The class PosteriorBVARs contains posterior output and the specification
including the last MCMC draw for the Bayesian Panel VAR model.
Note that due to the thinning of the MCMC output the starting value in element
last_draw might not be equal to the last draw provided in
element posterior.
Public fields
last_drawan object of class BVARs with the last draw of the current MCMC run as the starting value to be passed to the continuation of the MCMC estimation using
estimate().posteriora list containing Bayesian estimation output.
Methods
Method new()
Create a new posterior output PosteriorBVARs.
Usage
specify_posterior_bvars$new(specification_bvarPANEL, posterior_bvarPANEL)Method get_posterior()
Returns a list containing Bayesian estimation output.
Examples
specification = specify_bvars$new(
data = ilo_dynamic_panel[1:5]
)
posterior = estimate(specification, 5)
posterior$get_posterior()
Method get_last_draw()
Returns an object of class BVARs with the last draw of the current
MCMC run as the starting value to be passed to the continuation of the
MCMC estimation using estimate().
Examples
specification = specify_bvars$new(
data = ilo_dynamic_panel[1:5]
)
burn_in = estimate(specification, 5)
posterior = estimate(burn_in, 5)
Examples
specification = specify_bvars$new(
data = ilo_dynamic_panel[1:5]
)
posterior = estimate(specification, 5)
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
class(posterior)
#> [1] "PosteriorBVARs" "R6"
## ------------------------------------------------
## Method `specify_posterior_bvars$get_posterior`
## ------------------------------------------------
specification = specify_bvars$new(
data = ilo_dynamic_panel[1:5]
)
posterior = estimate(specification, 5)
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
posterior$get_posterior()
#> $A_c_cpp
#> [,1]
#> [1,] numeric,100
#> [2,] numeric,100
#> [3,] numeric,100
#> [4,] numeric,100
#> [5,] numeric,100
#>
#> $Sigma_c_cpp
#> [,1]
#> [1,] numeric,80
#> [2,] numeric,80
#> [3,] numeric,80
#> [4,] numeric,80
#> [5,] numeric,80
#>
#> $nu
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 5.1 5.100000 5.100000 5.100000 5.100000
#> [2,] 5.1 5.100000 5.100000 5.079436 5.079436
#> [3,] 5.1 5.100000 5.100000 5.100000 5.100000
#> [4,] 5.1 5.001131 5.001131 5.001131 5.001131
#> [5,] 5.1 5.034205 5.034205 5.034205 5.034205
#>
#> $m
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.8495975 0.8682247 0.7639118 1.0095890 1.2004548
#> [2,] 0.1281337 0.8407688 0.8563481 1.1168946 0.9626927
#> [3,] 0.3578316 0.4926332 0.1445306 0.4946215 1.0593604
#> [4,] 1.1938285 0.9798738 1.0490564 0.8187018 0.7589881
#> [5,] 0.1616546 0.9021193 0.7436686 0.4730325 0.7180821
#>
#> $w
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.25662651 0.06802740 0.14679021 0.07406320 0.03903621
#> [2,] 0.04219498 0.07144417 0.09336787 0.07929608 0.11391884
#> [3,] 0.04002768 0.08354273 0.14773708 0.08511706 0.19060316
#> [4,] 0.15471867 0.18480453 0.10876687 0.19204352 0.11311116
#> [5,] 0.04346377 0.10782900 0.09161363 0.12935369 0.08515449
#>
#> $s
