starvars: 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, <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>).

Getting started

Package details

AuthorAndrea Bucci [aut, cre, cph], Giulio Palomba [aut], Eduardo Rossi [aut], Andrea Faragalli [ctb]
MaintainerAndrea Bucci <andrea.bucci@unich.it>
LicenseGPL
Version1.1.10
URL https://github.com/andbucci/starvars
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("starvars")

Try the starvars package in your browser

Any scripts or data that you put into this service are public.

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