stan_Holt: Fitting an Holt state-space model.

In bayesforecast: Bayesian Time Series Modeling with Stan

Description

Fitting an Holt state-space model in Stan.

Usage

stan_Holt(
  ts,
  damped = FALSE,
  xreg = NULL,
  genT = FALSE,
  chains = 4,
  iter = 2000,
  warmup = floor(iter/2),
  adapt.delta = 0.9,
  tree.depth = 10,
  stepwise = TRUE,
  prior_sigma0 = NULL,
  prior_level = NULL,
  prior_level1 = NULL,
  prior_trend = NULL,
  prior_trend1 = NULL,
  prior_damped = NULL,
  prior_breg = NULL,
  prior_df = NULL,
  series.name = NULL,
  ...
)

Arguments

ts	a numeric or ts object with the univariate time series.
damped	a boolean value to specify a damped trend local level model. By default is FALSE. If trend option is FALSE then damped is set to FALSE automatically.
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.
genT	a boolean value to specify for a generalized t-student SSM model.
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_sigma0	The prior distribution for the scale parameter in an SSM model. By default the value is set NULL, then the default student(7,0,1) prior is used.
prior_level	The prior distribution for the level parameter in a SSM model. By default the value is set NULL, then the default normal(0,0.5) priors are used.
prior_level1	The prior distribution for the initial level parameter in a SSM model. By default the value is set NULL, then the default student(6,0,2.5) priors are used.
prior_trend	The prior distribution for the trend parameter in a SSM model. By default the value is set NULL, then the default normal(0,0.5) priors are used.
prior_trend1	The prior distribution for the initial trend parameter in a SSM model. By default the value is set NULL, then the default student(6,0,2.5) priors are used.
prior_damped	The prior distribution for the damped trend parameter in a SSM 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 ARMAX model. By default the value is set NULL, then the default student(7,0,1) priors are used.
prior_df	The prior distribution for the degree freedom parameters in a t-student innovations SSM model. By default the value is set NULL, then the default gamma(2,0.1) priors are used.
series.name	an optional string vector with the series names.
...	Further arguments passed to varstan function.

Details

The function returns a varstan object with the fitted model.

When genT option is TRUE a t-student innovations ssm model (see Ardia (2010)) is generated see Fonseca, et. al (2019) for more details.

The default priors used in a ssm( ) model are:

• level ~ normal(0,0.5)

• Trend ~ normal(0,0.5)

• damped~ normal(0,0.5)

• sigma0 ~ t-student(0,1,7)

• level1 ~ normal(0,1)

• trend1 ~ normal(0,1)

• dfv ~ gamma(2,0.1)

• breg ~ t-student(0,2.5,6)

For changing the default prior use the function set_prior().

Value

A varstan object with the fitted SSM model.

Author(s)

Asael Alonzo Matamoros.

References

Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of degrees of freedom estimation in the Asymmetric GARCH model with Student-t Innovations. arXiv doi: arXiv: 1910.01398.

See Also

Sarima auto.arima set_prior garch

Examples

 # Declaring a Holt model for the ipc data.
 sf1 = stan_Holt(ipc,iter = 500,chains = 1)

 # Declaring a Holt damped trend model for the ipc data.
 sf2 = stan_Holt(ipc,damped = TRUE,iter = 500,chains = 1)

bayesforecast documentation built on June 17, 2021, 5:14 p.m.