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 measuring contagion risk among financial institutions.

Sparse Graphical Multivariate Autoregression: A Bayesian approach

BILLIO, Monica;CASARIN, Roberto
2016-01-01

Abstract

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 measuring contagion risk among financial institutions.
2016
JSM Proceedings, Statistical Computing Section. Alexandria, VA: American Statistical Association, 2016
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3691746
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