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 139.24830 46.24218 53.47676 104.17525 59.59214
#> [2,] 835.77905 25.53658 139.90893 30.38242 102.70131
#> [3,] 81.46635 49.90824 55.86722 55.65314 37.30102
#> [4,] 24.60240 148.44889 46.33455 64.65289 59.86091
#> [5,] 222.69913 36.67961 51.61319 54.19050 76.18713
#>
#> $scale
#> [,1]
#> [1,] 0
#> [2,] 0
#> [3,] 0
#> [4,] 0
#> [5,] 0
#>
#> $Y
#> [,1]
#> [1,] numeric,700
#> [2,] numeric,700
#> [3,] numeric,700
#> [4,] numeric,700
#> [5,] numeric,700
#>
#> $Sigma_c
#> , , 1, 1
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 5.08689299 -0.8407560 0.1542650 -0.02152868
#> [2,] -0.84075601 5.1709941 -1.7802468 -0.66586435
#> [3,] 0.15426502 -1.7802468 5.7620224 0.86291840
#> [4,] -0.02152868 -0.6658644 0.8629184 3.86987995
#>
#> , , 2, 1
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 28.682658 -1.558275 -7.582660 4.756147
#> [2,] -1.558275 24.740815 -3.522011 -1.958386
#> [3,] -7.582660 -3.522011 22.377108 -1.492157
#> [4,] 4.756147 -1.958386 -1.492157 28.062153
#>
#> , , 3, 1
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 3.3916532 -0.2935344 1.601153 0.5913579
#> [2,] -0.2935344 12.7913756 -4.414127 2.3148893
#> [3,] 1.6011528 -4.4141270 7.360438 2.0299907
#> [4,] 0.5913579 2.3148893 2.029991 5.0889813
#>
#> , , 4, 1
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.97048860 -0.1641253 0.4574059 -0.08411293
#> [2,] -0.16412529 0.9107985 -0.3210008 -0.21223414
#> [3,] 0.45740595 -0.3210008 1.0980628 0.38666941
#> [4,] -0.08411293 -0.2122341 0.3866694 0.87630718
#>
#> , , 5, 1
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 6.52150823 -0.01312817 0.1188668 0.8213156
#> [2,] -0.01312817 9.33880976 -2.7360083 0.9247816
#> [3,] 0.11886675 -2.73600834 8.5500888 1.9375843
#> [4,] 0.82131556 0.92478161 1.9375843 6.8631800
#>
#> , , 1, 2
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.035306e+00 8.420698e-05 0.1412672 0.0009551398
#> [2,] 8.420698e-05 2.655727e+00 -0.0941932 -1.3491949122
#> [3,] 1.412672e-01 -9.419320e-02 1.4121781 0.5425639161
#> [4,] 9.551398e-04 -1.349195e+00 0.5425639 3.7512564444
#>
#> , , 2, 2
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.8377160 0.2852245 -0.3320211 -0.3593279
#> [2,] 0.2852245 1.0760848 -0.4301104 -0.4964190
#> [3,] -0.3320211 -0.4301104 1.3253317 1.0104256
#> [4,] -0.3593279 -0.4964190 1.0104256 2.1858945
#>
#> , , 3, 2
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 2.062551 -1.682078 3.035443 1.447656
#> [2,] -1.682078 13.031845 -10.222675 -2.303505
#> [3,] 3.035443 -10.222675 14.704625 5.735839
#> [4,] 1.447656 -2.303505 5.735839 5.242661
#>
#> , , 4, 2
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 4.1209483 -0.9201750 1.06865503 -0.30147160
#> [2,] -0.9201750 4.5081095 0.23260983 0.32608923
#> [3,] 1.0686550 0.2326098 4.66956973 0.02900489
#> [4,] -0.3014716 0.3260892 0.02900489 4.21159418
#>
#> , , 5, 2
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.0294702 0.2104471 -0.4844714 0.2135988
#> [2,] 0.2104471 4.0487092 -1.8741400 -0.2995485
#> [3,] -0.4844714 -1.8741400 3.9818668 1.1557146
#> [4,] 0.2135988 -0.2995485 1.1557146 1.6075915
#>
