Description
Vector Logistic Smooth Transition Models Estimation and Prediction.
Description
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>).
README.md
starvars
The goal of starvars is to estimate a Vector Logistic Smooth Transition model in R. The package allows also the user to test non-linearity and determine common structural breaks in multivariate time series. Furthermore, realized covariances and their Cholesky decomposition may be obtained through a dedicated function.
Installation
You can install the released version of starvars from CRAN with:
install.packages("starvars")
Example
This is a basic example which shows you how to solve a common problem:
## basic example code