SVARS

The R package svars implements 6 data-driven identification methods for structural vector autoregressive (SVAR) models. Have a look at the project on Github or visit the info page on CRAN. The publication in the Journal of Statistical Software can be found here.


Papers that

Authors: Alexander Lange, Bernhard Dalheimer, Simone Maxand, Helmut Herwartz

Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021). 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)), patterns of GARCH (Normadin, M., Phaneuf, L., 2004), 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>)).