One-step or multi-step ahead forecasts, with interval forecast, of a VLSTAR object.
# 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")
)An object of class ‘VLSTAR’ obtained through VLSTAR()
further arguments to be passed to and from other methods
An integer specifying the number of ahead predictions
Confidence level of the interval forecast
Vector of new data for the transition variable
An integer with the number of errors sampled for the Monte Carlo method
An integer with the number of errors sampled for the bootstrap method
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
data.frame or matrix of new data for the exogenous variables
A character identifying which multi-step ahead method should be used among naive, Monte Carlo and bootstrap
A list containing:
data.frame of predictions for each dependent variable and the (1-\(\alpha\)) prediction intervals
a matrix of values for y
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
VLSTAR for log-likehood and nonlinear least squares estimation of the VLSTAR model.