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] 0.8791454 0.04225267 0.824303 0.9544864
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -14.39417 1.119199 -15.85810 -12.58573
#> B[2,2] 26.42899 2.061810 23.00067 29.19221
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -24.183502 1.8238207 -26.353771 -22.058522
#> B[3,2] -9.970481 1.3481796 -11.983543 -8.187286
#> B[3,3] 4.556295 0.3546924 4.177978 4.990375
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.03603469 0.01448456 1.00601197 1.0556971
#> lag1_var2 -0.05707191 0.01826205 -0.08228354 -0.0311250
#> lag1_var3 -0.17393160 0.02009343 -0.20131446 -0.1367982
#> const -0.43397781 0.17217771 -0.73802882 -0.2372379
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.03207832 0.01808707 0.008755884 0.05982562
#> lag1_var2 0.95290281 0.01595871 0.933451696 0.97870847
#> lag1_var3 -0.10751847 0.02502719 -0.143815282 -0.07720829
#> const -0.37645870 0.13124787 -0.490449740 -0.15234097
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.6353398 0.09517387 0.5157742 0.80612925
#> lag1_var2 -0.1732000 0.12313029 -0.3292692 0.02525451
#> lag1_var3 -0.6515248 0.15081138 -0.8976978 -0.46613940
#> const -1.0189938 1.05173606 -2.6164628 0.64712957
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 55.60269 29.49570 15.92597 95.54725
#> B[2,]_shrinkage 165.04201 76.52940 75.11856 307.83172
#> B[3,]_shrinkage 136.86404 56.32265 71.88319 215.63172
#> B[1,]_shrinkage_scale 653.51806 365.74631 140.80401 1195.54194
#> B[2,]_shrinkage_scale 990.70480 604.57540 429.65017 1848.93749
#> B[3,]_shrinkage_scale 917.09656 480.35115 281.26672 1608.32753
#> B_global_scale 85.94420 43.94304 36.76815 135.77622
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 1.3051823 1.0179544 0.4317802 2.233092
#> A[2,]_shrinkage 0.9378915 0.4435990 0.4960773 1.975226
#> A[3,]_shrinkage 1.4029751 0.6679666 0.6771159 2.551294
#> A[1,]_shrinkage_scale 13.3870314 5.5459891 7.8551699 19.500016
#> A[2,]_shrinkage_scale 11.6692031 5.4402654 6.8138145 20.212811
#> A[3,]_shrinkage_scale 13.6528828 6.6910411 7.2230926 29.552251
#> A_global_scale 1.3705265 0.5958991 0.7990972 2.844731
#>
#>
# 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] 0.8791454 0.04225267 0.824303 0.9544864
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -14.39417 1.119199 -15.85810 -12.58573
#> B[2,2] 26.42899 2.061810 23.00067 29.19221
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -24.183502 1.8238207 -26.353771 -22.058522
#> B[3,2] -9.970481 1.3481796 -11.983543 -8.187286
#> B[3,3] 4.556295 0.3546924 4.177978 4.990375
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.03603469 0.01448456 1.00601197 1.0556971
#> lag1_var2 -0.05707191 0.01826205 -0.08228354 -0.0311250
#> lag1_var3 -0.17393160 0.02009343 -0.20131446 -0.1367982
#> const -0.43397781 0.17217771 -0.73802882 -0.2372379
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.03207832 0.01808707 0.008755884 0.05982562
#> lag1_var2 0.95290281 0.01595871 0.933451696 0.97870847
#> lag1_var3 -0.10751847 0.02502719 -0.143815282 -0.07720829
#> const -0.37645870 0.13124787 -0.490449740 -0.15234097
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.6353398 0.09517387 0.5157742 0.80612925
#> lag1_var2 -0.1732000 0.12313029 -0.3292692 0.02525451
#> lag1_var3 -0.6515248 0.15081138 -0.8976978 -0.46613940
#> const -1.0189938 1.05173606 -2.6164628 0.64712957
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 55.60269 29.49570 15.92597 95.54725
#> B[2,]_shrinkage 165.04201 76.52940 75.11856 307.83172
#> B[3,]_shrinkage 136.86404 56.32265 71.88319 215.63172
#> B[1,]_shrinkage_scale 653.51806 365.74631 140.80401 1195.54194
#> B[2,]_shrinkage_scale 990.70480 604.57540 429.65017 1848.93749
#> B[3,]_shrinkage_scale 917.09656 480.35115 281.26672 1608.32753
#> B_global_scale 85.94420 43.94304 36.76815 135.77622
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 1.3051823 1.0179544 0.4317802 2.233092
#> A[2,]_shrinkage 0.9378915 0.4435990 0.4960773 1.975226
#> A[3,]_shrinkage 1.4029751 0.6679666 0.6771159 2.551294
#> A[1,]_shrinkage_scale 13.3870314 5.5459891 7.8551699 19.500016
#> A[2,]_shrinkage_scale 11.6692031 5.4402654 6.8138145 20.212811
#> A[3,]_shrinkage_scale 13.6528828 6.6910411 7.2230926 29.552251
#> A_global_scale 1.3705265 0.5958991 0.7990972 2.844731
#>
#>