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
specification = specify_bsvar_sv$new(us_fiscal_lsuw)
#> The identification is set to the default option of lower-triangular structural matrix.
burn_in = estimate(specification, 5)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-SV model |
#> Non-centred SV model is estimated |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
posterior = estimate(burn_in, 5)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-SV model |
#> Non-centred SV model is estimated |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
summ = summary(posterior)
summ
#> $B
#> $B$ttr
#> mean sd 5% quantile 95% quantile
#> B[1,1] 0.7524348 0.02680938 0.7228779 0.784886
#>
#> $B$gs
#> mean sd 5% quantile 95% quantile
#> B[2,1] -29.18650 1.455234 -31.06154 -27.82874
#> B[2,2] 22.85501 1.146352 21.81268 24.33956
#>
#> $B$gdp
#> mean sd 5% quantile 95% quantile
#> B[3,1] -24.70986 2.829929 -27.61018 -21.13874
#> B[3,2] -40.03415 2.815522 -42.86223 -36.45396
#> B[3,3] 40.80452 2.849672 37.63170 43.78543
#>
#>
#> $A
#> $A$ttr
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.89332372 0.02624981 0.86805693 0.926407899
#> lag1_var2 -0.02199348 0.01196633 -0.03314638 -0.006506866
#> lag1_var3 -0.02161809 0.03646169 -0.06609304 0.012092153
#> const -0.09774041 0.12071267 -0.23478019 0.040821776
#>
#> $A$gs
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.08395921 0.017697743 -0.1033178 -0.06227824
#> lag1_var2 0.92954859 0.009041927 0.9176759 0.93774001
#> lag1_var3 -0.08444812 0.022186023 -0.1132686 -0.06331227
#> const -0.48623468 0.084894768 -0.5917290 -0.40110168
#>
#> $A$gdp
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.07947027 0.024964175 -0.10155669 -0.04712624
#> lag1_var2 -0.05113679 0.006341127 -0.05845706 -0.04425603
#> lag1_var3 0.81373284 0.033281303 0.76956737 0.83951408
#> const -0.26672639 0.071372415 -0.34383689 -0.18026460
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 10.56150 4.417036 7.445969 16.54145
#> B[2,]_shrinkage 145.71418 43.398668 115.109980 205.47844
#> B[3,]_shrinkage 385.43400 198.772565 253.231803 659.28887
#> B[1,]_shrinkage_scale 142.57367 55.492057 88.148040 214.25706
#> B[2,]_shrinkage_scale 219.03958 62.145313 150.250820 280.88687
#> B[3,]_shrinkage_scale 292.36507 105.596079 184.011970 409.71809
#> B_global_scale 19.21773 7.523432 11.814163 28.30318
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.6659376 0.22767495 0.4126714 0.8767868
#> A[2,]_shrinkage 0.5197614 0.14036953 0.3733379 0.6861794
#> A[3,]_shrinkage 0.4473919 0.16233770 0.3268141 0.6689358
#> A[1,]_shrinkage_scale 6.1306701 0.90188535 4.9649235 6.9163193
#> A[2,]_shrinkage_scale 5.8914369 0.45376671 5.3192419 6.3272534
#> A[3,]_shrinkage_scale 4.6908156 0.95583941 3.4338075 5.5289702
#> A_global_scale 0.6194208 0.09542415 0.5074372 0.7251827
#>
#>
# workflow with the pipe |>
############################################################
us_fiscal_lsuw |>
specify_bsvar_sv$new() |>
estimate(S = 5) |>
estimate(S = 5) |>
summary() -> summ
#> 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 5 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 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
summ
#> $B
#> $B$ttr
#> mean sd 5% quantile 95% quantile
#> B[1,1] 0.2260005 0.01933395 0.20775 0.2499536
#>
#> $B$gs
#> mean sd 5% quantile 95% quantile
#> B[2,1] -50.03601 1.928703 -51.85349 -47.53856
#> B[2,2] 46.71821 1.810868 44.36862 48.40386
#>
#> $B$gdp
#> mean sd 5% quantile 95% quantile
#> B[3,1] -40.75562 4.340580 -46.28969 -36.65580
#> B[3,2] -40.16594 2.911820 -43.37082 -37.05647
#> B[3,3] 68.24234 5.657974 62.98978 75.53306
#>
#>
#> $A
#> $A$ttr
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.00622447 0.015032907 0.99272026 1.0237934
#> lag1_var2 -0.02461387 0.008451842 -0.03478261 -0.0158823
#> lag1_var3 -0.72667406 0.018580635 -0.74829688 -0.7107594
#> const 0.29181455 0.081875862 0.18951259 0.3749824
#>
#> $A$gs
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.05401929 0.011936999 0.03929333 0.06670229
#> lag1_var2 0.92011159 0.008929686 0.91384665 0.93242117
#> lag1_var3 -0.82675052 0.014958504 -0.84531295 -0.81148609
#> const -0.14310303 0.071431348 -0.20829499 -0.04838837
#>
#> $A$gdp
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.07373846 0.016789478 0.05058289 0.08496491
#> lag1_var2 -0.04119476 0.007344152 -0.05074327 -0.03447128
#> lag1_var3 0.03107087 0.024003716 0.01591822 0.06429912
#> const 0.29191077 0.045625286 0.23071423 0.33157457
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 27.41060 22.62285 6.049032 56.19062
#> B[2,]_shrinkage 610.37689 319.37753 379.651974 1047.15314
#> B[3,]_shrinkage 1226.97252 406.37036 764.020657 1656.57916
#> B[1,]_shrinkage_scale 327.61610 249.87030 124.636789 662.29497
#> B[2,]_shrinkage_scale 549.12819 366.11095 280.613639 1040.54703
#> B[3,]_shrinkage_scale 809.78258 687.02034 249.032814 1707.50630
#> B_global_scale 38.63965 26.09554 16.490902 72.09242
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 1.526854 0.3104712 1.2698616 1.907183
#> A[2,]_shrinkage 1.341085 0.5019571 0.8649977 1.974786
#> A[3,]_shrinkage 2.135608 0.9946437 0.9048409 3.071828
#> A[1,]_shrinkage_scale 15.171483 6.4602221 8.3919109 23.099540
#> A[2,]_shrinkage_scale 14.301220 4.2423530 9.9330633 19.307504
#> A[3,]_shrinkage_scale 19.165159 5.8850714 11.5946978 24.500925
#> A_global_scale 1.525911 0.6975033 1.0107929 2.476682
#>
#>