{ "_id": "6acdfa9aa14063b82c3add94", "Package": "svars", "Type": "Package", "Title": "Data-Driven Identification of SVAR Models", "Version": "1.3.13", "Date": "2025-08-24", "Author": "Alexander Lange [aut, cre], Bernhard Dalheimer [aut], Helmut\nHerwartz [aut], Simone Maxand [aut], Hannes Riebl [ctb]", "Authors@R": "c(person(\"Alexander\", \"Lange\", role = c(\"aut\", \"cre\"), email = \"alexanderlange328@gmail.com\"), person(\"Bernhard\", \"Dalheimer\", role = c(\"aut\"), email = \"bernhard.dalheimer@uni-goettingen.de\"), person(\"Helmut\", \"Herwartz\", role = c(\"aut\"), email = \"hherwartz@uni-goettingen.de\"), person(\"Simone\", \"Maxand\", role = c(\"aut\"), email = \"smaxand@uni-goettingen.de\"), person(\"Hannes\", \"Riebl\", role = c(\"ctb\")))", "Maintainer": "Alexander Lange ", "Description": "Implements data-driven identification methods for\nstructural vector autoregressive (SVAR) models as described in\nLange et al. (2021) . Based on an\nexisting VAR model object (provided by e.g. VAR() from the\n'vars' package), the structural impact matrix is obtained via\ndata-driven identification techniques (i.e. changes in\nvolatility (Rigobon, R. (2003)\n), patterns of GARCH (Normadin,\nM., Phaneuf, L. (2004) ),\nindependent component analysis (Matteson, D. S, Tsay, R. S.,\n(2013) ), least dependent\ninnovations (Herwartz, H., Ploedt, M., (2016)\n), smooth transition in\nvariances (Luetkepohl, H., Netsunajev, A. (2017)\n) or non-Gaussian maximum\nlikelihood (Lanne, M., Meitz, M., Saikkonen, P. 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