Bayesian Graphical Models for Structural Vector Autoregressive Processes

41 Pages Posted: 11 Jan 2013 Last revised: 30 Sep 2014

Daniel Felix Ahelegbey

Boston University - Department of Mathematics and Statistics

Monica Billio

Ca Foscari University of Venice - Dipartimento di Economia

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics

Date Written: December 1, 2012

Abstract

This paper proposes a Bayesian, graph-based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous and temporal causal structures of the structural VAR model are represented by two different graphs. We also provide an efficient Markov chain Monte Carlo algorithm to estimate jointly the two causal structures and the parameters of the reduced-form VAR model. The BGVAR approach is shown to be quite effective in dealing with model identification and selection in multivariate time series of moderate dimension, as those considered in the economic literature. In the macroeconomic application the BGVAR identifies the relevant structural relationships among 20 US economic variables, thus providing a useful tool for policy analysis. The financial application contributes to the recent econometric literature on financial interconnectedness. The BGVAR approach provides evidence of a strong unidirectional linkage from financial to non-financial super-sectors during the 2007-2009 financial crisis and a strong bidirectional linkage between the two sectors during the 2010-2013 European sovereign debt crisis.

Keywords: Bayesian Graphical Models, Granger Causality, Markov Chain Monte Carlo, Structural VAR, Vector Autoregression

JEL Classification: C11, C15, C53, G17

Suggested Citation

Ahelegbey, Daniel Felix and Billio, Monica and Casarin, Roberto, Bayesian Graphical Models for Structural Vector Autoregressive Processes (December 1, 2012). University Ca' Foscari of Venice, Dept. of Economics Research Paper Series No. 36/WP/2012. Available at SSRN: https://ssrn.com/abstract=2198844 or http://dx.doi.org/10.2139/ssrn.2198844

Daniel Felix Ahelegbey (Contact Author)

Boston University - Department of Mathematics and Statistics ( email )

111 Cummington Mall
Boston, MA 02215
United States

HOME PAGE: http://sites.google.com/site/danielfelixahey/home

Monica Billio

Ca Foscari University of Venice - Dipartimento di Economia ( email )

Cannaregio 873
Venice, 30121
Italy

HOME PAGE: http://www.unive.it/persone/billio

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics ( email )

San Giobbe 873/b
Venice, 30121
Italy
+39 030.298.91.49 (Phone)
+39 030.298.88.37 (Fax)

HOME PAGE: http://venus.unive.it/r.casarin/

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