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] 0.1463088 0.009032835 0.1291764 0.1577415
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -9.122058 0.5749362 -9.814937 -8.240765
#> B[2,2] 29.337458 1.8448825 26.574637 31.575527
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -25.093668 2.128962 -28.78237 -22.87146
#> B[3,2] -8.671187 1.338821 -10.92238 -6.83709
#> B[3,3] 41.961601 3.227598 37.78359 47.38380
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.07848410 0.03742100 1.02289448 1.12821216
#> lag1_var2 0.04793021 0.01002822 0.03314085 0.06038962
#> lag1_var3 -0.99442768 0.04659825 -1.04786504 -0.92079391
#> const 0.45187505 0.09010311 0.31056622 0.60232381
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.006528893 0.013118161 -0.0161531 0.03013003
#> lag1_var2 0.978424932 0.008937848 0.9661768 0.99093117
#> lag1_var3 -0.283876436 0.018270584 -0.3119400 -0.24995013
#> const -0.201258634 0.061025674 -0.2746592 -0.11636077
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.07489302 0.028004602 0.03315787 0.11144552
#> lag1_var2 0.03630131 0.008622314 0.02578227 0.04953043
#> lag1_var3 0.31214088 0.035029962 0.27118482 0.36507477
#> const 0.34093693 0.069281883 0.24074181 0.45456348
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 204.0904 118.7422 42.76632 377.9641
#> B[2,]_shrinkage 418.2846 327.2226 115.76630 1084.1153
#> B[3,]_shrinkage 409.5379 242.6494 166.12470 818.3264
#> B[1,]_shrinkage_scale 1915.5645 994.8332 522.34557 3363.0842
#> B[2,]_shrinkage_scale 2188.1161 1027.6212 640.31230 3615.6016
#> B[3,]_shrinkage_scale 2422.1086 1258.3076 509.63617 4543.4095
#> B_global_scale 207.0792 103.5440 50.29796 338.5183
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.5456872 0.2967020 0.3112557 1.2465107
#> A[2,]_shrinkage 0.4383701 0.2110306 0.1567067 0.7801562
#> A[3,]_shrinkage 0.5731561 0.2722667 0.3125718 0.9904203
#> A[1,]_shrinkage_scale 6.0147272 2.5500143 3.0301482 9.6348756
#> A[2,]_shrinkage_scale 5.4767823 2.5994272 2.6107193 8.1987593
#> A[3,]_shrinkage_scale 5.6181383 1.5825742 3.4034778 8.1837449
#> A_global_scale 0.6862015 0.1904112 0.4653403 1.0070992
#>
#>
# 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] 0.1463088 0.009032835 0.1291764 0.1577415
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -9.122058 0.5749362 -9.814937 -8.240765
#> B[2,2] 29.337458 1.8448825 26.574637 31.575527
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -25.093668 2.128962 -28.78237 -22.87146
#> B[3,2] -8.671187 1.338821 -10.92238 -6.83709
#> B[3,3] 41.961601 3.227598 37.78359 47.38380
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.07848410 0.03742100 1.02289448 1.12821216
#> lag1_var2 0.04793021 0.01002822 0.03314085 0.06038962
#> lag1_var3 -0.99442768 0.04659825 -1.04786504 -0.92079391
#> const 0.45187505 0.09010311 0.31056622 0.60232381
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.006528893 0.013118161 -0.0161531 0.03013003
#> lag1_var2 0.978424932 0.008937848 0.9661768 0.99093117
#> lag1_var3 -0.283876436 0.018270584 -0.3119400 -0.24995013
#> const -0.201258634 0.061025674 -0.2746592 -0.11636077
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.07489302 0.028004602 0.03315787 0.11144552
#> lag1_var2 0.03630131 0.008622314 0.02578227 0.04953043
#> lag1_var3 0.31214088 0.035029962 0.27118482 0.36507477
#> const 0.34093693 0.069281883 0.24074181 0.45456348
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 204.0904 118.7422 42.76632 377.9641
#> B[2,]_shrinkage 418.2846 327.2226 115.76630 1084.1153
#> B[3,]_shrinkage 409.5379 242.6494 166.12470 818.3264
#> B[1,]_shrinkage_scale 1915.5645 994.8332 522.34557 3363.0842
#> B[2,]_shrinkage_scale 2188.1161 1027.6212 640.31230 3615.6016
#> B[3,]_shrinkage_scale 2422.1086 1258.3076 509.63617 4543.4095
#> B_global_scale 207.0792 103.5440 50.29796 338.5183
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.5456872 0.2967020 0.3112557 1.2465107
#> A[2,]_shrinkage 0.4383701 0.2110306 0.1567067 0.7801562
#> A[3,]_shrinkage 0.5731561 0.2722667 0.3125718 0.9904203
#> A[1,]_shrinkage_scale 6.0147272 2.5500143 3.0301482 9.6348756
#> A[2,]_shrinkage_scale 5.4767823 2.5994272 2.6107193 8.1987593
#> A[3,]_shrinkage_scale 5.6181383 1.5825742 3.4034778 8.1837449
#> A_global_scale 0.6862015 0.1904112 0.4653403 1.0070992
#>
#>