Computes posterior draws of the forecast error variance decomposition

Each of the draws from the posterior estimation of the model is transformed into a draw from the posterior distribution of the forecast error variance decomposition.

Usage

# S3 method for class 'PosteriorBSVARSIGN'
compute_variance_decompositions(posterior, horizon)

Arguments

posterior: posterior estimation outcome - an object of class PosteriorBSVARSIGN obtained by running the estimate function.
horizon: a positive integer number denoting the forecast horizon for the impulse responses computations.

Value

An object of class PosteriorFEVD, that is, an NxNx(horizon+1)xS array with attribute PosteriorFEVD containing S draws of the forecast error variance decomposition.

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 forecast error variance decomposition 2 years ahead
fevd           = compute_variance_decompositions(posterior, horizon = 8)

# workflow with the pipe |>
############################################################
set.seed(123)
optimism |>
  specify_bsvarSIGN$new(sign_irf = sign_irf) |> 
  estimate(S = 10) |> 
  compute_variance_decompositions(horizon = 8) -> fevd
#> **************************************************|
#>  bsvarSIGNs: Bayesian Structural VAR with sign,   |
#>              zero and narrative restrictions      |
#> **************************************************|
#>  Progress of simulation for 10 independent draws
#>  Press Esc to interrupt the computations
#> **************************************************|