BGVAR

Maximilian Böck; Martin Feldkircher; Florian Huber; Darjus Hosszejni

BGVAR

Maximilian Böck, Martin Feldkircher, Florian Huber, Darjus Hosszejni

Institute for Statistics and Mathematics

Publication: Non-textual form › Software

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.

Original language	English
Publication status	Published - 6 Nov 2021

Austrian Classification of Fields of Science and Technology (ÖFOS)

102022 Software development
101018 Statistics
502025 Econometrics
101026 Time series analysis

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University Library Catalogue

1 Journal article
1 Working Paper/Preprint

BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R
Böck, M., Feldkircher, M. & Huber, F., 2020.
Publication: Working/Discussion Paper › Working Paper/Preprint
Forecasting with Global Vector Autoregressive Models: A Bayesian Approach
Crespo Cuaresma, J., Feldkircher, M. & Huber, F., 2016, In: Journal of Applied Econometrics. 31, 7, p. 1371 - 1391
Publication: Scientific journal › Journal article › peer-review

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title = "BGVAR",

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 . 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.",

author = "Maximilian B{\"o}ck and Martin Feldkircher and Florian Huber and Darjus Hosszejni",

year = "2021",

month = nov,

day = "6",

language = "English",

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TY - ADVS

T1 - BGVAR

AU - Böck, Maximilian

AU - Feldkircher, Martin

AU - Huber, Florian

AU - Hosszejni, Darjus

PY - 2021/11/6

Y1 - 2021/11/6

N2 - 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 . 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.

AB - 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 . 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.

M3 - Software

ER -

BGVAR

Abstract

Austrian Classification of Fields of Science and Technology (ÖFOS)

Check availability

Other versions

BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R

Forecasting with Global Vector Autoregressive Models: A Bayesian Approach

Cite this