summary.PosteriorFEVDPANEL

Provides posterior means of the forecast error variance 
decompositions of each variable at all horizons.

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 

summary.PosteriorFEVDPANEL function

<dl><dt>object</dt>
<dd>an object of class <code>PosteriorFEVDPANEL</code> obtained using the
<code>compute_variance_decompositions()</code> function containing draws from the 
posterior distribution of the forecast error variance decompositions.</dd>
<dt>which_c</dt>
<dd>a positive integer or a character string specifying the country 
for which the forecast should be plotted.</dd>
<dt>...</dt>
<dd>additional arguments affecting the summary produced.</dd></dl>

Arguments

Tomasz Woźniak <a href="/link/wozniak.tom%40pm.me?package=bpvars&version=1.0" data-mini-rdoc="bpvars::wozniak.tom@pm.me">wozniak.tom@pm.me</a>

Author

Provides posterior summary of forecast error variance decompositions — summary.PosteriorFEVDPANEL

<dl>

<dt>object</dt>
<dd>an object of class <code>PosteriorFEVDPANEL</code> obtained using the
<code>compute_variance_decompositions()</code> function containing draws from the 
posterior distribution of the forecast error variance decompositions.</dd>


<dt>which_c</dt>
<dd>a positive integer or a character string specifying the country 
for which the forecast should be plotted.</dd>


<dt>...</dt>
<dd>additional arguments affecting the summary produced.</dd>

</dl>

Tomasz Woźniak <a href='mailto:wozniak.tom@pm.me'>wozniak.tom@pm.me</a>

summary.PosteriorFEVDPANEL: Provides posterior summary of forecast error variance decompositions

Description

Usage

Value

Arguments

Author

See Also

Examples