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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 (≥ 0.12.0.0.0), 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 |
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 |
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: sstvars_1.0.1.zip, r-release: sstvars_1.0.1.zip, r-oldrel: sstvars_1.0.1.zip |
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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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