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] 0.8894811 0.0447447 0.824223 0.9684456
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -18.54120 0.8592834 -19.79780 -17.21552
#> B[2,2] 33.77741 1.5588390 31.28291 36.04172
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -31.792893 2.2661429 -35.58117 -28.769614
#> B[3,2] -18.045404 2.0774032 -20.31942 -15.269654
#> B[3,3] 6.361845 0.3706728 6.04125 6.952776
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.003436044 0.01641994 0.98141592 1.02693651
#> lag1_var2 -0.004868417 0.01811926 -0.03829297 0.02280775
#> lag1_var3 -0.151360723 0.01999838 -0.17517301 -0.12516992
#> const -0.056247266 0.15733371 -0.34701296 0.20599041
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.006530092 0.01581767 -0.02439232 0.01989140
#> lag1_var2 0.961906724 0.01571793 0.93986735 0.98531024
#> lag1_var3 -0.068661651 0.02066835 -0.10018555 -0.04295973
#> const -0.355858780 0.12238095 -0.51068166 -0.19416307
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.2871848 0.1033761 0.08748286 0.41714645
#> lag1_var2 0.0271416 0.1332837 -0.16895281 0.19014622
#> lag1_var3 -0.3249173 0.1456577 -0.54056738 -0.03487728
#> const 0.1832235 1.0921882 -1.17072628 1.64415812
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 91.96295 54.15372 41.07132 166.0348
#> B[2,]_shrinkage 223.71449 99.92321 127.91237 442.4787
#> B[3,]_shrinkage 254.46937 103.81410 140.33180 415.7662
#> B[1,]_shrinkage_scale 847.06741 304.78655 389.10800 1321.5747
#> B[2,]_shrinkage_scale 1133.43622 439.25000 532.57450 1646.5706
#> B[3,]_shrinkage_scale 1167.56634 361.22079 773.32394 1642.2000
#> B_global_scale 100.17572 33.29230 58.53005 143.4988
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.7728640 0.2923582 0.4726655 1.365764
#> A[2,]_shrinkage 0.9401181 0.7173775 0.3652034 2.308926
#> A[3,]_shrinkage 0.9910831 0.4105958 0.5479981 1.646646
#> A[1,]_shrinkage_scale 8.8133545 2.3847344 5.7530171 10.981245
#> A[2,]_shrinkage_scale 8.5778060 3.2181695 5.5765056 14.589027
#> A[3,]_shrinkage_scale 9.5380368 2.3680308 6.0444277 13.409178
#> A_global_scale 0.9559161 0.1709353 0.7526526 1.280365
#>
#>
# 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] 0.8894811 0.0447447 0.824223 0.9684456
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -18.54120 0.8592834 -19.79780 -17.21552
#> B[2,2] 33.77741 1.5588390 31.28291 36.04172
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -31.792893 2.2661429 -35.58117 -28.769614
#> B[3,2] -18.045404 2.0774032 -20.31942 -15.269654
#> B[3,3] 6.361845 0.3706728 6.04125 6.952776
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.003436044 0.01641994 0.98141592 1.02693651
#> lag1_var2 -0.004868417 0.01811926 -0.03829297 0.02280775
#> lag1_var3 -0.151360723 0.01999838 -0.17517301 -0.12516992
#> const -0.056247266 0.15733371 -0.34701296 0.20599041
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.006530092 0.01581767 -0.02439232 0.01989140
#> lag1_var2 0.961906724 0.01571793 0.93986735 0.98531024
#> lag1_var3 -0.068661651 0.02066835 -0.10018555 -0.04295973
#> const -0.355858780 0.12238095 -0.51068166 -0.19416307
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.2871848 0.1033761 0.08748286 0.41714645
#> lag1_var2 0.0271416 0.1332837 -0.16895281 0.19014622
#> lag1_var3 -0.3249173 0.1456577 -0.54056738 -0.03487728
#> const 0.1832235 1.0921882 -1.17072628 1.64415812
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 91.96295 54.15372 41.07132 166.0348
#> B[2,]_shrinkage 223.71449 99.92321 127.91237 442.4787
#> B[3,]_shrinkage 254.46937 103.81410 140.33180 415.7662
#> B[1,]_shrinkage_scale 847.06741 304.78655 389.10800 1321.5747
#> B[2,]_shrinkage_scale 1133.43622 439.25000 532.57450 1646.5706
#> B[3,]_shrinkage_scale 1167.56634 361.22079 773.32394 1642.2000
#> B_global_scale 100.17572 33.29230 58.53005 143.4988
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.7728640 0.2923582 0.4726655 1.365764
#> A[2,]_shrinkage 0.9401181 0.7173775 0.3652034 2.308926
#> A[3,]_shrinkage 0.9910831 0.4105958 0.5479981 1.646646
#> A[1,]_shrinkage_scale 8.8133545 2.3847344 5.7530171 10.981245
#> A[2,]_shrinkage_scale 8.5778060 3.2181695 5.5765056 14.589027
#> A[3,]_shrinkage_scale 9.5380368 2.3680308 6.0444277 13.409178
#> A_global_scale 0.9559161 0.1709353 0.7526526 1.280365
#>
#>