Package index • bpvars

bpvars: Forecasting with Bayesian Hierarchical Panel VARs

Browse package information

bpvars-package bpvars: Forecasting with Bayesian Panel Vector Autoregressions

Data

Upload sample data set

ilo_conditional_forecasts: Data containing conditional projections for the logarithm of GDP (gdp) for 189 United Nations countries from 2024 to 2029

ilo_dynamic_panel: A 4-variable annual system for forecasting labour market outcomes for 189 United Nations countries from 1991 to 2023

ilo_exogenous_forecasts: Data containing future observations for 189 United Nations countries from 2024 to 2029 to be used to forecast with models with ilo_exogenous_variables

ilo_exogenous_variables: 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 United Nations countries from 1991 to 2023

country_grouping_incomegroup: A vector with country grouping by income group for 189 countries

country_grouping_region: A vector with country grouping by region for 189 countries

country_grouping_subregionbroad: A vector with country grouping by subregion for 189 countries

country_grouping_subregiondetailed: A vector with country grouping by detailed subregion for 189 countries

Model specification

Choose a model to work with

specify_bvarPANEL: R6 Class representing the specification of the BVARPANEL model

specify_bvarGroupPANEL: R6 Class representing the specification of the BVARGROUPPANEL model

More detailed model specification

Adjust or inspect the specified model

specify_prior_bvarPANEL: R6 Class Representing PriorBVARPANEL

specify_starting_values_bvarGroupPANEL: R6 Class Representing StartingValuesBVARGROUPPANEL

specify_starting_values_bvarPANEL: R6 Class Representing StartingValuesBVARPANEL

specify_panel_data_matrices: R6 Class Representing DataMatricesBVARPANEL

Estimation

Run Bayesian estimation of your model and inspect the outputs

estimate(<BVARGROUPPANEL>): Bayesian estimation of a Bayesian Hierarchical Panel Vector Autoregression with fixed or estimated country grouping

estimate(<BVARPANEL>): Bayesian estimation of a Bayesian Hierarchical Panel Vector Autoregression using Gibbs sampler

estimate(<PosteriorBVARGROUPPANEL>): Bayesian estimation of a Bayesian Hierarchical Panel Vector Autoregression with fixed or estimated country grouping

estimate(<PosteriorBVARPANEL>): Bayesian estimation of a Bayesian Hierarchical Panel Vector Autoregression using Gibbs sampler

specify_posterior_bvarGroupPANEL: R6 Class Representing PosteriorBVARGROUPPANEL

specify_posterior_bvarPANEL: R6 Class Representing PosteriorBVARPANEL

Posterior summaries

Analyse the posterior summaries of the posterior estimation outcomes

summary(<ForecastsPANEL>): Provides posterior summary of country-specific Forecasts

summary(<PosteriorBVARPANEL>): Provides posterior estimation summary for Bayesian Hierarchical Panel Vector Autoregressions

summary(<PosteriorFEVDPANEL>): Provides posterior summary of forecast error variance decompositions

Forecasting

Predict future values of your variables

forecast(<PosteriorBVARPANEL>): Forecasting using Hierarchical Panel Vector Autoregressions

ilo_conditional_forecasts: Data containing conditional projections for the logarithm of GDP (gdp) for 189 United Nations countries from 2024 to 2029

ilo_exogenous_forecasts: Data containing future observations for 189 United Nations countries from 2024 to 2029 to be used to forecast with models with ilo_exogenous_variables

Structural analyses

Compute interpretable outcomes

compute_variance_decompositions(<PosteriorBVARPANEL>): Computes posterior draws of the forecast error variance decomposition

Plot your results

Prepare beautiful and informative plots for your analyses

plot(<ForecastsPANEL>): Plots fitted values of dependent variables

plot(<PosteriorFEVDPANEL>): Plots forecast error variance decompositions