predict.VLSTAR: VLSTAR Prediction

Description

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

Usage

# S3 method for 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")
)

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

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

Author

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