verify_identification.PosteriorBSVAR {bsvars}

Verifies identification through heteroskedasticity or non-normality of of structural shocks

Description

Displays information that the model is homoskedastic and with normal shocks.

Usage

## S3 method for class 'PosteriorBSVAR'
verify_identification(posterior)

Arguments

posterior

the estimation outcome obtained using estimate function

Value

Nothing. Just displays a message.

Author(s)

Tomasz Woźniak [e-mail]

References

Lütkepohl, H., and Woźniak, T., (2020) Bayesian Inference for Structural Vector Autoregressions Identified by Markov-Switching Heteroskedasticity. Journal of Economic Dynamics and Control 113, 103862, doi:10.1016/j.jedc.2020.103862.

Lütkepohl, H., Shang, F., Uzeda, L., and Woźniak, T. (2024) Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference. University of Melbourne Working Paper, 1–57, doi:10.48550/arXiv.2404.11057.

See Also

verify_identification.PosteriorBSVAR, verify_identification.PosteriorBSVARSV, verify_identification.PosteriorBSVARMIX, verify_identification.PosteriorBSVARMSH, verify_identification.PosteriorBSVART

Examples

# simple workflow
############################################################
# upload data
data(us_fiscal_lsuw)

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

# estimate the model
posterior      = estimate(specification, 10)

# verify heteroskedasticity
sddr           = verify_identification(posterior)

# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
  specify_bsvar$new(p = 1) |>
  estimate(S = 10) |> 
  verify_identification() -> sddr