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] 35.32538 1.947888 33.09898 38.70744
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -0.05852654 2.843507 -3.726388 3.690433
#> B[2,2] 38.94786643 1.660281 36.734287 41.535125
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -14.5480841 2.261452 -17.895784 -11.699688
#> B[3,2] 0.1312712 2.361383 -3.534906 3.248789
#> B[3,3] 97.2979148 4.528059 90.756673 104.811816
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.920190075 0.01441084 0.89950336 0.94319345
#> lag1_var2 -0.005591443 0.01196487 -0.02339119 0.01506146
#> lag1_var3 0.098478727 0.02036251 0.06484857 0.12566029
#> const -0.063115679 0.08169851 -0.18504180 0.05531243
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.02323996 0.018520487 -0.048256216 0.008186634
#> lag1_var2 0.95215098 0.009809733 0.939435853 0.966784505
#> lag1_var3 0.03578900 0.024604704 -0.002976879 0.073164535
#> const -0.42391277 0.071763253 -0.526056673 -0.304736378
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.017362618 0.007730085 -0.02988272 -0.0072613594
#> lag1_var2 -0.008104517 0.005490463 -0.01797774 -0.0004099355
#> lag1_var3 1.020411446 0.009475792 1.00831129 1.0335312705
#> const -0.086899362 0.039083110 -0.14983130 -0.0386766815
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 872.8997 589.0555 231.6637 1643.238
#> B[2,]_shrinkage 860.0352 650.9305 154.7441 1970.938
#> B[3,]_shrinkage 1919.9001 982.6652 842.8577 3230.207
#> B[1,]_shrinkage_scale 6675.5621 3414.0366 1621.3829 12502.300
#> B[2,]_shrinkage_scale 6761.7691 3863.8927 1299.0476 12104.986
#> B[3,]_shrinkage_scale 8390.7483 4714.5816 2638.2289 15641.244
#> B_global_scale 694.4179 389.2128 143.5536 1260.961
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.4784737 0.2572436 0.2040746 0.9230891
#> A[2,]_shrinkage 0.5644127 0.3321284 0.1954102 1.2070394
#> A[3,]_shrinkage 0.5290319 0.3258285 0.2505474 1.1667932
#> A[1,]_shrinkage_scale 5.9970296 2.5309162 2.7721883 9.5767841
#> A[2,]_shrinkage_scale 6.6696716 2.9826810 3.2781065 11.6577191
#> A[3,]_shrinkage_scale 6.8789758 3.3335960 3.2577226 11.0041419
#> A_global_scale 0.7800935 0.2211279 0.5554428 1.3068948
#>
#>
# 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] 35.32538 1.947888 33.09898 38.70744
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -0.05852654 2.843507 -3.726388 3.690433
#> B[2,2] 38.94786643 1.660281 36.734287 41.535125
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -14.5480841 2.261452 -17.895784 -11.699688
#> B[3,2] 0.1312712 2.361383 -3.534906 3.248789
#> B[3,3] 97.2979148 4.528059 90.756673 104.811816
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.920190075 0.01441084 0.89950336 0.94319345
#> lag1_var2 -0.005591443 0.01196487 -0.02339119 0.01506146
#> lag1_var3 0.098478727 0.02036251 0.06484857 0.12566029
#> const -0.063115679 0.08169851 -0.18504180 0.05531243
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.02323996 0.018520487 -0.048256216 0.008186634
#> lag1_var2 0.95215098 0.009809733 0.939435853 0.966784505
#> lag1_var3 0.03578900 0.024604704 -0.002976879 0.073164535
#> const -0.42391277 0.071763253 -0.526056673 -0.304736378
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.017362618 0.007730085 -0.02988272 -0.0072613594
#> lag1_var2 -0.008104517 0.005490463 -0.01797774 -0.0004099355
#> lag1_var3 1.020411446 0.009475792 1.00831129 1.0335312705
#> const -0.086899362 0.039083110 -0.14983130 -0.0386766815
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 872.8997 589.0555 231.6637 1643.238
#> B[2,]_shrinkage 860.0352 650.9305 154.7441 1970.938
#> B[3,]_shrinkage 1919.9001 982.6652 842.8577 3230.207
#> B[1,]_shrinkage_scale 6675.5621 3414.0366 1621.3829 12502.300
#> B[2,]_shrinkage_scale 6761.7691 3863.8927 1299.0476 12104.986
#> B[3,]_shrinkage_scale 8390.7483 4714.5816 2638.2289 15641.244
#> B_global_scale 694.4179 389.2128 143.5536 1260.961
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.4784737 0.2572436 0.2040746 0.9230891
#> A[2,]_shrinkage 0.5644127 0.3321284 0.1954102 1.2070394
#> A[3,]_shrinkage 0.5290319 0.3258285 0.2505474 1.1667932
#> A[1,]_shrinkage_scale 5.9970296 2.5309162 2.7721883 9.5767841
#> A[2,]_shrinkage_scale 6.6696716 2.9826810 3.2781065 11.6577191
#> A[3,]_shrinkage_scale 6.8789758 3.3335960 3.2577226 11.0041419
#> A_global_scale 0.7800935 0.2211279 0.5554428 1.3068948
#>
#>