Provides posterior summary of heteroskedastic Structural VAR estimation
Source:R/summary.R
summary.PosteriorBSVARMSH.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 'PosteriorBSVARMSH'
summary(object, ...)Arguments
- object
an object of class PosteriorBSVARMSH obtained using the
estimate()function applied to heteroskedastic Bayesian Structural VAR model specification set by functionspecify_bsvar_msh$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_msh$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-stationaryMSH 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-stationaryMSH 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] 0.8870916 0.03528028 0.8352888 0.9401435
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -18.75312 1.056281 -20.41557 -17.02559
#> B[2,2] 34.48929 1.963238 31.31556 37.44283
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -31.734265 1.9821749 -34.881283 -29.181130
#> B[3,2] -17.571403 1.7504964 -20.532657 -15.311888
#> B[3,3] 6.365612 0.3687844 5.860931 6.857711
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.03901184 0.02218447 1.01328983 1.075651697
#> lag1_var2 -0.03904007 0.02200608 -0.06678832 -0.007636447
#> lag1_var3 -0.18076547 0.03043116 -0.23582994 -0.151711898
#> const -0.27756297 0.18578483 -0.47499354 -0.039775724
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.02059436 0.01895236 -0.005474478 0.04624105
#> lag1_var2 0.94770420 0.01664362 0.925140927 0.97450649
#> lag1_var3 -0.09296846 0.02617207 -0.138934713 -0.06145795
#> const -0.42762202 0.12984901 -0.572657427 -0.23667741
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.5963884 0.1224723 0.4778464 0.81983478
#> lag1_var2 -0.1691176 0.1283751 -0.3249726 0.03341021
#> lag1_var3 -0.6047201 0.1773131 -0.9097106 -0.40411847
#> const -1.0056562 1.0444947 -2.5912512 0.54467338
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 76.39409 44.33062 16.97432 137.5948
#> B[2,]_shrinkage 257.17279 123.77474 113.07231 487.9517
#> B[3,]_shrinkage 226.76803 92.28633 116.84255 353.7844
#> B[1,]_shrinkage_scale 906.38304 566.44932 154.48393 1777.2144
#> B[2,]_shrinkage_scale 1412.19482 977.67492 529.64550 2776.4937
#> B[3,]_shrinkage_scale 1335.76674 789.10138 341.52358 2506.0519
#> B_global_scale 120.12864 72.29638 44.55243 205.5031
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 1.2830810 1.0157458 0.4160975 2.213314
#> A[2,]_shrinkage 0.9348893 0.4440348 0.4890715 1.974114
#> A[3,]_shrinkage 1.3726551 0.6605221 0.6523746 2.465475
#> A[1,]_shrinkage_scale 13.2416233 5.5866821 7.6280199 19.457357
#> A[2,]_shrinkage_scale 11.6025068 5.4662643 6.7526322 20.190082
#> A[3,]_shrinkage_scale 13.4877808 6.7442862 7.1299776 29.559229
#> A_global_scale 1.3599467 0.5999579 0.7837480 2.842465
#>
#>
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar_msh$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-stationaryMSH 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-stationaryMSH 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] 0.8870916 0.03528028 0.8352888 0.9401435
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -18.75312 1.056281 -20.41557 -17.02559
#> B[2,2] 34.48929 1.963238 31.31556 37.44283
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -31.734265 1.9821749 -34.881283 -29.181130
#> B[3,2] -17.571403 1.7504964 -20.532657 -15.311888
#> B[3,3] 6.365612 0.3687844 5.860931 6.857711
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.03901184 0.02218447 1.01328983 1.075651697
#> lag1_var2 -0.03904007 0.02200608 -0.06678832 -0.007636447
#> lag1_var3 -0.18076547 0.03043116 -0.23582994 -0.151711898
#> const -0.27756297 0.18578483 -0.47499354 -0.039775724
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.02059436 0.01895236 -0.005474478 0.04624105
#> lag1_var2 0.94770420 0.01664362 0.925140927 0.97450649
#> lag1_var3 -0.09296846 0.02617207 -0.138934713 -0.06145795
#> const -0.42762202 0.12984901 -0.572657427 -0.23667741
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.5963884 0.1224723 0.4778464 0.81983478
#> lag1_var2 -0.1691176 0.1283751 -0.3249726 0.03341021
#> lag1_var3 -0.6047201 0.1773131 -0.9097106 -0.40411847
#> const -1.0056562 1.0444947 -2.5912512 0.54467338
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 76.39409 44.33062 16.97432 137.5948
#> B[2,]_shrinkage 257.17279 123.77474 113.07231 487.9517
#> B[3,]_shrinkage 226.76803 92.28633 116.84255 353.7844
#> B[1,]_shrinkage_scale 906.38304 566.44932 154.48393 1777.2144
#> B[2,]_shrinkage_scale 1412.19482 977.67492 529.64550 2776.4937
#> B[3,]_shrinkage_scale 1335.76674 789.10138 341.52358 2506.0519
#> B_global_scale 120.12864 72.29638 44.55243 205.5031
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 1.2830810 1.0157458 0.4160975 2.213314
#> A[2,]_shrinkage 0.9348893 0.4440348 0.4890715 1.974114
#> A[3,]_shrinkage 1.3726551 0.6605221 0.6523746 2.465475
#> A[1,]_shrinkage_scale 13.2416233 5.5866821 7.6280199 19.457357
#> A[2,]_shrinkage_scale 11.6025068 5.4662643 6.7526322 20.190082
#> A[3,]_shrinkage_scale 13.4877808 6.7442862 7.1299776 29.559229
#> A_global_scale 1.3599467 0.5999579 0.7837480 2.842465
#>
#>