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Graphical modelling of multivariate panel data models

Author

Listed:
  • Celia Gil-Bermejo Lazo

    (Instituto Complutense de Estudios Internacionales (ICEI), Universidad Complutense de Madrid.)

  • Jorge Onrubia Fernández

    (Instituto Complutense de Estudios Internacionales (ICEI), Universidad Complutense de Madrid.)

  • Antonio Jesús Sánchez Fuentes

    (Instituto Complutense de Estudios Internacionales (ICEI), Universidad Complutense de Madrid.)

Abstract

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.

Suggested Citation

  • Celia Gil-Bermejo Lazo & Jorge Onrubia Fernández & Antonio Jesús Sánchez Fuentes, 2022. "Graphical modelling of multivariate panel data models," Working Papers del Instituto Complutense de Estudios Internacionales 2202, Universidad Complutense de Madrid, Instituto Complutense de Estudios Internacionales.
    • Handle: RePEc:ucm:wpaper:2202
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    Keywords

    Granger causality; Panel data; Causal maps;
    All these keywords.

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