svars: Data-Driven Identification of SVAR Models
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: |
1.3.9 |
| 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: |
2022-02-04 |
| 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 |
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