ilo_exogenous_variables_missing

For each of the countries a time series of observations on 3 
dummy variables for the years 2008, 2020, and 2021 is provided. 
Last data update was implemented on 2025-09-26.

datasets

Provides Bayesian estimation and forecasting of dynamic panel data using
Bayesian Panel Vector Autoregressions with hierarchical prior distributions.
The models include country-specific 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. An 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> and 'bsvarSIGNs' by
Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they
constitute an integrated toolset. Copyright: 2025 International Labour
Organization.

Tomasz Woźniak

bpvars

Forecasting with Bayesian Panel Vector Autoregressions

Miguel Sanchez-Martinez

International Labour Organization 

ilo_exogenous_variables_missing function

A list of 189 <code>ts</code> objects with time series on 3 variables:<dl>
 <dt>2008</dt>
<dd>the aftermath of the Global Financial Crisis</dd> <dt>2020</dt>
<dd>the COVID pandemic</dd> <dt>2021</dt>
<dd>the aftermath of the COVID pandemic</dd>
</dl>

Format

A 3-variable annual system for of dummy observations for 2008, 2020, 
and 2021 to be used in the estimation of the Panel VAR model for 
189 countries to 2024 containing observations for matching periods from 
ilo_dynamic_panel_missing — ilo_exogenous_variables_missing

A list of 189 <code>ts</code> objects with time series on 3 variables:<dl>
 <dt>2008</dt>
<dd>the aftermath of the Global Financial Crisis</dd>

 <dt>2020</dt>
<dd>the COVID pandemic</dd>

 <dt>2021</dt>
<dd>the aftermath of the COVID pandemic</dd>


</dl>

A 3-variable annual system for of dummy observations for 2008, 2020, 
and 2021 to be used in the estimation of the Panel VAR model for 
189 countries to 2024 containing observations for matching periods from 
<code>ilo_dynamic_panel_missing</code>

ilo_exogenous_variables_missing: A 3-variable annual system for of dummy observations for 2008, 2020, and 2021 to be used in the estimation of the Panel VAR model for 189 countries to 2024 containing observations for matching periods from `ilo_dynamic_panel_missing`

Description

Usage

Arguments

Format

Examples