R: VLSTAR Prediction

predict.VLSTAR {starvars}

R Documentation

VLSTAR Prediction

Description

One-step or multi-step ahead forecasts, with interval forecast, of a VLSTAR object.

Usage

## S3 method for class 'VLSTAR'
predict(
  object,
  ...,
  n.ahead = 1,
  conf.lev = 0.95,
  st.new = NULL,
  M = 5000,
  B = 1000,
  st.num = NULL,
  newdata = NULL,
  method = c("naive", "Monte Carlo", "bootstrap")
)

Arguments

`object`	An object of class ‘`VLSTAR`’ obtained through `VLSTAR()`
`...`	further arguments to be passed to and from other methods
`n.ahead`	An integer specifying the number of ahead predictions
`conf.lev`	Confidence level of the interval forecast
`st.new`	Vector of new data for the transition variable
`M`	An integer with the number of errors sampled for the `Monte Carlo` method
`B`	An integer with the number of errors sampled for the `bootstrap` method
`st.num`	An integer with the index of dependent variable if `st.new` is `NULL` and the transition variable is a lag of one of the dependent variables
`newdata`	`data.frame` or `matrix` of new data for the exogenous variables
`method`	A character identifying which multi-step ahead method should be used among `naive`, `Monte Carlo` and `bootstrap`

Value

A list containing:

`forecasts`	`data.frame` of predictions for each dependent variable and the (1-`\alpha`) prediction intervals
`y`	a matrix of values for y

Author(s)

Andrea Bucci and Eduardo Rossi

References

Granger C.W.J. and Terasvirta T. (1993), Modelling Non-Linear Economic Relationships. Oxford University Press;

Lundbergh S. and Terasvirta T. (2007), Forecasting with Smooth Transition Autoregressive Models. John Wiley and Sons;

Terasvirta T. and Yang Y. (2014), Specification, Estimation and Evaluation of Vector Smooth Transition Autoregressive Models with Applications. CREATES Research Paper 2014-8