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Activity Number:
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290
- Advanced Bayesian Topics (Part 3)
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Type:
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Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #318660
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Title:
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Sparse Bayesian High-Dimensional Vector Autoregressions
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Author(s):
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Rui Meng*
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Companies:
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Lawrence Berkeley National Laboratory
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Keywords:
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Bayesian vector autoregression;
Shrinkage prior;
high dimension
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Abstract:
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Vector autoregressive (VAR) models have been widely used for modeling the temporal dependence of multivariate time series. Global-local priors are increasingly used to induce shrinkage in such models. In this article, we introduce both element-wise and lag-wise shrinkage on the VAR with stochastic volatility case via three-parameter beta prior. And we propose a new Bayesian estimation procedure for possibly high-dimensional VARs. We show that our proposed framework yields more precise estimates of model parameters and performs well in applications to out-of-sample forecasting.
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Authors who are presenting talks have a * after their name.
