Sparse Graphical Vector Autoregression: A Bayesian Approach
27 Pages Posted: 23 Dec 2014 Last revised: 10 Sep 2016
Date Written: May 15, 2016
This paper considers a sparsity approach for inference in large vector autoregressive (VAR) models. The approach is based on a Bayesian procedure and a graphical representation of VAR models. We discuss a Markov chain Monte Carlo algorithm for sparse graph selection, parameter estimation, and equation-specific lag selection. We show the efficiency of our algorithm on simulated data and illustrate the effectiveness of our approach in forecasting macroeconomic time series and in measuring contagion risk among financial institutions.
Keywords: Large VAR, Model Selection, Prior Distribution, Sparse Graphical Models
JEL Classification: C11, C15, C52, C55, E17, G01, G17
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