Data-Driven Identification of SVAR Models
Bootstrap after Bootstrap
Counterfactuals for SVAR Models
Chow Test for Structural Break
Forecast error variance decomposition for SVAR Models
Historical decomposition for SVAR Models
Recursive identification of SVAR models via Cholesky decomposition
Identification of SVAR models based on Changes in volatility (CV)
Independence-based identification of SVAR models via Cramer-von Mises ...
Independence-based identification of SVAR models build on distance cov...
Identification of SVAR models through patterns of GARCH
Non-Gaussian maximum likelihood (NGML) identification of SVAR models
Identification of SVAR models by means of a smooth transition (ST) in ...
Impulse Response Functions for SVAR Models
Chi-square test for joint hypotheses
Moving block bootstrap for IRFs of identified SVARs
Structural stability of a VAR(p)
svars: Data-driven identification of structural VAR models
Wild bootstrap for IRFs of identified SVARs
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>)).