Holt {bayesforecast} | R Documentation |
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, |
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)