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>)).
| Version: | 1.3.7 |
| Depends: | R (≥ 2.10), vars (≥ 1.5.3) |
| Imports: | expm, reshape2, ggplot2, copula, clue, pbapply, steadyICA, DEoptim, zoo, strucchange, Rcpp |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | testthat (≥ 2.1.0), tsDyn |
| Published: | 2021-03-18 |
| Author: | Alexander Lange [aut, cre], Bernhard Dalheimer [aut], Helmut Herwartz [aut], Simone Maxand [aut], Hannes Riebl [ctb] |
| Maintainer: | Alexander Lange <alexander.lange at uni-goettingen.de> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | yes |
| Citation: | svars citation info |
| In views: | TimeSeries |
| CRAN checks: | svars results |
| Reference manual: | svars.pdf |
| Vignettes: |
Data-Driven Identification of SVAR Models |
| Package source: | svars_1.3.7.tar.gz |
| Windows binaries: | r-devel: svars_1.3.7.zip, r-devel-UCRT: svars_1.3.7.zip, r-release: svars_1.3.7.zip, r-oldrel: svars_1.3.7.zip |
| macOS binaries: | r-release: svars_1.3.7.tgz, r-oldrel: svars_1.3.7.tgz |
| Old sources: | svars archive |
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