Forecasting with Bayesian Panel Vector Autoregressions
Forecasting with Bayesian Panel Vector Autoregressions
Computes forecasting performance measures for recursive pseudo-out-of-...
Computes forecasting performance measures for recursive pseudo-out-of-...
Computes posterior draws of the forecast error variance decomposition
Computes posterior draws of the forecast error variance decomposition
Computes posterior draws of the forecast error variance decomposition
Bayesian estimation of a Bayesian Hierarchical Panel Vector Autoregres...
Bayesian estimation of a Bayesian Hierarchical Panel Vector Autoregres...
Bayesian estimation of a Bayesian Hierarchical Panel Vector Autoregres...
Bayesian estimation of a Bayesian Hierarchical Vector Autoregressions ...
Bayesian estimation of a Bayesian Hierarchical Panel Vector Autoregres...
Bayesian estimation of a Bayesian Hierarchical Panel Vector Autoregres...
Bayesian estimation of a Bayesian Hierarchical Panel Vector Autoregres...
Bayesian estimation of a Bayesian Hierarchical Vector Autoregressions ...
Bayesian recursive pseudo-out-of-sample forecasting
Bayesian recursive pseudo-out-of-sample forecasting
Bayesian recursive pseudo-out-of-sample forecasting
Bayesian recursive pseudo-out-of-sample forecasting
Bayesian recursive pseudo-out-of-sample forecasting
Forecasting using Hierarchical Panel Vector Autoregressions
Forecasting using Hierarchical Panel Vector Autoregressions
Forecasting using Hierarchical Panel Vector Autoregressions
Forecasting using Hierarchical Vector Autoregressions for Dynamic Pane...
Plots fitted values of dependent variables
Plots forecast error variance decompositions
Objects exported from other packages
R6 Class representing the specification of the BVARGROUPPANEL model
R6 Class representing the specification of the BVARGROUPPRIORPANEL mod...
R6 Class representing the specification of the BVARPANEL model
R6 Class representing the specification of the BVARs model
R6 Class Representing DataMatricesBVARPANEL
R6 Class Representing specification of the pseudo-out-of-sample foreca...
R6 Class Representing PosteriorBVARGROUPPANEL
R6 Class Representing PosteriorBVARGROUPPRIORPANEL
R6 Class Representing PosteriorBVARPANEL
R6 Class Representing PosteriorBVARs
R6 Class Representing PriorBVARPANEL
R6 Class Representing PriorBVARs
R6 Class Representing StartingValuesBVARGROUPPANEL
R6 Class Representing StartingValuesBVARGROUPPRIORPANEL
R6 Class Representing StartingValuesBVARPANEL
R6 Class Representing StartingValuesBVARs
Provides posterior summary of country-specific Forecasts
Provides posterior estimation summary for Bayesian Hierarchical Panel ...
Provides posterior estimation summary for Bayesian Hierarchical Panel ...
Provides posterior estimation summary for Bayesian Hierarchical Panel ...
Provides posterior estimation summary for Bayesian Vector Autoregressi...
Provides posterior summary of forecast error variance decompositions
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.