Computes posterior draws of historical decompositions

Each of the draws from the posterior estimation of models from packages bsvars or bsvarSIGNs is transformed into a draw from the posterior distribution of the historical decompositions. IMPORTANT! The historical decompositions are interpreted correctly for covariance stationary data. Application to unit-root non-stationary data might result in non-interpretable outcomes.

Usage

# S3 method for class 'PosteriorBSVARSIGN'
compute_historical_decompositions(posterior, show_progress = TRUE)

Arguments

posterior: posterior estimation outcome - an object of class PosteriorBSVARSIGN obtained by running the estimate function.
show_progress: a logical value, if TRUE the estimation progress bar is visible

Value

An object of class PosteriorHD, that is, an NxNxTxS array with attribute PosteriorHD containing S draws of the historical decompositions.

References

Kilian, L., & Lütkepohl, H. (2017). Structural VAR Tools, Chapter 4, In: Structural vector autoregressive analysis. Cambridge University Press.

Author

Xiaolei Wang adamwang15@gmail.com and Tomasz Woźniak wozniak.tom@pm.me

Examples

# upload data
data(optimism)

# specify the model and set seed
set.seed(123)

# + no effect on productivity (zero restriction)
# + positive effect on stock prices (positive sign restriction) 
sign_irf       = matrix(c(0, 1, rep(NA, 23)), 5, 5)
specification  = specify_bsvarSIGN$new(optimism, sign_irf = sign_irf)

# estimate the model
posterior      = estimate(specification, 10)
#> **************************************************|
#>  bsvarSIGNs: Bayesian Structural VAR with sign,   |
#>              zero and narrative restrictions      |
#> **************************************************|
#>  Progress of simulation for 10 independent draws
#>  Press Esc to interrupt the computations
#> **************************************************|

# compute historical decompositions
hd            = compute_historical_decompositions(posterior)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Computing historical decomposition               |
#> **************************************************|
#>  This might take a little while :)                
#> **************************************************|

# workflow with the pipe |>
############################################################
set.seed(123)
optimism |>
  specify_bsvarSIGN$new(sign_irf = sign_irf) |> 
  estimate(S = 10) |> 
  compute_historical_decompositions() -> hd
#> **************************************************|
#>  bsvarSIGNs: Bayesian Structural VAR with sign,   |
#>              zero and narrative restrictions      |
#> **************************************************|
#>  Progress of simulation for 10 independent draws
#>  Press Esc to interrupt the computations
#> **************************************************|
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#>  Computing historical decomposition               |
#> **************************************************|
#>  This might take a little while :)                
#> **************************************************|