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>).

Version: 1.1.10
Depends: R (≥ 4.0)
Imports: MASS, ks, zoo, doSNOW, foreach, methods, matrixcalc, optimParallel, parallel, vars, xts, lessR, quantmod
Published: 2022-01-17
DOI: 10.32614/CRAN.package.starvars
Author: Andrea Bucci [aut, cre, cph], Giulio Palomba [aut], Eduardo Rossi [aut], Andrea Faragalli [ctb]
Maintainer: Andrea Bucci <andrea.bucci at>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
Materials: README
CRAN checks: starvars results


Reference manual: starvars.pdf


Package source: starvars_1.1.10.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): starvars_1.1.10.tgz, r-oldrel (arm64): starvars_1.1.10.tgz, r-release (x86_64): starvars_1.1.10.tgz, r-oldrel (x86_64): starvars_1.1.10.tgz
Old sources: starvars archive


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