Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Alexander Lange, Bernhard Dalheimer, Helmut Herwartz, Simone Maxand
Title: svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis
Abstract: Structural vector autoregressive (SVAR) models are frequently applied to trace the contemporaneous linkages among (macroeconomic) variables back to an interplay of orthogonal structural shocks. Under Gaussianity the structural parameters are unidentified without additional (often external and not data-based) information. In contrast, the often reasonable assumption of heteroskedastic and/or non-Gaussian model disturbances offers the possibility to identify unique structural shocks. We describe the R package svars which implements statistical identification techniques that can be both heteroskedasticity-based or independence-based. Moreover, it includes a rich variety of analysis tools that are well known in the SVAR literature. Next to a comprehensive review of the theoretical background, we provide a detailed description of the associated R functions. Furthermore, a macroeconomic application serves as a step-by-step guide on how to apply these functions to the identification and interpretation of structural VAR models.

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Paper: svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis     Download PDF (Downloads: 835)
Supplements:
svars_1.3.7.tar.gz: R source package Download (Downloads: 53; 1MB)
v97i05.R: R replication code Download (Downloads: 74; 5KB)

DOI: 10.18637/jss.v097.i05

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.