R: Structural Scenario Analysis for Bayesian Structural Vector Autoregression Models

# Structural Scenario Analysis for Bayesian Structural Vector Autoregression Models

## Documentation for package ‘APRScenario’ version 0.0.3.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

big_b_and_M | big_b_and_M This function returns the extended b and M matrices as in APR
forc_h | forc_h function
full_scenarios_core | Exported version of full_scenarios_core
gen_mats | gen_mats function
KL | KL function APR suggest this measure to assess the "plausibility" of the conditional forecast. It is based on the Kullback-Leibler measure of distance between the unconditional forecast and the conditional/scenario forecast.
mat_forc | mat_forc function ############################################################################## NB: HERE WE USE Antolin-Diaz et al notation # B is reduced form; # A is structural; # d is intercepts # M is reduced so that E(u_u')=Sigma=(A_0_A_0')^(-1) and M_0=A_0^(-1)*Q # Note that the code returns conflicting notation: # B=>A_0^(-1)*Q and # A=>B # ##############################################################################
NKdata | Example Dataset NKdata
plot_bvars | plot_bvars: This function plots the IRFs generated with the BVAR
plot_cond_forc | plot_cond_forc function; Data should conatain the variable "variable", the "hor" horizon and a "history"
plot_cond_histo | plot_cond_histo function
scenarios | scenarios function (fully optimized with Rcpp) This function computes the mean and covariances to draw from the conditional forecast The actual draw is done in the simscen function
SimScen | simscen function This function takes the mean and covariance of the conditional forecast to draw from the conditional forecast distribution The shock uncertainty is included in the simulation by default, but can be turned off.
simulate_conditional_forecasts | Simulate paths from conditional forecast distributions