Quotation Kastner, Gregor, Huber, Florian. 2017. Sparse Bayesian Vector Autoregressions in Huge Dimensions.


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Abstract

We develop a Bayesian vector autoregressive (VAR) model that is capable of handling vast dimensional information sets. Three features are introduced to permit reliable estimation of the model. First, we assume that the reduced-form errors in the VAR feature a factor stochastic volatility structure, allowing for conditional equation-by-equation estimation. Second, we apply a Dirichlet-Laplace prior to the VAR coefficients to cure the curse of dimensionality. Finally, since simulation-based methods are needed to simulate from the joint posterior distribution, we utilize recent innovations to efficiently sample from high-dimensional multivariate Gaussian distributions that improve upon recent algorithms by large margins. In the empirical exercise we apply the model to US data and evaluate its forecasting capabilities.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Working/discussion paper, preprint
Language English
Title Sparse Bayesian Vector Autoregressions in Huge Dimensions
Year 2017
URL https://arxiv.org/abs/1704.03239
JEL C11, C30, C53, E27

Associations

People
Kastner, Gregor (Details)
Huber, Florian (Former researcher)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1105 Computer software (Details)
1162 Statistics (Details)
5323 Econometrics (Details)
5701 Applied statistics (Details)
5707 Time series analysis (Details)
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