Constructor of the ets("Z","Z","Z") object for Bayesian estimation in Stan.
ssm(ts,trend = FALSE,damped = FALSE,seasonal = FALSE,xreg = NULL,
period = 0,genT = FALSE,series.name = NULL)The function returns a list with the data for running stan() function of
rstan package.
a numeric or ts object with the univariate time series.
a boolean value to specify a trend local level model. By default
is FALSE.
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.
a boolean value to specify a seasonal local level model. By default
is FALSE.
Optionally, a numerical matrix of external regressors, which must have the same number of rows as ts. It should not be a data frame.
an integer specifying the periodicity of the time series by default the value frequency(ts) is used.
a boolean value to specify for a generalized t-student SSM model.
an optional string vector with the time series names.
Asael Alonzo Matamoros.
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().
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)
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