# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "bpvars" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'bpvars: Forecasting with Bayesian Panel Vector Autoregressions'
version: 1.0.0.9000
doi: 10.32614/CRAN.package.bpvars
identifiers:
- type: doi
  value: 10.32614/CRAN.package.bpvars
abstract: 'Provides Bayesian estimation and forecasting of dynamic panel data using
  Bayesian Panel Vector Autoregressions with hierarchical prior distributions. The
  models include country-specific Vector Autoregressions (VARs) that share a global
  prior distribution that extend the model by Jarociński (2010) <https://doi.org/10.1002/jae.1082>.
  Under this prior expected value, each country''s system follows a global VAR with
  country-invariant parameters. Further flexibility is provided by the hierarchical
  prior structure that retains the Minnesota prior interpretation for the global VAR
  and features estimated prior covariance matrices, shrinkage, and persistence levels.
  Bayesian forecasting is developed for models including exogenous variables, allowing
  conditional forecasts given the future trajectories of some variables and restricted
  forecasts assuring that rates are forecasted to stay positive and less than 100.
  The package implements the model specification, estimation, and forecasting routines,
  facilitating coherent workflows and reproducibility. It also includes automated
  pseudo-out-of-sample forecasting and computation of forecasting performance measures.
  Beautiful plots, informative summary functions, and extensive documentation complement
  all this. Extraordinary computational speed is achieved thanks to employing frontier
  econometric and numerical techniques and algorithms written in ''C++''. The ''bpvars''
  package is aligned regarding objects, workflows, and code structure with the ''R''
  packages ''bsvars'' by Woźniak (2024) <https://doi.org/10.32614/CRAN.package.bsvars>
  and ''bsvarSIGNs'' by Wang & Woźniak (2025) <https://doi.org/10.32614/CRAN.package.bsvarSIGNs>,
  and they constitute an integrated toolset. Copyright: 2025 International Labour
  Organization. The International Labour Organization should not be held responsible
  for any issues arising from the use of the ''bpvars'' package or from the results
  obtained with it.'
authors:
- family-names: Woźniak
  given-names: Tomasz
  email: wozniak.tom@pm.me
  orcid: https://orcid.org/0000-0003-2212-2378
preferred-citation:
  type: manual
  title: 'bpvars: Forecasting with Bayesian Panel Vector Autoregressions'
  authors:
  - family-names: Wo\'zniak
    given-names: Tomasz
    email: wozniak.tom@pm.me
  year: '2026'
  notes: R package version 2.0
  url: https://CRAN.R-project.org/package=bpvars
  doi: 10.32614/CRAN.package.bpvars
repository: https://bsvars.r-universe.dev
repository-code: https://github.com/bsvars/bpvars
commit: 63e580711624b950d2257ccd1ebc8aa2c1dc6f3e
url: https://bsvars.org/bpvars/
date-released: '2025-12-12'
contact:
- family-names: Woźniak
  given-names: Tomasz
  email: wozniak.tom@pm.me
  orcid: https://orcid.org/0000-0003-2212-2378
references:
- type: article
  title: Forecasting with Bayesian Panel Vector Autoregressions Using the R Package
    bpvars
  authors:
  - family-names: Sanchez-Martinez
    given-names: Miguel
    email: sanchezmartinez@ilo.org
  - family-names: Wo\'zniak
    given-names: Tomasz
    email: tomasz.wozniak@unimelb.edu.au
  year: '2026'
  journal: University of Melbourne Working Paper
  start: '1'
  end: '39'