stan_SVM: Fitting a Stochastic volatility model
In bayesforecast: Bayesian Time Series Modeling with Stan

Description Usage Arguments Details Value Author(s) References See Also Examples

Fitting a Stochastic Volatility model (SVM) in Stan.

stan_SVM(
  ts,
  arma = c(0, 0),
  xreg = NULL,
  chains = 4,
  iter = 2000,
  warmup = floor(iter/2),
  adapt.delta = 0.9,
  tree.depth = 10,
  stepwise = TRUE,
  prior_mu0 = NULL,
  prior_sigma0 = NULL,
  prior_ar = NULL,
  prior_ma = NULL,
  prior_alpha = NULL,
  prior_beta = NULL,
  prior_breg = NULL,
  series.name = NULL,
  ...
)

`ts`	a numeric or ts object with the univariate time series.
`arma`	Optionally, a specification of the ARMA model,same as order parameter: the two components (p, q) are the AR order,and the MA order.
`xreg`	Optionally, a numerical matrix of external regressors, which must have the same number of rows as ts. It should not be a data frame.
`chains`	An integer of the number of Markov Chains chains to be run, by default 4 chains are run.
`iter`	An integer of total iterations per chain including the warm-up, by default the number of iterations are 2000.
`warmup`	A positive integer specifying number of warm-up (aka burn-in) iterations. This also specifies the number of iterations used for step-size adaptation, so warm-up samples should not be used for inference. The number of warmup should not be larger than `iter` and the default is `iter/2`.
`adapt.delta`	An optional real value between 0 and 1, the thin of the jumps in a HMC method. By default is 0.9.
`tree.depth`	An integer of the maximum depth of the trees evaluated during each iteration. By default is 10.
`stepwise`	If TRUE, will do stepwise selection (faster). Otherwise, it searches over all models. Non-stepwise selection can be very slow, especially for seasonal models.
`prior_mu0`	The prior distribution for the location parameter in an SVM model. By default the value is set `NULL`, then the default normal(0,1) prior is used.
`prior_sigma0`	The prior distribution for the scale parameter in an SVM model. By default the value is set `NULL`, then the default student(7,0,1) prior is used.
`prior_ar`	The prior distribution for the auto-regressive parameters in an ARMA model. By default the value is set `NULL`, then the default normal(0,0.5) priors are used.
`prior_ma`	The prior distribution for the moving average parameters in an ARMA model. By default the value is set `NULL`, then the default normal(0,0.5) priors are used.
`prior_alpha`	The prior distribution for the arch parameters in a GARCH model. By default the value is set `NULL`, then the default normal(0,0.5) priors are used.
`prior_beta`	The prior distribution for the GARCH parameters in a GARCH model. By default the value is set `NULL`, then the default normal(0,0.5) priors are used.
`prior_breg`	The prior distribution for the regression coefficient parameters in a ARIMAX model. By default the value is set `NULL`, then the default student(7,0,1) priors are used.
`series.name`	an optional string vector with the series names.
`...`	Further arguments passed to `varstan` function.

The function returns a varstan object with the fitted model.

A varstan object with the fitted SVM model.

Asael Alonzo Matamoros

Sangjoon,K. and Shephard, N. and Chib.S (1998). Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models. Review of Economic Studies. 65(1), 361-93. url: https://www.jstor.org/stable/2566931.

Tsay, R (2010). Analysis of Financial Time Series. Wiley-Interscience. 978-0470414354, second edition.

Shumway, R.H. and Stoffer, D.S. (2010).Time Series Analysis and Its Applications: With R Examples. Springer Texts in Statistics. isbn: 9781441978646. First edition.

garch set_prior