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] 35.23577 1.821631 32.36055 38.3665
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -1.210886 2.462629 -4.903203 2.574583
#> B[2,2] 39.933172 1.577864 37.891761 42.797654
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -14.4290114 2.183303 -18.311217 -10.870478
#> B[3,2] -0.2140076 1.952149 -2.780451 2.623556
#> B[3,3] 95.9199823 4.768203 89.049043 101.662956
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.9105950813 0.022897907 0.872407653 0.94277588
#> lag1_var2 0.0021691467 0.009545828 -0.008742908 0.01329558
#> lag1_var3 0.1085479404 0.029028793 0.066425311 0.15385028
#> const -0.0006590507 0.082530820 -0.116474351 0.11584120
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.03044777 0.01927840 -0.05355648 0.0003724801
#> lag1_var2 0.95506313 0.01016851 0.93579180 0.9719528959
#> lag1_var3 0.04324218 0.02450106 0.01037155 0.0756095277
#> const -0.40742494 0.08489388 -0.54064748 -0.2515980366
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.017295931 0.006078960 -0.02498971 -0.0086500943
#> lag1_var2 -0.005248387 0.003298598 -0.01062149 -0.0009931715
#> lag1_var3 1.020548413 0.007743462 1.01112865 1.0313859055
#> const -0.056767739 0.024627906 -0.08933259 -0.0272952936
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 1057.1535 737.1846 334.7029 2674.538
#> B[2,]_shrinkage 1028.1827 860.1049 218.8209 2202.194
#> B[3,]_shrinkage 2188.8845 1390.2292 688.5259 4629.800
#> B[1,]_shrinkage_scale 9836.1378 8981.0008 1814.1446 25396.615
#> B[2,]_shrinkage_scale 9483.7197 6633.4345 1539.5779 19785.059
#> B[3,]_shrinkage_scale 11496.0191 8675.7928 2201.5938 22906.932
#> B_global_scale 990.5311 720.1240 134.9493 1849.741
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.2723898 0.1449999 0.09040047 0.4828196
#> A[2,]_shrinkage 0.3407613 0.1481311 0.15815831 0.5521471
#> A[3,]_shrinkage 0.4278662 0.1939110 0.24638296 0.8068997
#> A[1,]_shrinkage_scale 3.7690895 1.7151073 1.79984087 6.0602411
#> A[2,]_shrinkage_scale 4.7045419 1.8164610 2.44544168 7.1956499
#> A[3,]_shrinkage_scale 5.8212150 2.9892229 2.16825723 11.0994786
#> A_global_scale 0.6126614 0.2386865 0.34045996 1.0201557
#>
#>
# 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] 35.23577 1.821631 32.36055 38.3665
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -1.210886 2.462629 -4.903203 2.574583
#> B[2,2] 39.933172 1.577864 37.891761 42.797654
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -14.4290114 2.183303 -18.311217 -10.870478
#> B[3,2] -0.2140076 1.952149 -2.780451 2.623556
#> B[3,3] 95.9199823 4.768203 89.049043 101.662956
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.9105950813 0.022897907 0.872407653 0.94277588
#> lag1_var2 0.0021691467 0.009545828 -0.008742908 0.01329558
#> lag1_var3 0.1085479404 0.029028793 0.066425311 0.15385028
#> const -0.0006590507 0.082530820 -0.116474351 0.11584120
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.03044777 0.01927840 -0.05355648 0.0003724801
#> lag1_var2 0.95506313 0.01016851 0.93579180 0.9719528959
#> lag1_var3 0.04324218 0.02450106 0.01037155 0.0756095277
#> const -0.40742494 0.08489388 -0.54064748 -0.2515980366
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.017295931 0.006078960 -0.02498971 -0.0086500943
#> lag1_var2 -0.005248387 0.003298598 -0.01062149 -0.0009931715
#> lag1_var3 1.020548413 0.007743462 1.01112865 1.0313859055
#> const -0.056767739 0.024627906 -0.08933259 -0.0272952936
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 1057.1535 737.1846 334.7029 2674.538
#> B[2,]_shrinkage 1028.1827 860.1049 218.8209 2202.194
#> B[3,]_shrinkage 2188.8845 1390.2292 688.5259 4629.800
#> B[1,]_shrinkage_scale 9836.1378 8981.0008 1814.1446 25396.615
#> B[2,]_shrinkage_scale 9483.7197 6633.4345 1539.5779 19785.059
#> B[3,]_shrinkage_scale 11496.0191 8675.7928 2201.5938 22906.932
#> B_global_scale 990.5311 720.1240 134.9493 1849.741
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.2723898 0.1449999 0.09040047 0.4828196
#> A[2,]_shrinkage 0.3407613 0.1481311 0.15815831 0.5521471
#> A[3,]_shrinkage 0.4278662 0.1939110 0.24638296 0.8068997
#> A[1,]_shrinkage_scale 3.7690895 1.7151073 1.79984087 6.0602411
#> A[2,]_shrinkage_scale 4.7045419 1.8164610 2.44544168 7.1956499
#> A[3,]_shrinkage_scale 5.8212150 2.9892229 2.16825723 11.0994786
#> A_global_scale 0.6126614 0.2386865 0.34045996 1.0201557
#>
#>