verify_identification.PosteriorBSVART {bsvars} | R Documentation |
Verifies identification through heteroskedasticity or non-normality of of structural shocks
Description
Computes the logarithm of Bayes factor for the hypothesis of normality
of the joint conditional distribution of the structural shocks via
Savage-Dickey Density Ration (SDDR).
The hypothesis of normality in this t-distributed shocks model is represented
by restriction setting the degrees-of-freedom parameter \nu
to infinity:
H_0: \nu = \infty
The logarithm of Bayes factor for this hypothesis can be computed using the SDDR as the difference of logarithms of the marginal posterior distribution ordinate at the restriction less the marginal prior distribution ordinate at the same point:
log p(H_0 | data) - log p(H_0)
Therefore, a negative value of the difference is the evidence against homoskedasticity of the structural shock. The estimation of the marginal posterior ordinate is done using truncated Gaussian kernel smoothing.
Usage
## S3 method for class 'PosteriorBSVART'
verify_identification(posterior)
Arguments
posterior |
the estimation outcome obtained using |
Value
An object of class SDDRidT
that is a list with components:
logSDDR
the value of the logarithm of the Bayes factor
SDDR
the value of the Bayes factor
Author(s)
Tomasz Woźniak wozniak.tom@pm.me
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
Examples
# simple workflow
############################################################
# upload data
data(us_fiscal_lsuw)
# specify the model and set seed
specification = specify_bsvar_t$new(us_fiscal_lsuw)
set.seed(123)
# estimate the model
posterior = estimate(specification, 10)
# verify heteroskedasticity
sddr = verify_identification(posterior)
# workflow with the pipe |>
############################################################
set.seed(123)
us_fiscal_lsuw |>
specify_bsvar_t$new() |>
estimate(S = 10) |>
verify_identification() -> sddr