文章基本信息

标题：svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis
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作者：Alexander Lange ; Bernhard Dalheimer ; Helmut Herwartz 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2021
卷号：97
期号：1
页码：1-34
DOI：10.18637/jss.v097.i05
出版社：University of California, Los Angeles
摘要：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.
关键词：SVAR models; identification; independent components; non-Gaussian maximum likelihood; changes in volatility; smooth transition covariance; R.
其他关键词：SVAR models;identification;independent components;non-Gaussian maximum likelihood;changes in volatility;smooth transition covariance;R