predict.VLSTAR: VLSTAR Prediction

In starvars: Vector Logistic Smooth Transition Models Estimation and 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-α) 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

See Also

VLSTAR for log-likehood and nonlinear least squares estimation of the VLSTAR model.

starvars documentation built on Jan. 18, 2022, 1:08 a.m.