compute_variance_decompositions.PosteriorBSVARSV function

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. In this heteroskedastic model the forecast error variance decompositions are computed for the forecasts with the origin at the last observation in sample data and using the conditional variance forecasts.

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

Arguments

• posterior: posterior estimation outcome - an object of class PosteriorBSVARSV obtained by running the estimate function.
• horizon: a positive integer number denoting the forecast horizon for the forecast error variance decomposition computations.

Returns

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.

Examples

# upload data
data(us_fiscal_lsuw)

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

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

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

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

# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
  specify_bsvar_sv$new(p = 1) |>
  estimate(S = 10) |> 
  estimate(S = 20) |> 
  compute_variance_decompositions(horizon = 8) -> fevd

References

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

See Also

compute_impulse_responses, estimate, normalise_posterior, summary

Author(s)

Tomasz Woźniak wozniak.tom@pm.me