#> , , 1, 3
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.24969276 -0.04342977 0.4431811 -0.1152837
#> [2,] -0.04342977 1.44140077 0.2283571 0.0843089
#> [3,] 0.44318112 0.22835713 2.2115798 0.1558194
#> [4,] -0.11528373 0.08430890 0.1558194 2.0303686
#>
#> , , 2, 3
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 6.0342399 1.6483193 -0.2352416 -0.5786638
#> [2,] 1.6483193 6.2265907 0.3613647 -2.0352641
#> [3,] -0.2352416 0.3613647 4.7960080 -0.8752841
#> [4,] -0.5786638 -2.0352641 -0.8752841 5.2458079
#>
#> , , 3, 3
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 3.4100174 0.8159719 2.312237 2.470904
#> [2,] 0.8159719 10.8239065 -5.493427 1.970670
#> [3,] 2.3122370 -5.4934275 10.565511 5.262841
#> [4,] 2.4709036 1.9706703 5.262841 9.806671
#>
#> , , 4, 3
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.8382500 -0.4166732 0.5164718 0.1781285
#> [2,] -0.4166732 2.7157383 -0.8537897 -0.1695782
#> [3,] 0.5164718 -0.8537897 1.7622806 0.1585451
#> [4,] 0.1781285 -0.1695782 0.1585451 1.6801872
#>
#> , , 5, 3
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.39332472 -0.1688535 0.06867563 -0.2647434
#> [2,] -0.16885350 4.6882566 -2.29062604 0.1066842
#> [3,] 0.06867563 -2.2906260 4.67117152 0.7500667
#> [4,] -0.26474341 0.1066842 0.75006669 1.9863207
#>
#> , , 1, 4
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 2.75348881 0.4053450 0.06528303 -0.21405532
#> [2,] 0.40534501 3.6117052 -1.05236595 -0.41535269
#> [3,] 0.06528303 -1.0523659 3.14765580 0.04746048
#> [4,] -0.21405532 -0.4153527 0.04746048 2.56683287
#>
#> , , 2, 4
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.9638329 0.2159094 -0.0752556 -0.2763092
#> [2,] 0.2159094 1.3227590 -0.5105467 -0.4617324
#> [3,] -0.0752556 -0.5105467 1.5785412 0.8032185
#> [4,] -0.2763092 -0.4617324 0.8032185 1.1254684
#>
#> , , 3, 4
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 3.6451138 0.9747772 1.772335 2.295122
#> [2,] 0.9747772 10.0821148 -5.503130 1.840813
#> [3,] 1.7723354 -5.5031304 7.925546 2.402241
#> [4,] 2.2951224 1.8408128 2.402241 5.989143
#>
#> , , 4, 4
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 2.32532447 -0.03780023 -0.1145324 0.3746790
#> [2,] -0.03780023 3.17653175 1.1784123 -0.4131481
#> [3,] -0.11453240 1.17841227 3.6209845 0.5053245
#> [4,] 0.37467898 -0.41314808 0.5053245 2.2837157
#>
#> , , 5, 4
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.4537982 0.2921388 0.1033012 -0.3196783
#> [2,] 0.2921388 8.2879409 -5.6078485 -1.2667623
#> [3,] 0.1033012 -5.6078485 6.5336294 1.6106308
#> [4,] -0.3196783 -1.2667623 1.6106308 2.0213459
#>
#> , , 1, 5
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.48430587 0.50605516 0.05459283 0.0282480
#> [2,] 0.50605516 2.39039889 0.06808628 0.2891137
#> [3,] 0.05459283 0.06808628 1.56722336 0.2916156
#> [4,] 0.02824800 0.28911369 0.29161556 1.9183029
#>
#> , , 2, 5
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.8993473 0.3675867 -0.5066207 -0.1112436
#> [2,] 0.3675867 3.5913713 -0.2206541 -0.4297706
#> [3,] -0.5066207 -0.2206541 3.0036206 0.3217448
#> [4,] -0.1112436 -0.4297706 0.3217448 2.9675964
