Holt: A constructor for a Holt trend state-space model.

In bayesforecast: Bayesian Time Series Modeling with Stan

A constructor for a Holt trend state-space model.

Description

Constructor of the ets("A","A","Z") object for Bayesian estimation in Stan.

Usage

Holt(ts, damped = FALSE, xreg = NULL, genT = FALSE, 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, damped = FALSE. If trend option is FALSE then damped = 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.
series.name	an optional string vector with the time series names.

Details

The genT = TRUE option generates a t-student innovations SSM model. For more references check Ardia (2010); or Fonseca, et. al (2019).

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

The function returns a list with the data for running stan() f unction of rstan package.

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, and garch.

Examples

mod1 = Holt(ipc)

# Declaring a Holt damped trend model for the ipc data.
mod2 = Holt(ipc,damped = TRUE)

bayesforecast documentation built on June 8, 2025, 10:42 a.m.