Provides posterior summary of homoskedastic Structural VAR estimation
Source:R/summary.R
summary.PosteriorBSVAR.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 'PosteriorBSVAR'
summary(object, ...)Arguments
- object
an object of class PosteriorBSVAR obtained using the
estimate()function applied to homoskedastic Bayesian Structural VAR model specification set by functionspecify_bsvar$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$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 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 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] 35.09588 1.686944 32.60351 37.48121
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -0.1028203 2.565796 -2.789346 5.252764
#> B[2,2] 39.4936838 2.545994 36.414127 43.647928
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -14.2980661 1.532602 -16.768833 -12.05737
#> B[3,2] -0.2501413 2.629023 -4.610455 3.39692
#> B[3,3] 97.4220490 4.249847 91.098013 103.00009
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.913398680 0.01679839 0.88815999 0.93957593
#> lag1_var2 -0.001027232 0.01422207 -0.01998918 0.01841525
#> lag1_var3 0.103631364 0.02346832 0.06937820 0.13147049
#> const -0.043058966 0.10580799 -0.22013019 0.14085229
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.02252694 0.02217486 -0.04814025 0.01829944
#> lag1_var2 0.95238376 0.01174687 0.93381567 0.97616006
#> lag1_var3 0.03295538 0.02871608 -0.01258597 0.07053000
#> const -0.43677036 0.10332090 -0.60060236 -0.26884739
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.017960694 0.007821574 -0.02641538 -0.006497867
#> lag1_var2 -0.004662448 0.004312109 -0.01030642 0.002116785
#> lag1_var3 1.020024669 0.010466417 1.00624043 1.032285960
#> const -0.061224135 0.030759783 -0.09527021 -0.018315391
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 508.5376 352.7570 169.84064 1171.878
#> B[2,]_shrinkage 582.6517 651.6802 174.95082 2240.144
#> B[3,]_shrinkage 1115.3237 532.3208 521.39944 1924.157
#> B[1,]_shrinkage_scale 3514.8043 3966.1603 566.36648 10829.201
#> B[2,]_shrinkage_scale 4519.4759 5223.2397 563.08518 12036.201
#> B[3,]_shrinkage_scale 4332.5062 5099.1766 638.50374 13759.712
#> B_global_scale 364.5536 402.9807 39.86076 1148.559
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.3385170 0.17698822 0.08581606 0.6219381
#> A[2,]_shrinkage 0.2848364 0.16713385 0.11335237 0.5611669
#> A[3,]_shrinkage 0.3237228 0.26792304 0.07360823 0.5715186
#> A[1,]_shrinkage_scale 3.9576003 1.46323260 1.56941879 5.8459596
#> A[2,]_shrinkage_scale 3.3051021 1.47918569 1.90384497 6.4437421
#> A[3,]_shrinkage_scale 3.5180074 1.92050729 1.26113427 6.8988395
#> A_global_scale 0.4550062 0.07662271 0.33770816 0.5882126
#>
#>
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar$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 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 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] 35.09588 1.686944 32.60351 37.48121
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -0.1028203 2.565796 -2.789346 5.252764
#> B[2,2] 39.4936838 2.545994 36.414127 43.647928
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -14.2980661 1.532602 -16.768833 -12.05737
#> B[3,2] -0.2501413 2.629023 -4.610455 3.39692
#> B[3,3] 97.4220490 4.249847 91.098013 103.00009
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.913398680 0.01679839 0.88815999 0.93957593
#> lag1_var2 -0.001027232 0.01422207 -0.01998918 0.01841525
#> lag1_var3 0.103631364 0.02346832 0.06937820 0.13147049
#> const -0.043058966 0.10580799 -0.22013019 0.14085229
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.02252694 0.02217486 -0.04814025 0.01829944
#> lag1_var2 0.95238376 0.01174687 0.93381567 0.97616006
#> lag1_var3 0.03295538 0.02871608 -0.01258597 0.07053000
#> const -0.43677036 0.10332090 -0.60060236 -0.26884739
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.017960694 0.007821574 -0.02641538 -0.006497867
#> lag1_var2 -0.004662448 0.004312109 -0.01030642 0.002116785
#> lag1_var3 1.020024669 0.010466417 1.00624043 1.032285960
#> const -0.061224135 0.030759783 -0.09527021 -0.018315391
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 508.5376 352.7570 169.84064 1171.878
#> B[2,]_shrinkage 582.6517 651.6802 174.95082 2240.144
#> B[3,]_shrinkage 1115.3237 532.3208 521.39944 1924.157
#> B[1,]_shrinkage_scale 3514.8043 3966.1603 566.36648 10829.201
#> B[2,]_shrinkage_scale 4519.4759 5223.2397 563.08518 12036.201
#> B[3,]_shrinkage_scale 4332.5062 5099.1766 638.50374 13759.712
#> B_global_scale 364.5536 402.9807 39.86076 1148.559
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.3385170 0.17698822 0.08581606 0.6219381
#> A[2,]_shrinkage 0.2848364 0.16713385 0.11335237 0.5611669
#> A[3,]_shrinkage 0.3237228 0.26792304 0.07360823 0.5715186
#> A[1,]_shrinkage_scale 3.9576003 1.46323260 1.56941879 5.8459596
#> A[2,]_shrinkage_scale 3.3051021 1.47918569 1.90384497 6.4437421
#> A[3,]_shrinkage_scale 3.5180074 1.92050729 1.26113427 6.8988395
#> A_global_scale 0.4550062 0.07662271 0.33770816 0.5882126
#>
#>