starvars: Vector Logistic Smooth Transition Models Estimation andPrediction
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 unich.it> |
| License: | GPL-2 |GPL-3 [expanded from: GPL] |
| URL: | https://github.com/andbucci/starvars |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | starvars results |
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