ssm: A constructor for a Additive linear State space model.
In bayesforecast: Bayesian Time Series Modeling with Stan

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

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

1 2	ssm(ts,trend = FALSE,damped = FALSE,seasonal = FALSE,xreg = NULL, period = 0,genT = FALSE,series.name = NULL)

`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.

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().

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

Asael Alonzo Matamoros.

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.

Sarima auto.arima set_prior garch

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)