bayesforecast (version 1.0.1)

ssm: A constructor for a Additive linear State space model.

Description

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

Usage

ssm(ts,trend = FALSE,damped = FALSE,seasonal = FALSE,xreg = NULL,
           period = 0,genT = FALSE,series.name = NULL)

Arguments

ts

a numeric or ts object with the univariate time series.

trend

a boolean value to specify a trend local level model. By default is FALSE.

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.

seasonal

a boolean value to specify a seasonal local level model. By default is FALSE.

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.

period

an integer specifying the periodicity of the time series by default the value frequency(ts) is used.

genT

a boolean value to specify for a generalized t-student SSM model.

series.name

an optional string vector with the time series names.

Value

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

Details

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

By default the ssm() function generates a local level model (or a ets("A","N","N") or exponential smoothing model from the forecast package). If trend is set TRUE, then a local trend ssm model is defined (a equivalent ets("A","A","N") or Holt model from the forecast package). For damped trend models set damped to TRUE. If seasonal is set to TRUE a seasonal local level model is defined (a equivalent ets("A","N","A") model from the forecast package). For a Holt-Winters method (ets("A","A","A")) set Trend and seasonal to TRUE.

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)

• Seasonal ~ normal(0,0.5)

• sigma0 ~ t-student(0,1,7)

• level1 ~ normal(0,1)

• trend1 ~ normal(0,1)

• seasonal1 ~ 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().

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

# NOT RUN {
mod1 = ssm(ipc)

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

# Declaring an additive Holt-Winters model for the birth data
mod3 = ssm(birth,trend = TRUE,damped = TRUE,seasonal = TRUE)

# }