Provides posterior summary of heteroskedastic Structural VAR estimation
Source:R/summary.R
summary.PosteriorBSVARSV.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 'PosteriorBSVARSV'
summary(object, ...)Arguments
- object
an object of class PosteriorBSVARSV obtained using the
estimate()function applied to heteroskedastic Bayesian Structural VAR model specification set by functionspecify_bsvar_sv$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_sv$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-SV model |
#> Non-centred SV model is estimated |
#> **************************************************|
#> 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-SV model |
#> Non-centred SV model is estimated |
#> **************************************************|
#> 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] 0.8968551 0.03709074 0.8283078 0.9328999
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -18.46946 0.8220482 -19.16881 -17.42530
#> B[2,2] 34.92845 1.3188127 33.13628 36.49547
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -34.821946 1.9026809 -37.35330 -32.30190
#> B[3,2] -22.246437 2.5840664 -25.70848 -18.58940
#> B[3,3] 7.219321 0.3103326 6.77616 7.64351
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.87849611 0.02891186 0.82739880 0.91007584
#> lag1_var2 -0.03617474 0.01761461 -0.06249171 -0.01192026
#> lag1_var3 0.05735366 0.03579801 0.01619320 0.12359260
#> const 0.03371037 0.15888776 -0.24270495 0.24494208
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.04710790 0.02073636 -0.08511392 -0.01509528
#> lag1_var2 0.93860893 0.01242777 0.92217757 0.95508547
#> lag1_var3 0.01635927 0.02715958 -0.02548643 0.06640819
#> const -0.34717939 0.11460452 -0.52234377 -0.20901670
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.3910296 0.16658864 -0.5967895 -0.1809398
#> lag1_var2 -0.2398368 0.09095333 -0.3763948 -0.1112160
#> lag1_var3 0.8940474 0.20909227 0.6090205 1.1478181
#> const 0.2510930 0.76238032 -0.9746165 1.3888192
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 105.8239 58.08665 34.72296 206.8484
#> B[2,]_shrinkage 303.2644 171.11669 156.62845 515.9037
#> B[3,]_shrinkage 304.2805 146.90411 173.49668 550.8772
#> B[1,]_shrinkage_scale 1120.3314 467.34851 477.66102 1713.2537
#> B[2,]_shrinkage_scale 1553.7656 531.91145 718.95160 2323.0100
#> B[3,]_shrinkage_scale 1444.4069 567.38831 707.24566 2228.4188
#> B_global_scale 124.7393 46.92956 56.77050 203.7057
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.3789412 0.2533293 0.1135425 0.8820791
#> A[2,]_shrinkage 0.3876942 0.2626464 0.1328798 0.8877898
#> A[3,]_shrinkage 0.6746070 0.3906257 0.3181639 1.4349097
#> A[1,]_shrinkage_scale 4.1076558 1.8934071 2.2052983 7.2891826
#> A[2,]_shrinkage_scale 4.7717278 2.3422141 2.5689294 9.3989777
#> A[3,]_shrinkage_scale 6.0204774 1.7744902 4.2236732 10.0204334
#> A_global_scale 0.5772347 0.1585564 0.3654327 0.8286607
#>
#>
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar_sv$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-SV model |
#> Non-centred SV model is estimated |
#> **************************************************|
#> 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-SV model |
#> Non-centred SV model is estimated |
#> **************************************************|
#> 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] 0.8968551 0.03709074 0.8283078 0.9328999
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -18.46946 0.8220482 -19.16881 -17.42530
#> B[2,2] 34.92845 1.3188127 33.13628 36.49547
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -34.821946 1.9026809 -37.35330 -32.30190
#> B[3,2] -22.246437 2.5840664 -25.70848 -18.58940
#> B[3,3] 7.219321 0.3103326 6.77616 7.64351
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.87849611 0.02891186 0.82739880 0.91007584
#> lag1_var2 -0.03617474 0.01761461 -0.06249171 -0.01192026
#> lag1_var3 0.05735366 0.03579801 0.01619320 0.12359260
#> const 0.03371037 0.15888776 -0.24270495 0.24494208
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.04710790 0.02073636 -0.08511392 -0.01509528
#> lag1_var2 0.93860893 0.01242777 0.92217757 0.95508547
#> lag1_var3 0.01635927 0.02715958 -0.02548643 0.06640819
#> const -0.34717939 0.11460452 -0.52234377 -0.20901670
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.3910296 0.16658864 -0.5967895 -0.1809398
#> lag1_var2 -0.2398368 0.09095333 -0.3763948 -0.1112160
#> lag1_var3 0.8940474 0.20909227 0.6090205 1.1478181
#> const 0.2510930 0.76238032 -0.9746165 1.3888192
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 105.8239 58.08665 34.72296 206.8484
#> B[2,]_shrinkage 303.2644 171.11669 156.62845 515.9037
#> B[3,]_shrinkage 304.2805 146.90411 173.49668 550.8772
#> B[1,]_shrinkage_scale 1120.3314 467.34851 477.66102 1713.2537
#> B[2,]_shrinkage_scale 1553.7656 531.91145 718.95160 2323.0100
#> B[3,]_shrinkage_scale 1444.4069 567.38831 707.24566 2228.4188
#> B_global_scale 124.7393 46.92956 56.77050 203.7057
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.3789412 0.2533293 0.1135425 0.8820791
#> A[2,]_shrinkage 0.3876942 0.2626464 0.1328798 0.8877898
#> A[3,]_shrinkage 0.6746070 0.3906257 0.3181639 1.4349097
#> A[1,]_shrinkage_scale 4.1076558 1.8934071 2.2052983 7.2891826
#> A[2,]_shrinkage_scale 4.7717278 2.3422141 2.5689294 9.3989777
#> A[3,]_shrinkage_scale 6.0204774 1.7744902 4.2236732 10.0204334
#> A_global_scale 0.5772347 0.1585564 0.3654327 0.8286607
#>
#>