Sparse Graphical Vector Autoregression: A Bayesian Approach

Annals of Economics and Statistics, No. 123/124, December 2016

University Ca' Foscari of Venice, Dept. of Economics Research Paper Series No. 24/WP/2014

30 Pages Posted: 27 Mar 2015 Last revised: 10 Mar 2019

See all articles by Daniel Felix Ahelegbey

Daniel Felix Ahelegbey

University of Pavia, Department of Economics and Management; Boston University, Department of Mathematics & Statistics

Monica Billio

Ca Foscari University of Venice - Dipartimento di Economia

Roberto Casarin

University Ca' Foscari of Venice - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: December 28, 2014

Abstract

In high-dimensional vector autoregressive (VAR) models, it is natural to have large number of predictors relative to the number of observations, and a lack of efficiency in estimation and forecasting. In this context, model selection is a difficult issue and standard procedures may often be inefficient. In this paper we aim to provide a solution to these problems. We introduce sparsity on the structure of temporal dependence of a graphical VAR and develop an efficient model selection approach. We follow a Bayesian approach and introduce prior restrictions to control the maximal number of explanatory variables for VAR models. We discuss the joint inference of the temporal dependence, the maximum lag order and the parameters of the model, and provide an efficient Markov chain Monte Carlo procedure. The efficiency of the proposed approach is showed on simulated experiments and real data to model and forecast selected US macroeconomic variables with many predictors.

Keywords: High-dimensional Models, Large Vector Autoregression, Model Selection, Prior Distribution, Sparse Graphical Models

JEL Classification: C11, C15, C52, C55, E17, G17

Suggested Citation

Ahelegbey, Daniel Felix and Billio, Monica and Casarin, Roberto, Sparse Graphical Vector Autoregression: A Bayesian Approach (December 28, 2014). Annals of Economics and Statistics, No. 123/124, December 2016, University Ca' Foscari of Venice, Dept. of Economics Research Paper Series No. 24/WP/2014, Available at SSRN: https://ssrn.com/abstract=2584858 or http://dx.doi.org/10.2139/ssrn.2584858

Daniel Felix Ahelegbey

University of Pavia, Department of Economics and Management ( email )

Via San Felice 7
Pavia, Lombardia 27100
Italy
+393888931128 (Phone)

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

Boston University, Department of Mathematics & Statistics ( email )

111 Cummington Mall
Boston, MA 02215
United States

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 (Contact Author)

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://sites.google.com/view/robertocasarin

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