starvars: Vector Logistic Smooth Transition Models / Realized Covariances Construction

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: 1.1.1
Depends: R (≥ 3.5.0)
Imports: MASS, ks, zoo, data.table, methods, matrixcalc, vars, maxLik, rlist, fGarch, lubridate, xts, lessR, quantmod
Published: 2020-09-17
Author: Andrea Bucci [aut, cre, cph], Giulio Palomba [aut], Eduardo Rossi [aut], Andrea Faragalli [ctb]
Maintainer: Andrea Bucci <andrea.bucci at unich.it>
License: GPL-2 | GPL-3 [expanded from: GPL]
URL: https://github.com/andbucci/starvars
NeedsCompilation: no
Materials: README
CRAN checks: starvars results

Downloads:

Reference manual: starvars.pdf
Package source: starvars_1.1.1.tar.gz
Windows binaries: r-devel: starvars_1.1.1.zip, r-release: starvars_1.1.1.zip, r-oldrel: starvars_1.1.1.zip
macOS binaries: r-release: starvars_1.1.1.tgz, r-oldrel: starvars_1.1.1.tgz
Old sources: starvars archive

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