High-Dimensional Posterior Consistency in Bayesian Vector Autoregressive Models

DOI10.1080/01621459.2018.1437043zbMath1420.62379OpenAlexW2787360447WikidataQ93046441 ScholiaQ93046441MaRDI QIDQ5231502

Satyajit Ghosh, George Michailidis, Kshitij Khare

Publication date: 27 August 2019

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6716151

zbMATH Keywords

posterior consistencyshrinkage priorBayesian lassovector autoregressive models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15)

Related Items

Strong selection consistency of Bayesian vector autoregressive models based on a pseudo-likelihood approach, Finite sample theory for high-dimensional functional/scalar time series with applications, Matrix Autoregressive Spatio-Temporal Models, High-dimensional dynamic systems identification with additional constraints, The EAS approach for graphical selection consistency in vector autoregression models, Bayesian sparse seemingly unrelated regressions model with variable selection and covariance estimation via the horseshoe+, Convergence analysis of a collapsed Gibbs sampler for Bayesian vector autoregressions, A state-space approach to time-varying reduced-rank regression, On consistency and sparsity for high-dimensional functional time series with application to autoregressions

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