LocalLevel: A constructor for local level state-space model.
In bayesforecast: Bayesian Time Series Modeling with Stan

LocalLevel

R Documentation

A constructor for local level state-space model.

Description

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

Usage

LocalLevel(ts, xreg = NULL, genT = FALSE, series.name = NULL)

Arguments

`ts`	a numeric or ts object with the univariate time series.
`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

By default the ssm() function generates a local-level, ets("A","N","N"), or exponential smoothing model from the forecast package. When trend = TRUE the SSM transforms into a local-trend, ets("A","A","N"), or the equivalent Holt model. For damped trend models set damped = TRUE. If seasonal = TRUE, the model is a seasonal local level model, or ets("A","N","A") model. Finally, the Holt-Winters method (ets("A","A","A")) is obtained by setting both Trend = TRUE and seasonal = TRUE.

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

The default priors used in a ssm( ) model are:

level ~ normal(0,0.5)
sigma0 ~ t-student(0,1,7)
level1 ~ 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() function 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.