Computes posterior draws of historical decompositions
Source:R/compute_historical_decompositions.R
compute_historical_decompositions.Rd
Each of the draws from the posterior estimation of a model is transformed into a draw from the posterior distribution of the historical decompositions.
Arguments
- posterior
posterior estimation outcome - an object of either of the classes: PosteriorBSVAR, PosteriorBSVARMSH, PosteriorBSVARMIX, or PosteriorBSVARSV obtained by running the
estimate
function. The interpretation depends on the normalisation of the shocks using functionnormalise_posterior()
. Verify if the default settings are appropriate.
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
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$new(us_fiscal_lsuw, p = 1)
#> The identification is set to the default option of lower-triangular structural matrix.
# run the burn-in
burn_in = estimate(specification, 10)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> **************************************************|
#> Progress of the MCMC simulation for 10 draws
#> Every 10th draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# estimate the model
posterior = estimate(burn_in, 50)
#> **************************************************|
#> bsvars: Bayesian Structural Vector Autoregressions|
#> **************************************************|
#> Gibbs sampler for the SVAR model |
#> **************************************************|
#> Progress of the MCMC simulation for 50 draws
#> Every 10th draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|
# compute historical decompositions
hd = compute_historical_decompositions(posterior)
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar$new(p = 1) |>
estimate(S = 50) |>
estimate(S = 100) |>
compute_historical_decompositions() -> hd
#> 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 50 draws
#> Every 10th 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 100 draws
#> Every 10th draw is saved via MCMC thinning
#> Press Esc to interrupt the computations
#> **************************************************|