bsvars 3.0

The package is under intensive development, and more functionality will be provided soon! To see the package ROADMAP towards the next version.

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  1. The package has a logo! And it’s beautiful! #37
  2. The package includes summary methods #1
  3. The package includes plot methods #36
  4. Method forecast allow for conditional forecasting given provided future trajectories of selected variables #76
  5. Sparse mixture and Markov-switching models can now have more than 20 regimes #57
  6. A new, more detailed, package description #62
  7. The website features the new logo. And includes some new information #38
  8. Updates on documentation to accommodate the fact that some generics and functions from package bsvars will be used in a broader family of packages, first of which is bsvarSIGNs. Includes updates on references. #63
  9. Fixed compute_fitted_values(). Now it’s correctly sampling from the predictive data density. #67
  10. Fixed some bugs that did not create problems #55
  11. Got rid of filling by reference in the samplers for the sake of granting the exported cpp functions usability #56
  12. Coded compute_*() functions as generics and methods #70
  13. Updated code for forecast error variance decompositions for heteroskedastic models (qas prompted by @adamwang15) #69

bsvars 2.1.0

Published on 11 December 2023

  1. Included Bayesian procedure for verifying structural shocks’ heteroskedastiicty equation-by-equation using Savage-Dickey density ratios #26
  2. Included Bayesian procedure for verifying joint hypotheses on autoregressive parameters using Savage-Dickey density ratios #26
  3. Included the possibility of specifying exogenous variables or deterministic terms and included the deterministic terms used by Lütkepohl, Shang, Uzeda, Woźniak (2023) #45
  4. Updated the data as in Lütkepohl, Shang, Uzeda, Woźniak (2023) #45
  5. Fixing the compilation problems reported HERE #48
  6. The package has its pkgdown website at #38

bsvars 2.0.0

Published on 23 October 2023

  1. Included Imports from package stochvol
  2. Posterior computations for:
  1. Implemented faster samplers based on random number generators from armadillo via RcppArmadillo #7
  2. The estimate_bsvar* functions now also normalise the output w.r.t. to a structural matrix with positive elements on the main diagonal #9
  3. Changed the order of arguments in the estimate_bsvar* functions with posterior first to facilitate workflows using the pipe |> #10
  4. Include citation info for the package #12
  5. Corrected sampler for AR parameter of the SV equations #19
  6. Added samplers from joint predictive densities #15
  7. A new centred Stochastic Volatility heteroskedastic process is implemented #22
  8. Introduced a three-level local-global equation-specific prior shrinkage hierarchy for the parameters of matrices and #34
  9. Improved checks for correct specification of arguments S and thin of the estimate method as enquired by @mfaragd #33
  10. Improved the ordinal numerals presentation for thinning in the progress bar #27

bsvars 1.0.0

Published on 1 September 2022

  1. repo transferred from GitLab to GitHub
  2. repository is made public
  3. version to be premiered on CRAN


  1. Added a new progress bar for the estimate_bsvar* functions
  2. Developed R6 classes for model specification and posterior outcomes; model specification includes sub-classes for priors, identifying restrictions, data matrices, and starting values
  3. Added a complete package documentation
  4. Written help files
  5. Developed tests for MCMC reproducibility
  6. Included sample data


  1. cpp scripts are imported, compile, and give no Errors, Warnings, or Notes
  2. R wrappers for the functions are fully operating
  3. full documentation describing package and functions’ functionality [sic!]