compute_historical_decompositions.PosteriorBSVARMIX: Computes posterior draws of historical decompositions

In bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

Computes posterior draws of historical decompositions

Description

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 'PosteriorBSVARMIX'
compute_historical_decompositions(posterior, show_progress = TRUE)

Arguments

posterior	posterior estimation outcome - an object of class PosteriorBSVARMIX 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.

Author(s)

Tomasz Woźniak [e-mail]

References

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

See Also

estimate, normalise_posterior, summary

Examples

# upload data
data(us_fiscal_lsuw)

# specify the model and set seed
set.seed(123)
specification  = specify_bsvar_mix$new(us_fiscal_lsuw, p = 1, M = 2)

# run the burn-in
burn_in        = estimate(specification, 10)

# estimate the model
posterior      = estimate(burn_in, 20)

# compute historical decompositions
hd             = compute_historical_decompositions(posterior)

# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
  specify_bsvar_mix$new(p = 1, M = 2) |>
  estimate(S = 10) |> 
  estimate(S = 20) |> 
  compute_historical_decompositions() -> hds
  

bsvars documentation built on Oct. 24, 2024, 5:11 p.m.