Provides posterior summary of heteroskedastic Structural VAR estimation
Source:R/summary.R
summary.PosteriorBSVAREXH.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 'PosteriorBSVAREXH'
summary(object, ...)Arguments
- object
an object of class PosteriorBSVAREXH obtained using the
estimate()function applied to heteroskedastic Bayesian Structural VAR model specification set by functionspecify_bsvar_exh$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
# specify the model and set seed
spec = specify_bsvar_exh$new(us_fiscal_lsuw)
#> The identification is set to the default option of lower-triangular structural matrix.
# run the burn-in
burn = estimate(spec, 10)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-exH 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
post = estimate(burn, 10)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-exH model |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
summary(post)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of the parameters |
#> **************************************************|
#> $B
#> $B$equation1
#> mean sd 5% quantile 95% quantile
#> B[1,1] 5.749783 0.1938326 5.439853 5.954017
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -33.744831 1.0632454 -34.903364 -32.187470
#> B[2,2] 1.795314 0.0447733 1.734957 1.853742
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -4.232686 2.2668853 -7.332903 -1.420974
#> B[3,2] -38.488787 0.9838067 -40.109052 -37.247616
#> B[3,3] 18.872336 0.5017798 18.236562 19.689836
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.9118501421 0.014668140 0.88978016 0.93043221
#> lag1_var2 -0.0007275159 0.008412255 -0.00976606 0.01234599
#> lag1_var3 0.0850005545 0.019968956 0.05800662 0.11245503
#> const -0.0271060206 0.062669669 -0.08698203 0.07809794
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.1871885 0.02277577 -0.2170248 -0.1517003
#> lag1_var2 0.9793956 0.01565940 0.9608505 1.0024932
#> lag1_var3 -0.2165115 0.03150847 -0.2648856 -0.1774594
#> const -0.3541337 0.11540478 -0.4827489 -0.1886364
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.33986623 0.03011450 -0.384583418 -0.3020010
#> lag1_var2 0.04860724 0.03343760 0.007737815 0.0946607
#> lag1_var3 0.48767491 0.04014538 0.440271247 0.5442280
#> const 0.08118711 0.25144475 -0.220706203 0.4132663
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 87.40012 76.53544 25.38009 222.4703
#> B[2,]_shrinkage 179.66797 38.64603 124.76875 228.7738
#> B[3,]_shrinkage 281.57612 107.17875 168.33279 440.0990
#> B[1,]_shrinkage_scale 606.69353 180.91011 331.35604 825.6715
#> B[2,]_shrinkage_scale 871.46964 292.40066 564.42463 1295.1194
#> B[3,]_shrinkage_scale 1021.66290 332.26258 634.56440 1565.7040
#> B_global_scale 78.89192 20.49748 46.09538 104.1703
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.5352850 0.3319084 0.2813669 1.0637461
#> A[2,]_shrinkage 0.7937538 0.8030465 0.2594349 2.0446705
#> A[3,]_shrinkage 0.4541261 0.2218712 0.1923428 0.7701345
#> A[1,]_shrinkage_scale 6.7777081 2.0877072 4.0114236 9.9675143
#> A[2,]_shrinkage_scale 8.0766367 5.5813297 3.8127870 17.4456886
#> A[3,]_shrinkage_scale 6.4124918 2.8925133 3.5447530 10.6230618
#> A_global_scale 0.8776608 0.3686819 0.5797221 1.5395776
#>
#>
# workflow with the pipe |>
############################################################
us_fiscal_lsuw |>
specify_bsvar_exh$new() |>
estimate(S = 10) |>
estimate(S = 10) |>
summary()
#> The identification is set to the default option of lower-triangular structural matrix.
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-exH 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-exH 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|
#> **************************************************|
#> Posterior summary of the parameters |
#> **************************************************|
#> $B
#> $B$equation1
#> mean sd 5% quantile 95% quantile
#> B[1,1] 0.1209625 0.003903127 0.1160647 0.1265312
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -22.47714 1.046601 -23.54928 -20.81280
#> B[2,2] 30.67443 1.415790 28.42524 32.11893
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -28.76823 1.222999 -30.54431 -27.41912
#> B[3,2] -17.22867 1.513954 -19.80128 -15.63967
#> B[3,3] 65.99116 2.850264 62.99648 70.74693
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.9266452 0.056303052 0.8456058 0.98953312
#> lag1_var2 0.0404513 0.009137871 0.0307806 0.05589086
#> lag1_var3 -1.0193105 0.066801702 -1.0948305 -0.92537623
#> const 0.3805956 0.085080217 0.2665109 0.49444249
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.03555481 0.03270830 -0.07867204 0.01357759
#> lag1_var2 0.97838455 0.01098236 0.96374922 0.99283516
#> lag1_var3 -0.76284437 0.03922775 -0.81836382 -0.71132549
#> const -0.18161753 0.12688193 -0.32472357 0.02065539
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.01017607 0.026076966 -0.04709795 0.01956522
#> lag1_var2 0.01633176 0.004311044 0.01001308 0.02163090
#> lag1_var3 0.31822344 0.031355061 0.28137838 0.36141581
#> const 0.16844103 0.049251047 0.09477383 0.23049269
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 59.77855 53.35694 15.96248 153.5574
#> B[2,]_shrinkage 232.51474 65.72658 145.63529 334.0628
#> B[3,]_shrinkage 772.93003 241.23120 561.86698 1142.1497
#> B[1,]_shrinkage_scale 676.44131 318.61852 325.14100 1090.4309
#> B[2,]_shrinkage_scale 1430.53358 431.46495 728.51737 1919.0277
#> B[3,]_shrinkage_scale 1926.12535 628.97502 1081.04294 2785.0398
#> B_global_scale 120.75085 34.63651 64.10951 155.1692
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.3831704 0.14187558 0.1945153 0.5457176
#> A[2,]_shrinkage 0.3563792 0.26870756 0.1532991 0.8318858
#> A[3,]_shrinkage 0.4548902 0.14170682 0.2995978 0.6688261
#> A[1,]_shrinkage_scale 3.3366022 1.17395729 1.7668151 4.8440147
#> A[2,]_shrinkage_scale 3.4276031 1.28093197 2.1087843 5.6564258
#> A[3,]_shrinkage_scale 4.4866798 1.30396499 2.9064572 6.5161362
#> A_global_scale 0.4274401 0.06696715 0.3472145 0.5235640
#>
#>