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] 0.1457447 0.009314145 0.1293442 0.1579257
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -8.526607 0.7646093 -9.54723 -7.44683
#> B[2,2] 27.593535 2.4748858 24.09579 30.86426
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -24.371301 1.927371 -26.69286 -21.93406
#> B[3,2] -7.144051 1.125330 -8.81883 -5.68333
#> B[3,3] 40.138549 3.181920 36.56111 43.58322
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.0688039 0.020651971 1.04008892 1.10076201
#> lag1_var2 -0.0289662 0.007017126 -0.03805791 -0.02054552
#> lag1_var3 -0.9631348 0.027250477 -1.00779944 -0.92577840
#> const -0.1699146 0.068046314 -0.26210118 -0.08297315
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.00259267 0.010770886 -0.01111269 0.02101303
#> lag1_var2 0.95201365 0.007731751 0.94134191 0.96163491
#> lag1_var3 -0.26718560 0.013782516 -0.28720807 -0.25391881
#> const -0.38892076 0.056124667 -0.46014828 -0.31626036
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.06632431 0.014533592 0.04484724 0.085360340
#> lag1_var2 -0.01537847 0.004268758 -0.02217867 -0.009766971
#> lag1_var3 0.33609339 0.018788824 0.31218481 0.364500572
#> const -0.07519547 0.051930331 -0.16318020 0.025019092
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 214.1591 315.46169 30.75986 509.8715
#> B[2,]_shrinkage 210.1952 151.70044 80.91627 525.3034
#> B[3,]_shrinkage 360.1923 150.22035 207.42146 636.5635
#> B[1,]_shrinkage_scale 1439.1323 1216.66180 353.80046 2923.7151
#> B[2,]_shrinkage_scale 1372.5247 749.63231 427.12363 2508.2925
#> B[3,]_shrinkage_scale 1727.6229 1049.75707 600.78956 3407.1613
#> B_global_scale 133.6762 84.63039 41.09738 269.5907
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.4579148 0.4319430 0.1432773 0.8497848
#> A[2,]_shrinkage 0.4358701 0.2043693 0.2061438 0.8446141
#> A[3,]_shrinkage 0.4611586 0.2957567 0.1636154 1.0013833
#> A[1,]_shrinkage_scale 4.0808902 1.3225609 2.1484565 5.9644275
#> A[2,]_shrinkage_scale 5.1880456 2.3993311 2.2539966 8.8684154
#> A[3,]_shrinkage_scale 4.8676175 2.3082269 1.9203296 9.5986153
#> A_global_scale 0.5545031 0.1630473 0.3707168 0.7558593
#>
#>
# 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] 0.1457447 0.009314145 0.1293442 0.1579257
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -8.526607 0.7646093 -9.54723 -7.44683
#> B[2,2] 27.593535 2.4748858 24.09579 30.86426
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -24.371301 1.927371 -26.69286 -21.93406
#> B[3,2] -7.144051 1.125330 -8.81883 -5.68333
#> B[3,3] 40.138549 3.181920 36.56111 43.58322
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.0688039 0.020651971 1.04008892 1.10076201
#> lag1_var2 -0.0289662 0.007017126 -0.03805791 -0.02054552
#> lag1_var3 -0.9631348 0.027250477 -1.00779944 -0.92577840
#> const -0.1699146 0.068046314 -0.26210118 -0.08297315
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.00259267 0.010770886 -0.01111269 0.02101303
#> lag1_var2 0.95201365 0.007731751 0.94134191 0.96163491
#> lag1_var3 -0.26718560 0.013782516 -0.28720807 -0.25391881
#> const -0.38892076 0.056124667 -0.46014828 -0.31626036
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.06632431 0.014533592 0.04484724 0.085360340
#> lag1_var2 -0.01537847 0.004268758 -0.02217867 -0.009766971
#> lag1_var3 0.33609339 0.018788824 0.31218481 0.364500572
#> const -0.07519547 0.051930331 -0.16318020 0.025019092
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 214.1591 315.46169 30.75986 509.8715
#> B[2,]_shrinkage 210.1952 151.70044 80.91627 525.3034
#> B[3,]_shrinkage 360.1923 150.22035 207.42146 636.5635
#> B[1,]_shrinkage_scale 1439.1323 1216.66180 353.80046 2923.7151
#> B[2,]_shrinkage_scale 1372.5247 749.63231 427.12363 2508.2925
#> B[3,]_shrinkage_scale 1727.6229 1049.75707 600.78956 3407.1613
#> B_global_scale 133.6762 84.63039 41.09738 269.5907
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.4579148 0.4319430 0.1432773 0.8497848
#> A[2,]_shrinkage 0.4358701 0.2043693 0.2061438 0.8446141
#> A[3,]_shrinkage 0.4611586 0.2957567 0.1636154 1.0013833
#> A[1,]_shrinkage_scale 4.0808902 1.3225609 2.1484565 5.9644275
#> A[2,]_shrinkage_scale 5.1880456 2.3993311 2.2539966 8.8684154
#> A[3,]_shrinkage_scale 4.8676175 2.3082269 1.9203296 9.5986153
#> A_global_scale 0.5545031 0.1630473 0.3707168 0.7558593
#>
#>