Vector Logistic Smooth Transition Models Estimation and Prediction
Long-run variance using Bartlett kernel
Coefficient method for objects of class VLSTAR
Log-Likelihood method
Multivariate log-likelihood
Multivariate CUMSUM test
Plot methods for a VLSTAR object
Plot methods for a vlstarpred object
VLSTAR Prediction
Print method for objects of class VLSTAR
Realized Covariance
Sum of squared error
Starting parameters for a VLSTAR model
Summary method for objects of class VLSTAR
VLSTAR- Estimation
Joint linearity test
Allows the user to estimate a vector logistic smooth transition autoregressive model via maximum log-likelihood or nonlinear least squares. It further permits to test for linearity in the multivariate framework against a vector logistic smooth transition autoregressive model with a single transition variable. The estimation method is discussed in Terasvirta and Yang (2014, <doi:10.1108/S0731-9053(2013)0000031008>). Also, realized covariances can be constructed from stock market prices or returns, as explained in Andersen et al. (2001, <doi:10.1016/S0304-405X(01)00055-1>).