Provides posterior summary of the structural shocks including their mean, standard deviations, as well as 5 and 95 percentiles.
Usage
# S3 method for class 'PosteriorShocks'
summary(object, ...)Arguments
- object
an object of class PosteriorShocks obtained using the
compute_structural_shocks()function containing draws the posterior distribution of the structural shocks.- ...
additional arguments affecting the summary produced.
Value
A list reporting the posterior mean, standard deviations, as well as 5 and 95 percentiles of the structural shocks for each of the equations and periods.
Author
Tomasz Woźniak wozniak.tom@pm.me
Examples
specification = specify_bsvar$new(us_fiscal_lsuw)
#> The identification is set to the default option of lower-triangular structural matrix.
burn_in = estimate(specification, 5)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
posterior = estimate(burn_in, 5)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# compute structural shocks
shocks = compute_structural_shocks(posterior)
shocks_summary = summary(shocks)
head(shocks_summary$shock1)
#> mean sd 5% quantile 95% quantile
#> 1 -0.4217146 0.1150629 -0.5751845 -0.3245687
#> 2 -0.4978940 0.1131395 -0.6489869 -0.3974478
#> 3 -0.4174761 0.1146373 -0.5710140 -0.3165630
#> 4 -0.3786269 0.1125896 -0.5298784 -0.2810264
#> 5 -0.2561990 0.1109281 -0.4062320 -0.1645447
#> 6 -0.2698613 0.1084453 -0.4166679 -0.1804146
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar$new() |>
estimate(S = 5) |>
estimate(S = 5) |>
compute_structural_shocks() |>
summary() -> shocks_summary
#> The identification is set to the default option of lower-triangular structural matrix.
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
head(shocks_summary$shock1)
#> mean sd 5% quantile 95% quantile
#> 1 -2.3394479 0.9047605 -3.265555 -1.3608028
#> 2 -1.3358308 0.9112341 -2.316532 -0.3906967
#> 3 -0.3336049 0.7120380 -1.145945 0.3616316
#> 4 -0.4283641 0.7971758 -1.325051 0.3577650
#> 5 -0.6147006 0.9141129 -1.623623 0.2963682
#> 6 -0.9106707 1.1216110 -2.123154 0.2233347