#>
#> , , 3, 5
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.2516785 -0.2040644 0.3636175 0.6970637
#> [2,] -0.2040644 10.1279506 -6.0518260 -1.9751432
#> [3,] 0.3636175 -6.0518260 6.3963002 3.3400882
#> [4,] 0.6970637 -1.9751432 3.3400882 4.7336884
#>
#> , , 4, 5
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 2.3054752 -0.6928484 0.1454327 -0.1811175
#> [2,] -0.6928484 2.0628241 -0.4185093 -0.5076050
#> [3,] 0.1454327 -0.4185093 2.3415650 0.8098505
#> [4,] -0.1811175 -0.5076050 0.8098505 3.0104384
#>
#> , , 5, 5
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 2.1940590 -0.1763117 0.1412932 -0.2058467
#> [2,] -0.1763117 3.9184714 -1.5883588 -1.0523772
#> [3,] 0.1412932 -1.5883588 4.8099380 1.1361850
#> [4,] -0.2058467 -1.0523772 1.1361850 2.8035049
#>
#>
#> $A_c
#> , , 1, 1
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.5987244 -0.04005507 -0.12735484 0.1657819
#> [2,] -0.1944307 0.85980546 -0.02540139 0.3659525
#> [3,] 0.6322887 -1.18817527 2.03386249 1.2805013
#> [4,] -0.7979614 1.23354733 -0.92484499 -0.3350355
#> [5,] -2.1512367 -4.68069834 2.78347780 0.4707550
#>
#> , , 2, 1
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.5444390 -0.4642077 0.2935320 0.2736167
#> [2,] -0.6031712 1.0351124 0.4721838 0.6606052
#> [3,] 0.7211623 0.4374799 1.0703864 1.6144261
#> [4,] -0.3383689 -0.1872511 -0.2826571 -0.5805243
#> [5,] 0.5286287 -0.5325410 2.3427089 -0.4853125
#>
#> , , 3, 1
#>
#> [,1] [,2] [,3] [,4]
#> [1,] -0.06064851 -0.74717766 -0.3283988 -0.9336209
#> [2,] 0.53869680 0.70896634 0.3636242 0.4033849
#> [3,] 1.02513166 0.37915280 1.1821431 0.8199009
#> [4,] -0.61917974 0.05066187 -0.1524461 0.5297212
#> [5,] 1.34802397 -0.61639296 2.0788848 2.1182279
#>
#> , , 4, 1
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.1571263 0.2826602 -0.77583810 -0.1348249
#> [2,] 0.1330714 1.1251550 0.02923913 -0.5829925
#> [3,] -0.2263628 0.2949786 0.86072011 -0.4437115
#> [4,] 0.4922452 -0.3867083 0.41527179 1.5247751
#> [5,] 0.9578444 -0.3573128 -1.18167944 -1.7934360
#>
#> , , 5, 1
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.551719273 -1.1173710 0.96900903 -0.8311049
#> [2,] 0.042372450 0.7533959 -0.09189942 0.4812597
#> [3,] 0.247938668 -0.1133430 0.72863772 0.9226531
#> [4,] -0.006630554 0.6318003 -0.11391157 0.4689519
#> [5,] -1.572972499 0.4366482 -2.49560529 -0.3729583
#>
#> , , 1, 2
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.79778468 0.2579027 -0.2432952 -0.49874301
#> [2,] 0.19271032 0.5220833 0.2637756 0.64376014
#> [3,] 0.20732079 -0.8167066 1.5557109 1.09346142
#> [4,] -0.11951381 0.7302408 -0.4513936 0.11769704
#> [5,] 0.09428293 -0.6082403 0.1162190 0.08173841
#>
#> , , 2, 2
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.56366370 -0.267854578 0.44513363 0.5399762
#> [2,] -0.47950933 0.733147635 0.33813951 0.5528164
#> [3,] -0.02741615 0.187630514 0.74237960 -0.1674293
#> [4,] 0.25127623 -0.003524348 0.01105889 0.8463266
#> [5,] 1.08674625 -0.785254404 -0.71478762 0.1472758
#>
#> , , 3, 2
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.3353306 -0.69679308 0.1220455 -0.3981005
