fit_ASV: MCMC Sampler for Adaptive Stchoastic Volatility (ASV) model

In dsp: Dynamic Shrinkage Process and Change Point Detection

MCMC Sampler for Adaptive Stchoastic Volatility (ASV) model

Description

The penalty is determined by the prior on the evolution errors, which include:

• the dynamic horseshoe prior ('DHS');

• the static horseshoe prior ('HS');

• the Bayesian lasso ('BL');

• the normal-inverse-gamma prior ('NIG').

In each case, the evolution error is a scale mixture of Gaussians. Sampling is accomplished with a (parameter-expanded) Gibbs sampler, mostly relying on a dynamic linear model representation.

Usage

fit_ASV(
  y,
  beta = 0,
  evol_error = "DHS",
  D = 1,
  nsave = 1000,
  nburn = 1000,
  nskip = 4,
  mcmc_params = list("h", "logy2hat", "sigma2", "evol_sigma_t2", "dhs_phi", "dhs_mean"),
  nugget = FALSE,
  computeDIC = TRUE,
  verbose = TRUE
)

Arguments

y	the T x 1 vector of time series observations.
beta	the mean of the observed process y. If not provided, they are assumed to be 0.
evol_error	the evolution error distribution; must be one of 'DHS' (dynamic horseshoe prior), 'HS' (horseshoe prior), 'BL' (Bayesian lasso), or 'NIG' (normal-inverse-gamma prior)
D	degree of differencing (D = 1, or D = 2)
nsave	number of MCMC iterations to record
nburn	number of MCMC iterations to discard (burin-in)
nskip	number of MCMC iterations to skip between saving iterations, i.e., save every (nskip + 1)th draw
mcmc_params	named list of parameters for which we store the MCMC output; must be one or more of: "h" (Log variance) "h_smooth" (smooth estimate of log variances. Only used when nugget_asv = TRUE) "logy2hat" (posterior predictive distribution of log(y^2)) "sigma2" (Variance, i.e. exp(h)) "evol_sigma_t2" (evolution error variance) "dhs_phi" (DHS AR(1) coefficient) "dhs_mean" (DHS AR(1) unconditional mean)
nugget	logical; if TRUE, fits the nugget variant of the ASV model
computeDIC	logical; if TRUE, compute the deviance information criterion DIC and the effective number of parameters p_d
verbose	logical; should R report extra information on progress?

Value

A named list of the nsave MCMC samples for the parameters named in mcmc_params

Note

The data y may contain NAs, which will be treated with a simple imputation scheme via an additional Gibbs sampling step. In general, rescaling y to have unit standard deviation is recommended to avoid numerical issues.

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