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Forecasting using v...
Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage
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- Gefang, Deborah (författare)
- University of Leicester, Leicester, UK
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- Koop, Gary (författare)
- University of Strathclyde, Strathclyde, UK; Economic Statistics Centre of Excellenc
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- Poon, Aubrey, 1987- (författare)
- Örebro universitet,Handelshögskolan vid Örebro Universitet,University of Strathclyde, Strathclyde, UK; Economic Statistics Centre of Excellenc
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(creator_code:org_t)
- Elsevier, 2023
- 2023
- Engelska.
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 39:1, s. 346-363
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- SAMHÄLLSVETENSKAP -- Ekonomi och näringsliv (hsv//swe)
- SOCIAL SCIENCES -- Economics and Business (hsv//eng)
Nyckelord
- Variational inference
- Vector autoregression
- Stochastic volatility
- Hierarchical prior
- Forecasting
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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