CRAN/E | sstvars

sstvars

Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

Installation

About

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.

github.com/saviviro/sstvars
System requirements BLAS, LAPACK
Bug report File report

Key Metrics

Version 1.0.1
R ≥ 4.0.0
Published 2024-05-29 102 days ago
Needs compilation? yes
License GPL-3
CRAN checks sstvars results

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Maintainer

Maintainer

Savi Virolainen

Authors

Savi Virolainen

aut / cre

Material

README
NEWS
Reference manual
Package source

Vignettes

sstvars: Structural Smooth Transition Vector Autoregressive Models R

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

sstvars archive

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

Suggests

knitr
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LinkingTo

Rcpp
RcppArmadillo