Provides posterior summary of heteroskedastic Structural VAR estimation
Source:R/summary.R
summary.PosteriorBSVAREXH.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 'PosteriorBSVAREXH'
summary(object, ...)Arguments
- object
an object of class PosteriorBSVAREXH obtained using the
estimate()function applied to heteroskedastic Bayesian Structural VAR model specification set by functionspecify_bsvar_exh$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
# specify the model and set seed
spec = specify_bsvar_exh$new(us_fiscal_lsuw)
#> The identification is set to the default option of lower-triangular structural matrix.
# run the burn-in
burn = estimate(spec, 10)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-exH 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
post = estimate(burn, 10)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-exH model |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
summary(post)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Posterior summary of the parameters |
#> **************************************************|
#> $B
#> $B$equation1
#> mean sd 5% quantile 95% quantile
#> B[1,1] 0.1950818 0.007386825 0.1861644 0.206435
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -27.92516 1.138433 -29.52635 -26.51888
#> B[2,2] 24.54653 1.005829 23.31432 25.96066
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -24.92590 2.945665 -29.80607 -21.33459
#> B[3,2] -26.66552 2.415342 -29.85635 -23.23412
#> B[3,3] 49.21074 1.529060 46.85936 51.25532
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 1.122728008 0.03267780 1.0866569720 1.16400814
#> lag1_var2 0.007228053 0.01573271 -0.0136945784 0.02885882
#> lag1_var3 -0.811008763 0.04318803 -0.8636640218 -0.76395300
#> const 0.191010374 0.12888434 0.0003571099 0.35473050
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.1963453 0.04035519 0.1394641 0.242059862
#> lag1_var2 0.9626336 0.02064907 0.9287501 0.986814968
#> lag1_var3 -0.9841728 0.05428202 -1.0405745 -0.907727042
#> const -0.1673422 0.15608413 -0.4360478 -0.007150803
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.207136501 0.03778811 0.16783406 0.25414577
#> lag1_var2 0.002046394 0.01578519 -0.01761875 0.02272994
#> lag1_var3 0.007824502 0.05055534 -0.05234482 0.06176869
#> const 0.194923949 0.12282054 0.02702078 0.33972709
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 77.45673 35.86833 45.06077 138.55894
#> B[2,]_shrinkage 201.55735 79.17090 85.86337 294.74631
#> B[3,]_shrinkage 408.23187 164.55942 230.30902 684.36444
#> B[1,]_shrinkage_scale 648.24336 249.92393 264.43779 944.44491
#> B[2,]_shrinkage_scale 720.72131 330.94139 333.62969 1163.16506
#> B[3,]_shrinkage_scale 842.88862 357.15778 427.11752 1382.55387
#> B_global_scale 61.63043 20.77670 33.55365 89.28196
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.7715838 0.35323743 0.4187094 1.3127986
#> A[2,]_shrinkage 0.5150829 0.33849105 0.1987464 1.0993331
#> A[3,]_shrinkage 0.5008191 0.17353390 0.3083176 0.7833503
#> A[1,]_shrinkage_scale 6.2254699 2.36838328 3.3431848 9.5206337
#> A[2,]_shrinkage_scale 5.2408737 2.11328721 2.6976303 8.0964944
#> A[3,]_shrinkage_scale 5.6918404 1.71810008 4.6008548 8.8111871
#> A_global_scale 0.6157048 0.09386147 0.4665042 0.7214287
#>
#>
# workflow with the pipe |>
############################################################
us_fiscal_lsuw |>
specify_bsvar_exh$new() |>
estimate(S = 10) |>
estimate(S = 10) |>
summary()
#> The identification is set to the default option of lower-triangular structural matrix.
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR-exH 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-exH 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|
#> **************************************************|
#> Posterior summary of the parameters |
#> **************************************************|
#> $B
#> $B$equation1
#> mean sd 5% quantile 95% quantile
#> B[1,1] 0.36 0.007948277 0.3517644 0.3723926
#>
#> $B$equation2
#> mean sd 5% quantile 95% quantile
#> B[2,1] -30.4201 0.7000894 -31.17961 -29.29732
#> B[2,2] 18.3022 0.4179408 17.63622 18.77111
#>
#> $B$equation3
#> mean sd 5% quantile 95% quantile
#> B[3,1] -19.62228 2.132645 -22.85493 -17.35016
#> B[3,2] -33.23079 1.540400 -35.19476 -30.99765
#> B[3,3] 32.61794 1.156033 31.00875 34.22015
#>
#>
#> $A
#> $A$equation1
#> mean sd 5% quantile 95% quantile
#> lag1_var1 0.878519577 0.04666489 0.82848062 0.95015567
#> lag1_var2 -0.005927987 0.01875567 -0.03092058 0.01825931
#> lag1_var3 -0.205777635 0.05544759 -0.28980097 -0.14948489
#> const 0.026307715 0.14656928 -0.16838531 0.23354672
#>
#> $A$equation2
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.08413602 0.03863600 -0.1359218 -0.03364269
#> lag1_var2 0.95304555 0.02822532 0.9118699 0.98169522
#> lag1_var3 -0.48499150 0.04202349 -0.5305355 -0.42979233
#> const -0.29631634 0.19166524 -0.5762592 -0.08488350
#>
#> $A$equation3
#> mean sd 5% quantile 95% quantile
#> lag1_var1 -0.10936987 0.06027696 -0.17922786 -0.02405619
#> lag1_var2 -0.00480963 0.03321864 -0.05405314 0.02703940
#> lag1_var3 0.31420388 0.06868683 0.21491706 0.39609342
#> const 0.13191283 0.23680995 -0.20055004 0.37672107
#>
#>
#> $hyper
#> $hyper$B
#> mean sd 5% quantile 95% quantile
#> B[1,]_shrinkage 111.88880 76.84561 28.26971 229.1695
#> B[2,]_shrinkage 134.67598 51.44436 52.25262 201.2661
#> B[3,]_shrinkage 340.80128 107.84168 215.18583 507.4172
#> B[1,]_shrinkage_scale 810.09355 375.69016 418.99333 1357.3376
#> B[2,]_shrinkage_scale 769.07024 322.49488 357.88993 1226.8057
#> B[3,]_shrinkage_scale 1075.93905 596.73971 438.00761 1932.8645
#> B_global_scale 79.80372 41.82807 40.38212 141.0267
#>
#> $hyper$A
#> mean sd 5% quantile 95% quantile
#> A[1,]_shrinkage 0.6361864 0.4442558 0.1958202 1.3210973
#> A[2,]_shrinkage 0.3545144 0.1823720 0.2009763 0.6539642
#> A[3,]_shrinkage 0.5218119 0.3626142 0.1722698 1.0772999
#> A[1,]_shrinkage_scale 6.6705299 2.4607924 3.6161782 10.0247345
#> A[2,]_shrinkage_scale 5.0843742 2.5081332 2.4938621 8.8303147
#> A[3,]_shrinkage_scale 5.6335297 1.5981276 3.5397016 7.8462150
#> A_global_scale 0.6982590 0.1977076 0.4990999 1.0052183
#>
#>