Provides posterior summary of heteroskedastic Structural VAR estimation
Source:R/summary.R
summary.PosteriorBSVARMSH.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 'PosteriorBSVARMSH'
summary(object, ...)Arguments
- object
an object of class PosteriorBSVARMSH obtained using the
estimate()function applied to heteroskedastic Bayesian Structural VAR model specification set by functionspecify_bsvar_msh$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_msh$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-stationaryMSH 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-stationaryMSH 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] 0.905559 0.04765416 0.8401119 0.9666544
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -18.24134 1.222942 -19.86753 -16.43178
#> B[2,2] 34.13613 2.281377 30.70614 37.28311
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -32.83921 2.123394 -36.018317 -30.040749
#> B[3,2] -16.08219 1.536692 -17.968571 -13.995490
#> B[3,3] 6.45492 0.339832 5.972701 6.920447
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.871138101 0.02127644 0.83022010 0.90086559
#> lag1_var2 -0.004261879 0.01196716 -0.02255421 0.01318017
#> lag1_var3 0.005727761 0.02777996 -0.02995239 0.05824378
#> const -0.107299499 0.11098347 -0.29827663 0.05619071
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.0627683560 0.01859482 -0.08523879 -0.03735508
#> lag1_var2 0.9652773755 0.01132283 0.95154202 0.98582857
#> lag1_var3 0.0004893731 0.02326335 -0.02802197 0.02901127
#> const -0.3437180278 0.09697503 -0.46767103 -0.22263456
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.48228780 0.1500522 -0.68800323 -0.25201524
#> lag1_var2 0.03938369 0.0404859 -0.02453474 0.09176835
#> lag1_var3 0.60277760 0.1911127 0.36560913 0.85261413
#> const -0.06805704 0.3642663 -0.66043277 0.53352302
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 62.73660 21.41002 28.30505 93.04222
#> B[2,]_shrinkage 181.95075 63.57409 109.34169 290.93041
#> B[3,]_shrinkage 207.00506 104.55333 81.61105 449.56415
#> B[1,]_shrinkage_scale 653.55290 225.23994 363.07370 1007.20692
#> B[2,]_shrinkage_scale 767.96933 198.97013 458.14684 1081.48506
#> B[3,]_shrinkage_scale 763.00944 192.87495 483.22029 1108.38586
#> B_global_scale 63.99689 13.22256 41.82352 81.24698
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.2450042 0.07598887 0.1419385 0.3724916
#> A[2,]_shrinkage 0.3617389 0.18307322 0.1664882 0.7328828
#> A[3,]_shrinkage 0.4007919 0.20903195 0.1756399 0.8086641
#> A[1,]_shrinkage_scale 3.4403430 1.37194243 1.7001660 5.1360618
#> A[2,]_shrinkage_scale 4.4887431 2.02099829 2.4046754 9.3567792
#> A[3,]_shrinkage_scale 4.6239420 2.19980208 2.2788978 7.6073994
#> A_global_scale 0.5076981 0.12931529 0.3340523 0.6944414
#>
#>
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar_msh$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-stationaryMSH 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-stationaryMSH 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] 0.905559 0.04765416 0.8401119 0.9666544
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -18.24134 1.222942 -19.86753 -16.43178
#> B[2,2] 34.13613 2.281377 30.70614 37.28311
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -32.83921 2.123394 -36.018317 -30.040749
#> B[3,2] -16.08219 1.536692 -17.968571 -13.995490
#> B[3,3] 6.45492 0.339832 5.972701 6.920447
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.871138101 0.02127644 0.83022010 0.90086559
#> lag1_var2 -0.004261879 0.01196716 -0.02255421 0.01318017
#> lag1_var3 0.005727761 0.02777996 -0.02995239 0.05824378
#> const -0.107299499 0.11098347 -0.29827663 0.05619071
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.0627683560 0.01859482 -0.08523879 -0.03735508
#> lag1_var2 0.9652773755 0.01132283 0.95154202 0.98582857
#> lag1_var3 0.0004893731 0.02326335 -0.02802197 0.02901127
#> const -0.3437180278 0.09697503 -0.46767103 -0.22263456
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.48228780 0.1500522 -0.68800323 -0.25201524
#> lag1_var2 0.03938369 0.0404859 -0.02453474 0.09176835
#> lag1_var3 0.60277760 0.1911127 0.36560913 0.85261413
#> const -0.06805704 0.3642663 -0.66043277 0.53352302
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 62.73660 21.41002 28.30505 93.04222
#> B[2,]_shrinkage 181.95075 63.57409 109.34169 290.93041
#> B[3,]_shrinkage 207.00506 104.55333 81.61105 449.56415
#> B[1,]_shrinkage_scale 653.55290 225.23994 363.07370 1007.20692
#> B[2,]_shrinkage_scale 767.96933 198.97013 458.14684 1081.48506
#> B[3,]_shrinkage_scale 763.00944 192.87495 483.22029 1108.38586
#> B_global_scale 63.99689 13.22256 41.82352 81.24698
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.2450042 0.07598887 0.1419385 0.3724916
#> A[2,]_shrinkage 0.3617389 0.18307322 0.1664882 0.7328828
#> A[3,]_shrinkage 0.4007919 0.20903195 0.1756399 0.8086641
#> A[1,]_shrinkage_scale 3.4403430 1.37194243 1.7001660 5.1360618
#> A[2,]_shrinkage_scale 4.4887431 2.02099829 2.4046754 9.3567792
#> A[3,]_shrinkage_scale 4.6239420 2.19980208 2.2788978 7.6073994
#> A_global_scale 0.5076981 0.12931529 0.3340523 0.6944414
#>
#>