bsvars 2.0.0
Published on 23 October 2023
- Included Imports from package stochvol
- Posterior computations for:
- impulse responses and forecast error variance decomposition #3,
- structural shocks and historical decompositions #14
- fitted values #17
- conditional standard deviations #16
- regime probabilities for MS and MIX models #18
- Implemented faster samplers based on random number generators from
armadillo via RcppArmadillo #7
- The
estimate_bsvar* functions now also normalise the
output w.r.t. to a structural matrix with positive elements on the main
diagonal #9
- Changed the order of arguments in the
estimate_bsvar*
functions with posterior first to facilitate workflows
using the pipe |> #10
- Include citation info for the package #12
- Corrected sampler for AR parameter of the SV equations #19
- Added samplers from joint predictive densities #15
- A new centred Stochastic Volatility heteroskedastic process is
implemented #22
- Introduced a three-level local-global equation-specific prior
shrinkage hierarchy for the parameters of matrices and #34
- Improved checks for correct specification of arguments
S and thin of the estimate method
as enquired by @mfaragd #33
- Improved the ordinal numerals presentation for thinning in the
progress bar #27
bsvars 1.0.0
Published on 1 September 2022
- repo transferred from GitLab to GitHub
- repository is made public
- version to be premiered on CRAN
bsvars 0.0.2.9000
- Added a new progress bar for the
estimate_bsvar*
functions
- Developed R6 classes for model specification and
posterior outcomes; model specification includes sub-classes for priors,
identifying restrictions, data matrices, and starting values
- Added a complete package documentation
- Written help files
- Developed tests for MCMC reproducibility
- Included sample data
bsvars 0.0.1.9000
- cpp scripts are imported, compile, and give no
Errors, Warnings, or Notes
- R wrappers for the functions are fully
operating
- full documentation describing package and functions’ functionality
[sic!]