#> [2,] 1.0292094 0.57799151 0.8689654 1.0343531
#> [3,] 1.6280436 0.38520720 1.8182462 1.7362257
#> [4,] -1.4000940 0.09473132 -1.0126617 -0.6231816
#> [5,] 0.8759372 -2.26236800 2.4187302 2.4981765
#>
#> , , 4, 2
#>
#> [,1] [,2] [,3] [,4]
#> [1,] -0.02268471 -0.02939119 -0.6411944 -0.10869883
#> [2,] -0.71382680 0.91536413 0.6775033 0.19383638
#> [3,] -0.51039382 0.61472467 1.0692731 0.02532967
#> [4,] 0.89046189 -0.61490471 0.1694432 0.95984930
#> [5,] -1.50124460 2.37727565 -2.3607712 3.92393339
#>
#> , , 5, 2
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.114037401 -1.00681288 1.16910198 0.5631610
#> [2,] -0.001706124 0.64348532 -0.04541168 0.3488063
#> [3,] 0.004711892 -0.05644135 0.53546099 0.4885293
#> [4,] -0.083420060 0.55106238 -0.09504551 0.2349790
#> [5,] 1.764728787 1.00553509 -0.77811226 1.0198545
#>
#> , , 1, 3
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.0497309 -0.1849985 0.15550016 0.13984920
#> [2,] -0.2015697 1.0885620 0.01072221 -0.01754677
#> [3,] -0.3350773 -0.3916415 1.56040941 0.45207363
#> [4,] 0.3106461 0.4029647 -0.61340063 0.48466298
#> [5,] 0.5684009 1.7041112 0.64623034 1.02886277
#>
#> , , 2, 3
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.69384418 -0.5453756 0.74319934 0.5383564
#> [2,] -0.26497494 0.9752232 0.03240591 0.2790843
#> [3,] 0.32354198 0.4346008 0.91285926 -0.1749873
#> [4,] -0.05357033 -0.1793323 -0.17539657 0.8913243
#> [5,] -4.82275632 -0.2182183 1.00099981 1.7368524
#>
#> , , 3, 3
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.1477270 -0.89392447 0.05828931 -0.8093019
#> [2,] 0.8814261 0.74189306 0.77916922 0.9643891
#> [3,] 1.4198220 0.58048136 1.50520080 1.6973555
#> [4,] -1.1235394 -0.09359393 -0.69654087 -0.3825799
#> [5,] 1.5878577 1.65847035 1.27129160 -0.3536352
#>
#> , , 4, 3
#>
#> [,1] [,2] [,3] [,4]
#> [1,] -0.07165513 0.09608699 -0.3840078 -0.31337155
#> [2,] 0.47368776 1.78876034 -0.2316028 0.08787527
#> [3,] 0.31074776 0.98207351 0.1476246 0.22847407
#> [4,] 0.04142907 -1.03679085 0.9571781 0.91688547
#> [5,] 1.12566725 1.50674956 0.9573976 -3.09309392
#>
#> , , 5, 3
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.8219340 -0.4639069 0.97316605 0.7314333
#> [2,] 0.1220811 0.7313453 -0.20770156 0.3428325
#> [3,] 0.3144079 -0.1942680 0.54306800 0.3686094
#> [4,] -0.1945537 0.3942140 0.06019911 0.2784768
#> [5,] -1.6744021 2.4460652 -3.23066531 0.4153140
#>
#> , , 1, 4
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.67203303 0.01555798 -0.2608835 0.07738636
#> [2,] 0.30116228 0.99721022 0.1605589 -0.46342805
#> [3,] 0.16884916 -0.05556979 1.1063217 -0.43987166
#> [4,] -0.06636707 0.04972309 -0.0212792 1.43088044
#> [5,] 0.66716648 0.05328869 -0.1797359 0.63331595
#>
#> , , 2, 4
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.6817680 -0.23444557 -0.03044250 0.55703204
#> [2,] -0.2992227 0.65150205 0.62060274 0.54003443
#> [3,] -0.1020009 -0.20891390 0.75319793 0.01386569
#> [4,] 0.2459655 0.32207532 0.08704604 0.69716920
#> [5,] 0.4080080 -0.03882738 -0.17330569 -0.11414430
#>
#> , , 3, 4
