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:10.48550/arXiv.2403.14216>, Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.
Version: 1.2.1 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), utils (≥ 4.0.0) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown Published: 2025-06-19 DOI: 10.32614/CRAN.package.sstvars Author: Savi Virolainen [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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=sstvars to link to this page.
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