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] 35.08773 1.758916 32.53032 38.35467
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -0.679238 1.934894 -2.963047 3.332628
#> B[2,2] 39.713185 1.755591 36.911630 41.765456
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -14.3842400 1.902854 -17.040966 -10.98104
#> B[3,2] 0.1637784 2.471842 -2.835244 4.39948
#> B[3,3] 96.1747125 4.945967 89.280074 103.26470
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.9172540818 0.02194780 0.88728861 0.94503673
#> lag1_var2 -0.0004097986 0.01243653 -0.02265821 0.01458568
#> lag1_var3 0.1017874130 0.02780531 0.06399140 0.13972764
#> const -0.0145288728 0.10016880 -0.20829325 0.10032816
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.02393269 0.01914904 -0.058144563 -0.00459606
#> lag1_var2 0.95192073 0.01036179 0.937837593 0.96765373
#> lag1_var3 0.03545451 0.02381736 0.006204548 0.07312802
#> const -0.43600024 0.08555523 -0.544058761 -0.31875692
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.017273456 0.008281776 -0.027653235 -0.006335693
#> lag1_var2 -0.005209727 0.003049773 -0.009818577 -0.001968844
#> lag1_var3 1.020727278 0.010198660 1.005376398 1.036492598
#> const -0.054869268 0.024676501 -0.086703840 -0.024959999
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 920.0554 631.5168 242.9323 2258.856
#> B[2,]_shrinkage 892.1252 726.2191 203.1311 1873.056
#> B[3,]_shrinkage 1844.2555 1189.3819 662.1813 4154.248
#> B[1,]_shrinkage_scale 8246.8191 7598.4992 1404.1106 21432.818
#> B[2,]_shrinkage_scale 7466.1058 5543.6684 1674.8716 16393.465
#> B[3,]_shrinkage_scale 9374.6374 7370.7459 2042.0064 19601.266
#> B_global_scale 787.6242 588.9231 159.5532 1549.593
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.2952908 0.1755818 0.07769445 0.6539009
#> A[2,]_shrinkage 0.4351435 0.3645229 0.11757557 0.9623471
#> A[3,]_shrinkage 0.3226787 0.2034372 0.07120971 0.7499557
#> A[1,]_shrinkage_scale 3.6714253 1.5069793 1.56881446 5.8480080
#> A[2,]_shrinkage_scale 4.8400026 2.6598311 1.84483235 9.7674471
#> A[3,]_shrinkage_scale 4.0290767 2.5481605 1.21578511 6.1798450
#> A_global_scale 0.5186554 0.2339998 0.26082635 0.8621645
#>
#>
# 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] 35.08773 1.758916 32.53032 38.35467
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -0.679238 1.934894 -2.963047 3.332628
#> B[2,2] 39.713185 1.755591 36.911630 41.765456
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -14.3842400 1.902854 -17.040966 -10.98104
#> B[3,2] 0.1637784 2.471842 -2.835244 4.39948
#> B[3,3] 96.1747125 4.945967 89.280074 103.26470
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.9172540818 0.02194780 0.88728861 0.94503673
#> lag1_var2 -0.0004097986 0.01243653 -0.02265821 0.01458568
#> lag1_var3 0.1017874130 0.02780531 0.06399140 0.13972764
#> const -0.0145288728 0.10016880 -0.20829325 0.10032816
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.02393269 0.01914904 -0.058144563 -0.00459606
#> lag1_var2 0.95192073 0.01036179 0.937837593 0.96765373
#> lag1_var3 0.03545451 0.02381736 0.006204548 0.07312802
#> const -0.43600024 0.08555523 -0.544058761 -0.31875692
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.017273456 0.008281776 -0.027653235 -0.006335693
#> lag1_var2 -0.005209727 0.003049773 -0.009818577 -0.001968844
#> lag1_var3 1.020727278 0.010198660 1.005376398 1.036492598
#> const -0.054869268 0.024676501 -0.086703840 -0.024959999
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 920.0554 631.5168 242.9323 2258.856
#> B[2,]_shrinkage 892.1252 726.2191 203.1311 1873.056
#> B[3,]_shrinkage 1844.2555 1189.3819 662.1813 4154.248
#> B[1,]_shrinkage_scale 8246.8191 7598.4992 1404.1106 21432.818
#> B[2,]_shrinkage_scale 7466.1058 5543.6684 1674.8716 16393.465
#> B[3,]_shrinkage_scale 9374.6374 7370.7459 2042.0064 19601.266
#> B_global_scale 787.6242 588.9231 159.5532 1549.593
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.2952908 0.1755818 0.07769445 0.6539009
#> A[2,]_shrinkage 0.4351435 0.3645229 0.11757557 0.9623471
#> A[3,]_shrinkage 0.3226787 0.2034372 0.07120971 0.7499557
#> A[1,]_shrinkage_scale 3.6714253 1.5069793 1.56881446 5.8480080
#> A[2,]_shrinkage_scale 4.8400026 2.6598311 1.84483235 9.7674471
#> A[3,]_shrinkage_scale 4.0290767 2.5481605 1.21578511 6.1798450
#> A_global_scale 0.5186554 0.2339998 0.26082635 0.8621645
#>
#>