alexanderlange53/svars: Data-Driven Identification of SVAR Models

Implements data-driven identification methods for structural vector autoregressive (SVAR) models. 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>)).

Getting started

Package details

AuthorAlexander Lange [aut, cre], Bernhard Dalheimer [aut], Helmut Herwartz [aut], Simone Maxand [aut], Hannes Riebl [ctb]
MaintainerAlexander Lange <[email protected]>
LicenseMIT + file LICENSE
Version1.3.2
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("alexanderlange53/svars")
alexanderlange53/svars documentation built on Sept. 11, 2019, 11:58 p.m.