Package: APRScenario 0.0.3.1

APRScenario: Structural Scenario Analysis for Bayesian Structural Vector Autoregression Models

Implements the scenario analysis proposed by Antolin-Diaz, Petrella and Rubio-Ramirez (2021) "Structural scenario analysis with SVARs" <doi:10.1016/j.jmoneco.2020.06.001>.

Authors:Giovanni Lombardo [aut, cre]

# Install 'APRScenario' in R:

install.packages('APRScenario', repos = c('https://giannilmbd.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/giannilmbd/aprscenario/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

Datasets:

NKdata - Example Dataset NKdata

On CRAN:

openblas cpp

4.78 score 4 stars 10 scripts 536 downloads 12 exports 41 dependencies

Last updated from:35187cc764. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	164
linux-devel-x86_64	OK	179
source / vignettes	OK	274
linux-release-arm64	OK	166
linux-release-x86_64	OK	178
macos-release-arm64	OK	203
macos-release-x86_64	OK	436
macos-oldrel-arm64	OK	208
macos-oldrel-x86_64	OK	376
windows-devel	OK	148
windows-release	OK	140
windows-oldrel	OK	160
wasm-release	OK	142

Exports:big_b_and_M forc_h full_scenarios_core gen_mats KL mat_forc plot_bvars plot_cond_forc plot_cond_histo scenarios SimScen simulate_conditional_forecasts

Dependencies:abind cli cpp11 dplyr farver generics ggplot2 glue GPArotation gtable isoband labeling lattice lifecycle lubridate magrittr MASS mnormt nlme pillar pkgconfig psych purrr R6 RColorBrewer Rcpp RcppArmadillo RcppProgress rlang S7 scales stringi stringr tibble tidyr tidyselect timechange utf8 vctrs viridisLite withr

APRScenario vignette

Scenario analysis | Create Forecast Functions | Conditional Forecast | Packages | Getting all the necessary matrices

Rendered fromAPRScenario.Rmdusingknitr::rmarkdown

Last update: 2025-12-23
Started: 2025-07-10

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
big_b_and_M This function returns the extended b and M matrices as in APR	big_b_and_M
forc_h function	forc_h
Exported version of full_scenarios_core	full_scenarios_core
gen_mats function	gen_mats
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.	KL
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 # ##############################################################################	mat_forc
Example Dataset NKdata	NKdata
plot_bvars: This function plots the IRFs generated with the BVAR	plot_bvars
plot_cond_forc function; Data should conatain the variable "variable", the "hor" horizon and a "history"	plot_cond_forc
plot_cond_histo function	plot_cond_histo
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	scenarios
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.	SimScen
Simulate paths from conditional forecast distributions	simulate_conditional_forecasts