Vector Logistic Smooth Transition Models Estimation and Prediction

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, ). Also, realized covariances can be constructed from stock market prices or returns, as explained in Andersen et al. (2001, ).

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