Provides posterior summary of historical decompositions

Provides posterior means of the historical decompositions variable by variable.

Usage

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

Arguments

object

an object of class PosteriorHD obtained using the compute_historical_decompositions() function containing posterior draws of historical decompositions.

...

additional arguments affecting the summary produced.

Value

A list reporting the posterior means of historical decompositions for each of the variables.

See also

compute_historical_decompositions

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$new(diff(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 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 model                 |
#> **************************************************|
#>  Progress of the MCMC simulation for 20 draws
#>     Every draw is saved via MCMC thinning
#>  Press Esc to interrupt the computations
#> **************************************************|

# compute historical decompositions
hds            = compute_historical_decompositions(posterior)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Computing historical decomposition               |
#> **************************************************|
#>  This might take a little while :)                
#> **************************************************|
hds_summary    = summary(hds)
#>  **************************************************|
#>  bsvars: Bayesian Structural Vector Autoregressions|
#>  **************************************************|
#>    Posterior means of historical decompositions    |
#>  **************************************************|

# workflow with the pipe |>
############################################################
set.seed(123)
diff(us_fiscal_lsuw) |>
  specify_bsvar$new() |>
  estimate(S = 10) |> 
  estimate(S = 20) |> 
  compute_historical_decompositions() |>
  summary() -> hds_summary
#> The identification is set to the default option of lower-triangular structural matrix.
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Gibbs sampler for the SVAR 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 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|
#> **************************************************|
#>  Computing historical decomposition               |
#> **************************************************|
#>  This might take a little while :)                
#> **************************************************|
#>  **************************************************|
#>  bsvars: Bayesian Structural Vector Autoregressions|
#>  **************************************************|
#>    Posterior means of historical decompositions    |
#>  **************************************************|