| summary.SDDRidMSH | R Documentation |
Provides summary of the Savage-Dickey density ratios for verification of structural shocks homoskedasticity. The outcomes can be used to make probabilistic statements about identification through heteroskedasticity closely following ideas by Lütkepohl& Woźniak (2020).
## S3 method for class 'SDDRidMSH'
summary(object, ...)
object |
an object of class |
... |
additional arguments affecting the summary produced. |
A table reporting the logarithm of Bayes factors of homoskedastic to
heteroskedastic posterior odds "log(SDDR)" for each structural shock,
their numerical standard errors "NSE", and the implied posterior
probability of the homoskedasticity and heteroskedasticity hypothesis,
"Pr[homoskedasticity|data]" and "Pr[heteroskedasticity|data]"
respectively.
Tomasz Woźniak wozniak.tom@pm.me
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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jedc.2020.103862")}.
verify_identification.PosteriorBSVARMSH
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar_msh$new(us_fiscal_lsuw, M = 2)
set.seed(123)
# estimate the model
posterior = estimate(specification, 10)
# verify heteroskedasticity
sddr = verify_identification(posterior)
summary(sddr)
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar_msh$new(M = 2) |>
estimate(S = 10) |>
verify_identification() |>
summary() -> sddr_summary
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.