bayesforecast (version 1.0.1)

stan_SVM: Fitting a Stochastic volatility model

Description

Fitting a Stochastic Volatility model (SVM) in Stan.

Usage

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,
  ...
)

Arguments

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.

Value

A varstan object with the fitted SVM model.

Details

The function returns a varstan object with the fitted model.

References

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.

See Also

garch set_prior

Examples

# NOT RUN {
 # Declares a SVM model for the IPC data
 sf1 = stan_SVM(ipc,arma = c(1,1),iter = 500,chains = 1)
# }
# NOT RUN {
# }