Provides posterior summary of non-normal Structural VAR estimation
Source:R/summary.R
summary.PosteriorBSVARMIX.RdProvides posterior mean, standard deviations, as well as 5 and 95 percentiles of the parameters: the structural matrix \(B\), autoregressive parameters \(A\), and hyper parameters.
Usage
# S3 method for class 'PosteriorBSVARMIX'
summary(object, ...)Arguments
- object
an object of class PosteriorBSVARMIX obtained using the
estimate()function applied to non-normal Bayesian Structural VAR model specification set by functionspecify_bsvar_mix$new()containing draws from the posterior distribution of the parameters.- ...
additional arguments affecting the summary produced.
Value
A list reporting the posterior mean, standard deviations, as well as 5 and 95 percentiles of the parameters: the structural matrix \(B\), autoregressive parameters \(A\), and hyper-parameters.
Author
Tomasz Woźniak wozniak.tom@pm.me
Examples
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
set.seed(123)
specification = specify_bsvar_mix$new(us_fiscal_lsuw)
#> The identification is set to the default option of lower-triangular structural matrix.
# run the burn-in
burn_in = estimate(specification, 10)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-finiteMIX model |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# estimate the model
posterior = estimate(burn_in, 20)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-finiteMIX model |
#> **************************************************|
#> Progress of the MCMC simulation for 20 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
summary(posterior)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of the parameters |
#> **************************************************|
#> $B
#> $B$equation1
#> mean sd 5% quantile 95% quantile
#> B[1,1] 0.1449162 0.007267542 0.1332178 0.1570438
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -8.811002 0.667645 -9.86811 -7.760265
#> B[2,2] 28.537240 2.153693 25.09124 32.001655
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -24.690383 1.8342985 -26.610586 -21.458429
#> B[3,2] -7.327384 0.9657082 -9.080312 -5.999524
#> B[3,3] 40.788738 2.9525925 35.306957 44.475450
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.24723723 0.02141285 1.2095415 1.27167157
#> lag1_var2 -0.08246796 0.01946051 -0.1183488 -0.06319558
#> lag1_var3 -1.13138785 0.02980704 -1.1605588 -1.08368406
#> const -0.26567963 0.17096640 -0.6224231 -0.12523768
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.06527148 0.01336794 0.04742533 0.08510973
#> lag1_var2 0.94069406 0.01154040 0.91688973 0.95407024
#> lag1_var3 -0.33045704 0.01847202 -0.36086491 -0.31071541
#> const -0.38216543 0.10583374 -0.62261447 -0.28211094
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.18450003 0.01186940 0.16856667 0.2015697401
#> lag1_var2 -0.04881106 0.01253461 -0.07107985 -0.0348609222
#> lag1_var3 0.22501189 0.01695013 0.20250675 0.2469733430
#> const -0.12510036 0.11223426 -0.33737876 0.0006134041
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 306.7015 351.2104 50.54474 839.8371
#> B[2,]_shrinkage 410.9587 346.0439 101.99288 997.1704
#> B[3,]_shrinkage 449.0158 238.1776 206.51944 755.1564
#> B[1,]_shrinkage_scale 2423.2192 2126.3985 596.89629 6679.3929
#> B[2,]_shrinkage_scale 2878.5042 2747.6859 606.11469 8372.1182
#> B[3,]_shrinkage_scale 2561.5384 1824.8129 959.14927 6818.2078
#> B_global_scale 242.7689 201.3279 68.82274 661.8064
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.9376393 0.5131084 0.3834680 1.756917
#> A[2,]_shrinkage 0.5714414 0.2891398 0.2821778 1.147262
#> A[3,]_shrinkage 0.8381269 0.5843654 0.4141772 1.779891
#> A[1,]_shrinkage_scale 9.3859226 3.9571496 4.6932003 16.244926
#> A[2,]_shrinkage_scale 6.7373966 1.5709598 4.3621147 9.249969
#> A[3,]_shrinkage_scale 9.6816221 3.6609981 6.1683380 17.267315
#> A_global_scale 1.0246142 0.3259178 0.6207570 1.517074
#>
#>
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar_mix$new() |>
estimate(S = 10) |>
estimate(S = 20) |>
summary()
#> The identification is set to the default option of lower-triangular structural matrix.
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-finiteMIX model |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-finiteMIX model |
#> **************************************************|
#> Progress of the MCMC simulation for 20 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of the parameters |
#> **************************************************|
#> $B
#> $B$equation1
#> mean sd 5% quantile 95% quantile
#> B[1,1] 0.1449162 0.007267542 0.1332178 0.1570438
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -8.811002 0.667645 -9.86811 -7.760265
#> B[2,2] 28.537240 2.153693 25.09124 32.001655
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -24.690383 1.8342985 -26.610586 -21.458429
#> B[3,2] -7.327384 0.9657082 -9.080312 -5.999524
#> B[3,3] 40.788738 2.9525925 35.306957 44.475450
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.24723723 0.02141285 1.2095415 1.27167157
#> lag1_var2 -0.08246796 0.01946051 -0.1183488 -0.06319558
#> lag1_var3 -1.13138785 0.02980704 -1.1605588 -1.08368406
#> const -0.26567963 0.17096640 -0.6224231 -0.12523768
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.06527148 0.01336794 0.04742533 0.08510973
#> lag1_var2 0.94069406 0.01154040 0.91688973 0.95407024
#> lag1_var3 -0.33045704 0.01847202 -0.36086491 -0.31071541
#> const -0.38216543 0.10583374 -0.62261447 -0.28211094
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.18450003 0.01186940 0.16856667 0.2015697401
#> lag1_var2 -0.04881106 0.01253461 -0.07107985 -0.0348609222
#> lag1_var3 0.22501189 0.01695013 0.20250675 0.2469733430
#> const -0.12510036 0.11223426 -0.33737876 0.0006134041
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 306.7015 351.2104 50.54474 839.8371
#> B[2,]_shrinkage 410.9587 346.0439 101.99288 997.1704
#> B[3,]_shrinkage 449.0158 238.1776 206.51944 755.1564
#> B[1,]_shrinkage_scale 2423.2192 2126.3985 596.89629 6679.3929
#> B[2,]_shrinkage_scale 2878.5042 2747.6859 606.11469 8372.1182
#> B[3,]_shrinkage_scale 2561.5384 1824.8129 959.14927 6818.2078
#> B_global_scale 242.7689 201.3279 68.82274 661.8064
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.9376393 0.5131084 0.3834680 1.756917
#> A[2,]_shrinkage 0.5714414 0.2891398 0.2821778 1.147262
#> A[3,]_shrinkage 0.8381269 0.5843654 0.4141772 1.779891
#> A[1,]_shrinkage_scale 9.3859226 3.9571496 4.6932003 16.244926
#> A[2,]_shrinkage_scale 6.7373966 1.5709598 4.3621147 9.249969
#> A[3,]_shrinkage_scale 9.6816221 3.6609981 6.1683380 17.267315
#> A_global_scale 1.0246142 0.3259178 0.6207570 1.517074
#>
#>