BigVAR: Dimension Reduction Methods for Multivariate Time Series

Estimates VAR and VARX models with Structured Penalties using the methods developed by Nicholson et al (2017)<doi:10.1016/j.ijforecast.2017.01.003> and Nicholson et al (2020) <doi:10.48550/arXiv.1412.5250>.

Version: 1.1.2
Depends: R (≥ 3.5.0), methods, lattice
Imports: MASS, zoo, Rcpp, stats, utils, grDevices, graphics, abind
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
Suggests: knitr, rmarkdown, gridExtra, expm, MCS, quantmod, codetools
Published: 2023-01-09
DOI: 10.32614/CRAN.package.BigVAR
Author: Will Nicholson [cre, aut], David Matteson [aut], Jacob Bien [aut], Ines Wilms [aut]
Maintainer: Will Nicholson <wbn8 at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
SystemRequirements: C++11
Materials: NEWS
In views: TimeSeries
CRAN checks: BigVAR results


Reference manual: BigVAR.pdf
Vignettes: BigVAR: Tools for Modeling Sparse Vector Autoregressions with Exogenous Variables


Package source: BigVAR_1.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BigVAR_1.1.2.tgz, r-oldrel (arm64): BigVAR_1.1.2.tgz, r-release (x86_64): BigVAR_1.1.2.tgz, r-oldrel (x86_64): BigVAR_1.1.2.tgz
Old sources: BigVAR archive

Reverse dependencies:

Reverse imports: VIRF
Reverse suggests: frequencyConnectedness


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