t_sampleLogVolMu: Sample the TAR(1) unconditional means

In dsp: Dynamic Shrinkage Process and Change Point Detection

Sample the TAR(1) unconditional means

Description

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.

Usage

t_sampleLogVolMu(
  h,
  h_mu,
  h_phi,
  h_phi2,
  h_sigma_eta_t,
  h_sigma_eta_0,
  h_st,
  h_log_scale = 0
)

Arguments

h	the T vector of log-volatilities
h_mu	the 1 vector of previous means
h_phi	the 1 vector of AR(1) coefficient(s)
h_phi2	the 1 vector of previous penalty coefficient(s)
h_sigma_eta_t	the T vector of log-vol innovation standard deviations
h_sigma_eta_0	the standard deviations of initial log-vols
h_st	the T vector of indicators on whether each time-step exceed the estimated threshold
h_log_scale	prior mean from scale mixture of Gaussian (Polya-Gamma) prior, e.g. log(sigma_e^2) or dhs_mean0

Value

the sampled mean(s) dhs_mean

dsp documentation built on Aug. 21, 2025, 5:29 p.m.