Compute one draw for each of the parameters in the dynamic shrinkage process
for the special case in which the shrinkage parameter kappa ~ Beta(alpha, beta)
with alpha = beta. The primary example is the dynamic horseshoe process with
alpha = beta = 1/2.
sampleDSP(
omega,
evolParams,
sigma_e = 1,
loc = NULL,
prior_dhs_phi = c(10, 2),
alphaPlusBeta = 1
)List of relevant components:
the T x p evolution error standard deviations sigma_wt,
the T x p log-volatility ht, the p x 1 log-vol unconditional mean(s) dhs_mean,
the p x 1 log-vol AR(1) coefficient(s) dhs_phi,
the T x p log-vol innovation standard deviations sigma_eta_t from the Polya-Gamma priors,
the p x 1 initial log-vol SD sigma_eta_0,
and the mean of log-vol means dhs_mean0 (relevant when p > 1)
T x p matrix of evolution errors
list of parameters to be updated (see Value below)
the observation error standard deviation; for (optional) scaling purposes
list of the row and column indices to fill in a band-sparse matrix
the parameters of the prior for the log-volatility AR(1) coefficient dhs_phi;
either NULL for uniform on [-1,1] or a 2-dimensional vector of (shape1, shape2) for a Beta prior
on [(dhs_phi + 1)/2]
For the symmetric prior kappa ~ Beta(alpha, beta) with alpha=beta, specify the sum [alpha + beta]