update_t_error {stochvol} | R Documentation |
Single MCMC update to Student's t-distribution
Description
Samples the degrees of freedom parameter of standardized and homoskedastic t-distributed input variates. Marginal data augmentation (MDA) is applied, tau is the vector of auxiliary latent states. Depending on the prior specification, nu might not be updated, just tau.
Usage
update_t_error(
homosked_data,
tau,
mean,
sd,
nu,
prior_spec,
do_tau_acceptance_rejection = TRUE
)
Arguments
homosked_data |
de-meaned and homoskedastic observations |
tau |
the vector of the latent states used in MDA. Updated in place |
mean |
the vector of the conditional means // TODO update docs in R |
sd |
the vector of the conditional standard deviations |
nu |
parameter nu. The degrees of freedom for the t-distribution. Updated in place |
prior_spec |
prior specification object. See type_definitions.h |
do_tau_acceptance_rejection |
boolean. If |
Details
The function samples tau and nu from the following hierarchical model: homosked_data_i = sqrt(tau_i) * (mean_i + sd_i * N(0, 1)) tau_i ~ InvGamma(.5*nu, .5*(nu-2)) Naming: The data is homoskedastic ex ante in the model, mean_i and sd_i are conditional on some other parameter in the model. The prior on tau corresponds to a standardized t-distributed heavy tail on the data.
See Also
Other stochvol_cpp:
update_fast_sv()
,
update_general_sv()
,
update_regressors()