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] 0.1186827 0.003214882 0.1148036 0.1235951
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -2.375302 0.08461678 -2.515124 -2.279495
#> B[2,2] 38.613613 1.26967280 37.248097 40.688064
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -34.3719418 1.176715 -35.877102 -32.902578
#> B[3,2] -0.4070549 1.818362 -2.796607 1.937935
#> B[3,3] 69.2410458 2.436525 66.030104 72.219744
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.04801957 0.01859803 1.02517792 1.07250029
#> lag1_var2 0.07276879 0.01710247 0.05092473 0.09628538
#> lag1_var3 -1.17393053 0.02130958 -1.21068065 -1.15043396
#> const 0.72584738 0.08961318 0.59816997 0.85557831
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.02756030 0.01518650 -0.05060450 -0.009553035
#> lag1_var2 0.96075766 0.01246638 0.94035024 0.974656393
#> lag1_var3 -0.02990366 0.01971962 -0.05515446 -0.001746796
#> const -0.35880937 0.09619447 -0.52074257 -0.266087124
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.04941729 0.008972796 0.03530967 0.05914532
#> lag1_var2 0.02817736 0.007354138 0.01891603 0.03911239
#> lag1_var3 0.38701223 0.011866181 0.37303765 0.40524555
#> const 0.29973463 0.039761323 0.24471449 0.35322136
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 133.3350 68.11497 57.47855 244.6557
#> B[2,]_shrinkage 267.3135 161.39062 155.49960 552.7087
#> B[3,]_shrinkage 876.6243 481.54998 390.43911 1628.9220
#> B[1,]_shrinkage_scale 1214.3086 480.81992 593.85936 1840.0323
#> B[2,]_shrinkage_scale 1421.9740 455.79579 991.21218 2173.3222
#> B[3,]_shrinkage_scale 1690.9419 494.70828 1051.37108 2383.3930
#> B_global_scale 141.6229 36.55620 101.54385 196.4104
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 1.6929423 0.9918462 0.8326696 3.1991606
#> A[2,]_shrinkage 1.0437923 0.3505429 0.6550454 1.5665306
#> A[3,]_shrinkage 0.6684905 0.1758330 0.4687726 0.9331951
#> A[1,]_shrinkage_scale 12.8823339 4.9096264 8.6065174 21.4180564
#> A[2,]_shrinkage_scale 10.9629990 3.7423400 6.9063508 16.6564068
#> A[3,]_shrinkage_scale 9.0181747 1.6372850 6.9717232 11.4052882
#> A_global_scale 1.1698315 0.1548472 0.9706936 1.3829503
#>
#>
# 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] 0.1474609 0.00534023 0.1405138 0.1548521
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -29.38231 1.2368558 -31.37445 -28.06368
#> B[2,2] 21.65885 0.9064454 20.68853 23.11751
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -10.638938 1.522505 -12.315045 -8.119630
#> B[3,2] 6.285582 1.121012 4.451295 7.526795
#> B[3,3] 92.234058 2.717768 89.253021 96.258646
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.03061902 0.05279111 0.9619320 1.0912443193
#> lag1_var2 -0.61220276 0.01952387 -0.6393350 -0.5839910902
#> lag1_var3 -0.08246527 0.06265655 -0.1539550 0.0006147082
#> const 0.52772587 0.22096708 0.2575431 0.8615454641
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.1169141 0.06067619 0.03866382 0.2033203
#> lag1_var2 0.1102524 0.02362802 0.07907432 0.1459269
#> lag1_var3 -0.1979724 0.07120474 -0.29434982 -0.0986293
#> const 0.1840286 0.28404474 -0.15046459 0.6291116
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.014478931 0.009537335 -0.02660174 0.0001810202
#> lag1_var2 -0.014293164 0.004472183 -0.02010323 -0.0081437395
#> lag1_var3 1.014936518 0.012014751 0.99779398 1.0314383777
#> const -0.003388276 0.044351848 -0.06650067 0.0633157039
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 140.2157 120.07963 56.53255 321.1766
#> B[2,]_shrinkage 276.7461 83.86127 169.31157 400.6441
#> B[3,]_shrinkage 944.8312 376.69129 513.77069 1497.3037
#> B[1,]_shrinkage_scale 1246.2153 623.45540 562.97528 2201.2957
#> B[2,]_shrinkage_scale 1752.8499 974.48701 647.61608 3269.4548
#> B[3,]_shrinkage_scale 1768.8907 1118.52435 606.99707 3576.1839
#> B_global_scale 147.4883 87.66842 42.51116 273.0407
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.5896421 0.27112399 0.2696961 0.9545009
#> A[2,]_shrinkage 0.6014087 0.20549780 0.3742268 0.8782232
#> A[3,]_shrinkage 0.4050385 0.24433449 0.1695794 0.7719811
#> A[1,]_shrinkage_scale 4.5739942 2.34883864 2.3854856 8.2179968
#> A[2,]_shrinkage_scale 5.1703304 1.30462120 3.7761702 6.9828991
#> A[3,]_shrinkage_scale 4.5136788 1.31008872 2.9615145 6.6021026
#> A_global_scale 0.5448415 0.09949215 0.4278288 0.6941276
#>
#>