ilo_conditional_forecasts

For each of the countries a time series of 3 observations on 
the future values of gdp is provided. These future values are taken from 
IMF projections. The future values of other variables are set to <code>NA</code>.
Last data update was implemented on 2026-04-18.

datasets

Provides Bayesian estimation and forecasting of dynamic panel data using
Bayesian Panel Vector Autoregressions with hierarchical prior distributions
following the specification by Sanchez-Martinez & Woźniak (2026) <doi:10.48550/arXiv.2606.14143>.
The models include country-specific Vector Autoregressions (VARs) that share a global prior
distribution that extend the model by Jarociński (2010) <doi: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) <doi:10.32614/CRAN.package.bsvars>, 'bsvarSIGNs' by
Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>,
and 'bvars' by Liu, Ramirez Hassan, & Woźniak (2026)
<doi:10.32614/CRAN.package.bvars> 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.

Tomasz Woźniak

bpvars

Forecasting with Bayesian Panel Vector Autoregressions

Miguel Sanchez-Martinez

International Labour Organization 

ilo_conditional_forecasts function

A list of 189 <code>ts</code> objects with time series of 3 observations 
on 4 variables:<dl>
 <dt>gdp</dt>
<dd>logarithm of gross domestic product</dd> <dt>UR</dt>
<dd>annual unemployment rate</dd> <dt>EPR</dt>
<dd>annual employment rate</dd> <dt>LFPR</dt>
<dd>annual labour force participation rate</dd>
</dl>

Format

Data containing future observations for 189 countries 
from 2025 to 2027 to be used for conditional forecasts given 
the future values of gdp. — ilo_conditional_forecasts

A list of 189 <code>ts</code> objects with time series of 3 observations 
on 4 variables:<dl>
 <dt>gdp</dt>
<dd>logarithm of gross domestic product</dd>

 <dt>UR</dt>
<dd>annual unemployment rate</dd>

 <dt>EPR</dt>
<dd>annual employment rate</dd>

 <dt>LFPR</dt>
<dd>annual labour force participation rate</dd>


</dl>

ilo_conditional_forecasts: Data containing future observations for 189 countries from 2025 to 2027 to be used for conditional forecasts given the future values of gdp.

Description

Usage

Arguments

Format

Examples