
Authors and Citation
Authors
-
Fei Shang. Contributor.
co-authored the Journal of Econometrics methodological paper by Lütkepohl, Shang, Uzeda, Woźniak (2026), introduced major code corrections and improvements -
Helmut Lütkepohl. Contributor.
co-authored the Journal of Econometrics methodological paper by Lütkepohl, Shang, Uzeda, Woźniak (2026) -
Luis Uzeda. Contributor.
co-authored the Journal of Econometrics methodological paper by Lütkepohl, Shang, Uzeda, Woźniak (2026) -
Xiaolei Wang. Contributor.
corrected C++ code for historical decompositions -
Bruno Cavalcante. Contributor.
corrected R code for HMSH forecasting
Citation
Source: inst/CITATION
Woźniak, Tomasz (2025). bsvars: Bayesian Estimation of Structural Vector Autoregressive Models. R package version 4.0, URL: https://cran.r-project.org/package=bsvars, DOI: 10.32614/CRAN.package.bsvars.
@Manual{,
title = {bsvars: Bayesian Estimation of Structural Vector Autoregressive Models},
author = {Tomasz Wo\'zniak},
year = {2025},
note = {R package version 4.0},
url = {https://CRAN.R-project.org/package=bsvars},
doi = {10.32614/CRAN.package.bsvars},
}
Woźniak, Tomasz (2025). Fast and Efficient Bayesian Analysis of Structural Vector Autoregressions Using the R Package bsvars. University of Melbourne Working Paper, DOI: 10.48550/arXiv.2410.15090.
@Article{,
title = {Fast and Efficient Bayesian Analysis of Structural Vector Autoregressions Using the R Package bsvars},
author = {Tomasz Wo\'zniak},
year = {2025},
journal = {University of Melbourne Working Paper},
pages = {1--25},
doi = {10.48550/arXiv.2410.15090},
}
Lütkepohl, H., Shang, F., Uzeda, L., Woźniak, T. (2026). Partial Identification of Structural Vector Autoregressions with Non-centred Stochastic Volatility. Journal of Econometrics, Vol. 256, pp. 106107. DOI: 10.1016/j.jeconom.2025.106107.
@Article{,
title = {Partial Identification of Structural Vector Autoregressions with Non-centred Stochastic Volatility},
author = {Helmut Lütkepohl and Fei Shang and Luis Uzeda and Tomasz Wo\'zniak},
journal = {Journal of Econometrics},
year = {2026},
volume = {256},
pages = {106107},
doi = {10.1016/j.jeconom.2025.106107},
}