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] 0.9005433 0.04574206 0.8203742 0.9624406
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -18.24211 1.143443 -19.94457 -16.44809
#> B[2,2] 33.80342 2.114931 30.53473 36.82297
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -31.491697 2.662864 -35.009798 -27.944403
#> B[3,2] -16.279204 2.129902 -19.323706 -13.187279
#> B[3,3] 6.180156 0.399678 5.546039 6.719512
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.95669529 0.05287017 0.87357150 1.014508486
#> lag1_var2 -0.02020603 0.01758662 -0.04275015 0.007203617
#> lag1_var3 -0.08754558 0.07268735 -0.16416752 0.028856275
#> const -0.16177188 0.12971447 -0.34538052 0.002610843
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.02432531 0.02315977 -0.04911686 0.009815769
#> lag1_var2 0.95328875 0.01135388 0.93684190 0.969055663
#> lag1_var3 -0.04196492 0.03219588 -0.08835816 -0.009183178
#> const -0.41505636 0.10263745 -0.59575575 -0.300690131
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.02339197 0.32430766 -0.5049411 0.45510725
#> lag1_var2 -0.06357084 0.06049177 -0.1426047 0.02768859
#> lag1_var3 0.04382255 0.43710530 -0.5068822 0.77822603
#> const -0.42557601 0.60560350 -1.3871623 0.23395753
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 138.4687 72.25939 58.72117 288.3024
#> B[2,]_shrinkage 263.5317 130.50338 134.83879 435.1622
#> B[3,]_shrinkage 249.0862 131.97925 115.86525 553.9159
#> B[1,]_shrinkage_scale 1391.2338 550.75569 499.93634 2169.3098
#> B[2,]_shrinkage_scale 1621.1281 665.42683 648.90515 2565.6762
#> B[3,]_shrinkage_scale 1698.4291 835.92325 726.36554 3371.3704
#> B_global_scale 156.8093 60.58783 64.44263 247.4316
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.6857877 0.4887629 0.2772815 1.590642
#> A[2,]_shrinkage 0.7544812 0.4553286 0.2623970 1.493287
#> A[3,]_shrinkage 0.8514122 0.3946918 0.4014989 1.430982
#> A[1,]_shrinkage_scale 7.8097681 2.5587499 4.6698253 12.682484
#> A[2,]_shrinkage_scale 8.7308486 2.8321626 4.9677743 13.188784
#> A[3,]_shrinkage_scale 9.7150392 2.5907258 5.6873462 13.301033
#> A_global_scale 0.8991397 0.1600903 0.6502228 1.134657
#>
#>
# 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] 0.9005433 0.04574206 0.8203742 0.9624406
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -18.24211 1.143443 -19.94457 -16.44809
#> B[2,2] 33.80342 2.114931 30.53473 36.82297
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -31.491697 2.662864 -35.009798 -27.944403
#> B[3,2] -16.279204 2.129902 -19.323706 -13.187279
#> B[3,3] 6.180156 0.399678 5.546039 6.719512
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.95669529 0.05287017 0.87357150 1.014508486
#> lag1_var2 -0.02020603 0.01758662 -0.04275015 0.007203617
#> lag1_var3 -0.08754558 0.07268735 -0.16416752 0.028856275
#> const -0.16177188 0.12971447 -0.34538052 0.002610843
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.02432531 0.02315977 -0.04911686 0.009815769
#> lag1_var2 0.95328875 0.01135388 0.93684190 0.969055663
#> lag1_var3 -0.04196492 0.03219588 -0.08835816 -0.009183178
#> const -0.41505636 0.10263745 -0.59575575 -0.300690131
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.02339197 0.32430766 -0.5049411 0.45510725
#> lag1_var2 -0.06357084 0.06049177 -0.1426047 0.02768859
#> lag1_var3 0.04382255 0.43710530 -0.5068822 0.77822603
#> const -0.42557601 0.60560350 -1.3871623 0.23395753
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 138.4687 72.25939 58.72117 288.3024
#> B[2,]_shrinkage 263.5317 130.50338 134.83879 435.1622
#> B[3,]_shrinkage 249.0862 131.97925 115.86525 553.9159
#> B[1,]_shrinkage_scale 1391.2338 550.75569 499.93634 2169.3098
#> B[2,]_shrinkage_scale 1621.1281 665.42683 648.90515 2565.6762
#> B[3,]_shrinkage_scale 1698.4291 835.92325 726.36554 3371.3704
#> B_global_scale 156.8093 60.58783 64.44263 247.4316
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.6857877 0.4887629 0.2772815 1.590642
#> A[2,]_shrinkage 0.7544812 0.4553286 0.2623970 1.493287
#> A[3,]_shrinkage 0.8514122 0.3946918 0.4014989 1.430982
#> A[1,]_shrinkage_scale 7.8097681 2.5587499 4.6698253 12.682484
#> A[2,]_shrinkage_scale 8.7308486 2.8321626 4.9677743 13.188784
#> A[3,]_shrinkage_scale 9.7150392 2.5907258 5.6873462 13.301033
#> A_global_scale 0.8991397 0.1600903 0.6502228 1.134657
#>
#>