Skip to contents

Provides posterior summary of heteroskedastic Structural VAR estimation

Source: R/summary.R
summary.PosteriorBSVARSV.Rd

Provides 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 'PosteriorBSVARSV'
summary(object, ...)

Arguments

object

an object of class PosteriorBSVARSV obtained using the estimate() function applied to heteroskedastic Bayesian Structural VAR model specification set by function specify_bsvar_sv$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.

See also

estimate, specify_bsvar_sv

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_sv$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-SV model              |
#>    Non-centred SV model is estimated              |
#> **************************************************|
#>  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-SV model              |
#>    Non-centred SV model is estimated              |
#> **************************************************|
#>  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] 0.1469206 0.006062622   0.1402002    0.1585284
#> 
#> $B$equation2
#>             mean       sd 5% quantile 95% quantile
#> B[2,1] -12.38527 0.740964   -13.62140    -11.48032
#> B[2,2]  40.20519 2.345169    37.32229     44.10876
#> 
#> $B$equation3
#>              mean       sd 5% quantile 95% quantile
#> B[3,1] -38.385481 2.258204   -41.51627   -34.953677
#> B[3,2]  -8.951953 2.550733   -12.66373    -5.077967
#> B[3,3]  62.253502 3.438587    56.30151    68.160968
#> 
#> 
#> $A
#> $A$equation1
#>                 mean         sd 5% quantile 95% quantile
#> lag1_var1  1.0244660 0.02325616   0.9914658   1.05905990
#> lag1_var2 -0.0827699 0.03335015  -0.1183212  -0.02799828
#> lag1_var3 -0.8711731 0.03207860  -0.9177089  -0.82579169
#> const     -0.4115923 0.22784055  -0.6370424  -0.01318342
#> 
#> $A$equation2
#>                   mean         sd 5% quantile 95% quantile
#> lag1_var1  0.003971754 0.01665495 -0.02132162   0.02358129
#> lag1_var2  0.933395512 0.01525154  0.91770628   0.95563454
#> lag1_var3 -0.257876638 0.02137591 -0.28337887  -0.22059363
#> const     -0.489972492 0.11781438 -0.65028828  -0.31168524
#> 
#> $A$equation3
#>                  mean         sd 5% quantile 95% quantile
#> lag1_var1  0.04414684 0.01507222  0.02407978   0.06506311
#> lag1_var2 -0.05738968 0.02249465 -0.08594839  -0.02250236
#> lag1_var3  0.38875111 0.01968031  0.36224605   0.41886992
#> const     -0.30141027 0.14722754 -0.46388005  -0.07310653
#> 
#> 
#> $hyper
#> $hyper$B
#>                            mean        sd 5% quantile 95% quantile
#> B[1,]_shrinkage       1203.8542 1107.1196    121.5518     2978.490
#> B[2,]_shrinkage        851.5032  365.3617    398.3216     1582.319
#> B[3,]_shrinkage       1426.3654 1039.9409    385.1111     3611.604
#> B[1,]_shrinkage_scale 8073.1299 4961.1526   1386.9332    15248.222
#> B[2,]_shrinkage_scale 7935.1783 4507.5929   1830.0583    17905.325
#> B[3,]_shrinkage_scale 9019.1146 5236.3729   1887.7240    17584.601
#> B_global_scale         795.6531  449.0971    152.2418     1220.058
#> 
#> $hyper$A
#>                            mean        sd 5% quantile 95% quantile
#> A[1,]_shrinkage       0.5235967 0.2227498   0.2666590    0.8226043
#> A[2,]_shrinkage       0.4392824 0.3029605   0.1967496    1.1998566
#> A[3,]_shrinkage       0.5858711 0.2622841   0.2392140    1.0114170
#> A[1,]_shrinkage_scale 5.5126318 1.8178165   3.5266149    7.6545847
#> A[2,]_shrinkage_scale 5.0813030 1.9164053   3.0974824    7.9525957
#> A[3,]_shrinkage_scale 6.4805349 3.3732470   3.5601451   13.8244833
#> A_global_scale        0.6678598 0.1653758   0.4423705    0.9422769
#> 
#> 

# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
  specify_bsvar_sv$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-SV model              |
#>    Non-centred SV model is estimated              |
#> **************************************************|
#>  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-SV model              |
#>    Non-centred SV model is estimated              |
#> **************************************************|
#>  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] 0.1469206 0.006062622   0.1402002    0.1585284
#> 
#> $B$equation2
#>             mean       sd 5% quantile 95% quantile
#> B[2,1] -12.38527 0.740964   -13.62140    -11.48032
#> B[2,2]  40.20519 2.345169    37.32229     44.10876
#> 
#> $B$equation3
#>              mean       sd 5% quantile 95% quantile
#> B[3,1] -38.385481 2.258204   -41.51627   -34.953677
#> B[3,2]  -8.951953 2.550733   -12.66373    -5.077967
#> B[3,3]  62.253502 3.438587    56.30151    68.160968
#> 
#> 
#> $A
#> $A$equation1
#>                 mean         sd 5% quantile 95% quantile
#> lag1_var1  1.0244660 0.02325616   0.9914658   1.05905990
#> lag1_var2 -0.0827699 0.03335015  -0.1183212  -0.02799828
#> lag1_var3 -0.8711731 0.03207860  -0.9177089  -0.82579169
#> const     -0.4115923 0.22784055  -0.6370424  -0.01318342
#> 
#> $A$equation2
#>                   mean         sd 5% quantile 95% quantile
#> lag1_var1  0.003971754 0.01665495 -0.02132162   0.02358129
#> lag1_var2  0.933395512 0.01525154  0.91770628   0.95563454
#> lag1_var3 -0.257876638 0.02137591 -0.28337887  -0.22059363
#> const     -0.489972492 0.11781438 -0.65028828  -0.31168524
#> 
#> $A$equation3
#>                  mean         sd 5% quantile 95% quantile
#> lag1_var1  0.04414684 0.01507222  0.02407978   0.06506311
#> lag1_var2 -0.05738968 0.02249465 -0.08594839  -0.02250236
#> lag1_var3  0.38875111 0.01968031  0.36224605   0.41886992
#> const     -0.30141027 0.14722754 -0.46388005  -0.07310653
#> 
#> 
#> $hyper
#> $hyper$B
#>                            mean        sd 5% quantile 95% quantile
#> B[1,]_shrinkage       1203.8542 1107.1196    121.5518     2978.490
#> B[2,]_shrinkage        851.5032  365.3617    398.3216     1582.319
#> B[3,]_shrinkage       1426.3654 1039.9409    385.1111     3611.604
#> B[1,]_shrinkage_scale 8073.1299 4961.1526   1386.9332    15248.222
#> B[2,]_shrinkage_scale 7935.1783 4507.5929   1830.0583    17905.325
#> B[3,]_shrinkage_scale 9019.1146 5236.3729   1887.7240    17584.601
#> B_global_scale         795.6531  449.0971    152.2418     1220.058
#> 
#> $hyper$A
#>                            mean        sd 5% quantile 95% quantile
#> A[1,]_shrinkage       0.5235967 0.2227498   0.2666590    0.8226043
#> A[2,]_shrinkage       0.4392824 0.3029605   0.1967496    1.1998566
#> A[3,]_shrinkage       0.5858711 0.2622841   0.2392140    1.0114170
#> A[1,]_shrinkage_scale 5.5126318 1.8178165   3.5266149    7.6545847
#> A[2,]_shrinkage_scale 5.0813030 1.9164053   3.0974824    7.9525957
#> A[3,]_shrinkage_scale 6.4805349 3.3732470   3.5601451   13.8244833
#> A_global_scale        0.6678598 0.1653758   0.4423705    0.9422769
#> 
#>