Quotation Böck, Maximilian, Feldkircher, Martin, Huber, Florian, Hosszejni, Darjus. 2021. BGVAR.


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Abstract

Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Software
Title BGVAR
Date Nov. 6, 2021
Version 2.4.3
Licence GPL-3
Operating system Linux, Windows, MacOS
Language English
Programming language R, C++

Associations

People
Böck, Maximilian (Former researcher)
Feldkircher, Martin (Former researcher)
Hosszejni, Darjus (Details)
External
Huber, Florian (University of Salzburg, Austria)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT 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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