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.
NKdata                  Example Dataset NKdata
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.
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
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 #
                        ##############################################################################
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
simulate_conditional_forecasts
                        Simulate paths from conditional forecast
                        distributions
