doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2025) <doi:10.1016/j.jedc.2025.105162>, Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.">

sstvars: Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models (original) (raw)

Penalized and non-penalized maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, counterfactual analysis, and computation of impulse response functions, generalized impulse response functions, generalized forecast error variance decompositions, as well as historical decompositions. See Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2025) <doi:10.1016/j.jedc.2025.105162>, Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.

Version: 1.2.2
Depends: R (≥ 4.0.0)
Imports: Rcpp (≥ 1.0.0), RcppArmadillo (≥ 0.12.0.0.0), parallel (≥ 4.0.0), pbapply (≥ 1.7-0), stats (≥ 4.0.0), graphics (≥ 4.0.0), utils (≥ 4.0.0)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2025-09-15
DOI: 10.32614/CRAN.package.sstvars
Author: Savi Virolainen ORCID iD [aut, cre]
Maintainer: Savi Virolainen <savi.virolainen at helsinki.fi>
BugReports: https://github.com/saviviro/sstvars/issues
License: GPL-3
URL: https://github.com/saviviro/sstvars
NeedsCompilation: yes
SystemRequirements: BLAS, LAPACK
Materials: README, NEWS
In views: Econometrics, TimeSeries
CRAN checks: sstvars results

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