Provides posterior summary of forecast error variance decompositions

Provides posterior means of the forecast error variance decompositions of each variable at all horizons.

Usage

# S3 method for class 'PosteriorFEVDPANEL'
summary(object, which_c, ...)

Arguments

object: an object of class PosteriorFEVDPANEL obtained using the compute_variance_decompositions() function containing draws from the posterior distribution of the forecast error variance decompositions.
which_c: a positive integer or a character string specifying the country for which the forecast should be plotted.
...: additional arguments affecting the summary produced.

Value

A list reporting the posterior mean of the forecast error variance decompositions of each variable at all horizons.

Author

Tomasz Woźniak wozniak.tom@pm.me

Examples

# specify the model and set seed
specification  = specify_bvarPANEL$new(ilo_dynamic_panel[1:5], p = 1)

# run the burn-in
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
#> **************************************************|

# estimate the model
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
#> **************************************************|

# compute forecast error variance decomposition 4 years ahead
fevd           = compute_variance_decompositions(posterior, horizon = 4)
summary(fevd, which_c = "ARG")
#>  **************************************************|
#>  bsvars: Bayesian Structural Vector Autoregressions|
#>  **************************************************|
#>    Posterior means of forecast error               |
#>      variance decompositions                       |
#>  **************************************************|
#> $variable1
#>      shock1    shock2   shock3   shock4
#> 0 100.00000  0.000000  0.00000  0.00000
#> 1  52.47700  8.667721 22.42000 16.43528
#> 2  42.60683 10.471100 27.08710 19.83497
#> 3  38.19962 11.279071 29.17345 21.34786
#> 4  35.67999 11.742537 30.36712 22.21035
#> 
#> $variable2
#>     shock1     shock2   shock3   shock4
#> 0 99.68962  0.3103785  0.00000  0.00000
#> 1 58.50565  6.9938721 19.82316 14.67732
#> 2 41.52526  9.8030863 27.91465 20.75701
#> 3 38.90548 10.2634199 29.08355 21.74755
#> 4 38.61655 10.4703426 29.14046 21.77265
#> 
#> $variable3
#>      shock1     shock2    shock3   shock4
#> 0 97.763863  0.5462981  1.689839  0.00000
#> 1  2.658713 17.6973106 46.261517 33.38246
#> 2  5.041860 17.2334653 45.084003 32.64067
#> 3  8.047810 16.6750020 43.633958 31.64323
#> 4 10.720598 16.1868823 42.353480 30.73904
#> 
#> $variable4
#>      shock1   shock2   shock3   shock4
#> 0 0.9971876 16.70805 45.33350 36.96126
#> 1 1.6850489 17.99715 46.34403 33.97377
#> 2 3.1276955 17.86940 45.77805 33.22485
#> 3 4.6597584 17.63606 45.08750 32.61668
#> 4 6.1210601 17.38820 44.40964 32.08110
#>