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 |
| 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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