sstvars: Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

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, and calculation of impulse response functions, generalized impulse response functions, and generalized forecast error variance 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 (2024) <doi:10.48550/arXiv.2403.14216>, Savi Virolainen (2024) <doi:10.48550/arXiv.2404.19707>.

Version: 1.0.1
Depends: R (≥ 4.0.0)
Imports: Rcpp (≥ 1.0.0), RcppArmadillo (≥, parallel (≥ 4.0.0), pbapply (≥ 1.7-0), stats (≥ 4.0.0), graphics (≥ 4.0.0)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
Published: 2024-05-29
DOI: 10.32614/CRAN.package.sstvars
Author: Savi Virolainen ORCID iD [aut, cre]
Maintainer: Savi Virolainen <savi.virolainen at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: BLAS, LAPACK
Materials: README NEWS
In views: Econometrics, TimeSeries
CRAN checks: sstvars results


Reference manual: sstvars.pdf
Vignettes: sstvars: Structural Smooth Transition Vector Autoregressive Models R


Package source: sstvars_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sstvars_1.0.1.tgz, r-oldrel (arm64): sstvars_1.0.1.tgz, r-release (x86_64): sstvars_1.0.1.tgz, r-oldrel (x86_64): sstvars_1.0.1.tgz
Old sources: sstvars archive


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