Data-Driven Identification of SVAR Models R package svars version 1.3.12 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)). Software Depends: R (>= 2.10), vars (>= 1.5.3) Imports: expm, reshape2, ggplot2, copula, clue, pbapply, steadyICA, DEoptim, zoo, strucchange, Rcpp, methods LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 2.1.0), tsDyn Alexander Lange <alexander.lange@uni-goettingen.de> Comprehensive R Archive Network (CRAN) Alexander Lange [aut, cre], Bernhard Dalheimer [aut], Helmut Herwartz [aut], Simone Maxand [aut], Hannes Riebl [ctb] MIT + file LICENSE (https://CRAN.R-project.org/package=svars/LICENSE) 2025-08-24 application/tgz https://CRAN.R-project.org/package=svars doi:10.32614/CRAN.package.svars