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.
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Titolo: | Sparse Bayesian Graphical VAR for Risk Analysis |
Autori: | |
Data di pubblicazione: | 2016 |
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. |
Handle: | http://hdl.handle.net/11571/1347845 |
ISBN: | 978-0-9839375-6-2 |
Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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