (Spirtes et al, 2000, Demiralp and Hoover, 2003, Eicher, 2007 and 2012) searching iteratively for the existing dependencies between a multivariate set of information. Finally, we illustrate our proposal by revisiting existing studies in the context of panel Vector Autoregressive (VAR) models to the analysis of the fiscal policy-growth nexus .

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