Compute one draw of the unconditional means in an TAR(1) model with Gaussian innovations and time-dependent innovation variances. In particular, we use the sampler for the log-volatility TAR(1) process with the parameter-expanded Polya-Gamma sampler. The sampler also applies to a multivariate case with independent components.
t_sampleLogVolMu(
h,
h_mu,
h_phi,
h_phi2,
h_sigma_eta_t,
h_sigma_eta_0,
h_st,
h_log_scale = 0
)the sampled mean(s) dhs_mean
the T vector of log-volatilities
the 1 vector of previous means
the 1 vector of AR(1) coefficient(s)
the 1 vector of previous penalty coefficient(s)
the T vector of log-vol innovation standard deviations
the standard deviations of initial log-vols
the T vector of indicators on whether each time-step exceed the estimated threshold
prior mean from scale mixture of Gaussian (Polya-Gamma) prior, e.g. log(sigma_e^2) or dhs_mean0