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Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage
University of Leicester, Leicester, UK.
University of Strathclyde, Strathclyde, UK; Economic Statistics Centre of Excellenc.
Örebro University, Örebro University School of Business. University of Strathclyde, Strathclyde, UK; Economic Statistics Centre of Excellenc.ORCID iD: 0000-0003-2587-8779
2023 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 39, no 1, p. 346-363Article in journal (Refereed) Published
Abstract [en]

Many recent papers in macroeconomics have used large vector autoregressions (VARs) involving 100 or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital to achieve reasonable results. Computational concerns currently limit the range of priors used and render difficult the addition of empirically important features such as stochastic volatility to the large VAR. In this paper, we develop variational Bayesian methods for large VARs that overcome the computational hurdle and allow for Bayesian inference in large VARs with a range of hierarchical shrinkage priors and with time-varying volatilities. We demonstrate the computational feasibility and good forecast performance of our methods in an empirical application involving a large quarterly US macroeconomic data set.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 39, no 1, p. 346-363
Keywords [en]
Variational inference, Vector autoregression, Stochastic volatility, Hierarchical prior, Forecasting
National Category
Economics and Business
Identifiers
URN: urn:nbn:se:oru:diva-96330DOI: 10.1016/j.ijforecast.2021.11.012ISI: 000904903100021Scopus ID: 2-s2.0-85122512611OAI: oai:DiVA.org:oru-96330DiVA, id: diva2:1625824
Note

Funding agency:

Office of National Statistics (ONS)

Available from: 2022-01-10 Created: 2022-01-10 Last updated: 2023-01-30Bibliographically approved

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Poon, Aubrey

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
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More languages
Output format
  • html
  • text
  • asciidoc
  • rtf