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] 0.8859504 0.04557373 0.8390233 0.9705268
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -14.51780 0.9649436 -16.31072 -13.26978
#> B[2,2] 26.47413 1.7613650 24.24419 29.60215
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -24.104179 1.9709510 -27.222219 -21.860571
#> B[3,2] -12.139160 1.4572263 -14.147340 -9.979660
#> B[3,3] 4.694757 0.3745233 4.298605 5.206056
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.995520830 0.01379379 0.97880356 1.01789831
#> lag1_var2 -0.009726718 0.01493311 -0.03190701 0.01013020
#> lag1_var3 -0.140151641 0.01709098 -0.16685603 -0.12048095
#> const -0.093901179 0.12765971 -0.30678376 0.07955208
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.01216069 0.01407726 -0.02894543 0.01419200
#> lag1_var2 0.96204461 0.01466788 0.94028778 0.98128622
#> lag1_var3 -0.06207442 0.01863837 -0.09366021 -0.04089315
#> const -0.35814747 0.11690882 -0.51223291 -0.18774552
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.23526160 0.08565937 0.08332651 0.352730995
#> lag1_var2 0.02331848 0.12389679 -0.15991602 0.183045821
#> lag1_var3 -0.26159894 0.12098253 -0.44146429 -0.008021952
#> const 0.13553997 1.01445366 -1.15857762 1.457816020
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 69.81477 37.09537 38.37764 121.5577
#> B[2,]_shrinkage 149.03715 69.05517 81.80745 279.0465
#> B[3,]_shrinkage 161.07046 65.07743 87.54984 262.7530
#> B[1,]_shrinkage_scale 645.44736 197.93789 315.88403 994.4652
#> B[2,]_shrinkage_scale 834.60526 294.00010 415.29299 1208.4563
#> B[3,]_shrinkage_scale 860.80737 244.48031 599.95750 1255.5275
#> B_global_scale 76.03565 21.48212 45.89639 104.9907
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.7581819 0.2851213 0.4624183 1.331598
#> A[2,]_shrinkage 0.9213164 0.6994917 0.3593850 2.237067
#> A[3,]_shrinkage 0.9390282 0.3947502 0.5158148 1.576518
#> A[1,]_shrinkage_scale 8.6563628 2.3525220 5.6104726 10.793061
#> A[2,]_shrinkage_scale 8.4133668 3.1244351 5.4802277 14.226370
#> A[3,]_shrinkage_scale 9.2380758 2.3301440 5.8405356 13.026686
#> A_global_scale 0.9381420 0.1684611 0.7384985 1.251813
#>
#>
# 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] 0.8859504 0.04557373 0.8390233 0.9705268
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -14.51780 0.9649436 -16.31072 -13.26978
#> B[2,2] 26.47413 1.7613650 24.24419 29.60215
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -24.104179 1.9709510 -27.222219 -21.860571
#> B[3,2] -12.139160 1.4572263 -14.147340 -9.979660
#> B[3,3] 4.694757 0.3745233 4.298605 5.206056
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.995520830 0.01379379 0.97880356 1.01789831
#> lag1_var2 -0.009726718 0.01493311 -0.03190701 0.01013020
#> lag1_var3 -0.140151641 0.01709098 -0.16685603 -0.12048095
#> const -0.093901179 0.12765971 -0.30678376 0.07955208
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.01216069 0.01407726 -0.02894543 0.01419200
#> lag1_var2 0.96204461 0.01466788 0.94028778 0.98128622
#> lag1_var3 -0.06207442 0.01863837 -0.09366021 -0.04089315
#> const -0.35814747 0.11690882 -0.51223291 -0.18774552
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.23526160 0.08565937 0.08332651 0.352730995
#> lag1_var2 0.02331848 0.12389679 -0.15991602 0.183045821
#> lag1_var3 -0.26159894 0.12098253 -0.44146429 -0.008021952
#> const 0.13553997 1.01445366 -1.15857762 1.457816020
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 69.81477 37.09537 38.37764 121.5577
#> B[2,]_shrinkage 149.03715 69.05517 81.80745 279.0465
#> B[3,]_shrinkage 161.07046 65.07743 87.54984 262.7530
#> B[1,]_shrinkage_scale 645.44736 197.93789 315.88403 994.4652
#> B[2,]_shrinkage_scale 834.60526 294.00010 415.29299 1208.4563
#> B[3,]_shrinkage_scale 860.80737 244.48031 599.95750 1255.5275
#> B_global_scale 76.03565 21.48212 45.89639 104.9907
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.7581819 0.2851213 0.4624183 1.331598
#> A[2,]_shrinkage 0.9213164 0.6994917 0.3593850 2.237067
#> A[3,]_shrinkage 0.9390282 0.3947502 0.5158148 1.576518
#> A[1,]_shrinkage_scale 8.6563628 2.3525220 5.6104726 10.793061
#> A[2,]_shrinkage_scale 8.4133668 3.1244351 5.4802277 14.226370
#> A[3,]_shrinkage_scale 9.2380758 2.3301440 5.8405356 13.026686
#> A_global_scale 0.9381420 0.1684611 0.7384985 1.251813
#>
#>