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.2037624 -0.5290492 -0.2765778 -0.8635522
#> [2,] 0.6834715 0.9069822 0.4426620 0.8094196
#> [3,] 1.0087202 0.7918026 1.3566178 1.6054284
#> [4,] -0.8005091 -0.3966959 -0.3873729 -0.2786149
#> [5,] 4.2886150 -3.3232564 4.9020831 2.3297191
#>
#> , , 4, 4
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.23229976 0.00283252 -0.6978078 0.07493173
#> [2,] 0.71221830 1.18570372 -0.2391063 -0.38956078
#> [3,] 0.03243621 0.72913659 0.5923565 -0.60442166
#> [4,] 0.18586475 -0.71067694 0.6178181 1.54238276
#> [5,] 2.13212514 -0.44720795 2.3422063 2.85999953
#>
#> , , 5, 4
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.73206601 -0.02692687 0.90121672 0.57545490
#> [2,] 0.06164654 0.88706999 -0.01054811 -0.07335276
#> [3,] 0.21349910 0.17394404 0.54942578 -0.13341620
#> [4,] -0.09673508 -0.01617469 -0.04567415 0.91365152
#> [5,] 0.75853556 -5.54461485 2.47285544 -2.16222793
#>
#> , , 1, 5
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.259220323 0.05659643 0.06196316 0.1641865
#> [2,] -0.171880402 1.17933532 -0.16288213 -0.2851683
#> [3,] 0.004672815 0.33571823 0.93954170 -0.2616378
#> [4,] -0.078451706 -0.35357282 0.05528620 1.1950393
#> [5,] -0.580945834 -0.31707525 -0.06627702 0.1786312
#>
#> , , 2, 5
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 1.0467520 -0.6038637 -0.4248320 0.30382526
#> [2,] -0.4397968 0.4590555 1.1712751 0.26146732
#> [3,] -0.3377222 -0.1275229 1.5221805 0.08648848
#> [4,] 0.3022059 0.4193114 -0.5329299 0.81385624
#> [5,] 4.5437159 0.0471289 -1.0201441 -3.81445693
#>
#> , , 3, 5
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.3226345 -0.6121589 0.02769584 -0.7616524
#> [2,] 0.5801279 0.7890130 0.69948729 0.9647592
#> [3,] 1.0215554 0.5400422 1.73661688 1.7110276
#> [4,] -0.8070887 -0.1508068 -0.78598666 -0.3980075
#> [5,] 3.3089831 -0.6089832 -1.86149130 -0.1995715
#>
#> , , 4, 5
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.05945378 0.1677705 0.3096784 0.2132793
#> [2,] -0.04497754 0.6582011 -0.3949692 -0.5836340
#> [3,] 0.27344407 0.2756603 0.6913566 -0.1836050
#> [4,] 0.05454775 -0.3377458 0.1914266 1.1388575
#> [5,] 0.09717716 1.7552741 0.9062654 -0.7334278
#>
#> , , 5, 5
#>
#> [,1] [,2] [,3] [,4]
#> [1,] 0.7186698 -0.17771405 1.0893769 0.1529921
#> [2,] 0.2617184 1.08045157 -0.4813574 -0.7031931
#> [3,] 0.4096646 0.46145690 -0.1141647 -0.9993535
#> [4,] -0.2787951 -0.34027930 0.5945817 1.9322166
#> [5,] -0.4876987 0.06958336 0.6972269 1.5093343
#>
#>
## ------------------------------------------------
## Method `specify_posterior_bvars$get_last_draw`
## ------------------------------------------------
specification = specify_bvars$new(
data = ilo_dynamic_panel[1:5]
)
burn_in = estimate(specification, 5)
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
posterior = estimate(burn_in, 5)
#> **************************************************|
#> bpvars: Forecasting with Bayesian Panel VARs |
#> **************************************************|
#> Progress of the MCMC simulation for 5 draws
#> Every draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|