svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis

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http://hdl.handle.net/10138/335987

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Lange , A , Dalheimer , B , Herwartz , H & Maxand , S 2021 , ' svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis ' , Journal of Statistical Software , vol. 97 , no. 5 . https://doi.org/10.18637/jss.v097.i05

Title: svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis
Author: Lange, Alexander; Dalheimer, Bernhard; Herwartz, Helmut; Maxand, Simone
Other contributor: University of Helsinki, Economics

Date: 2021-03
Language: eng
Number of pages: 34
Belongs to series: Journal of Statistical Software
ISSN: 1548-7660
DOI: https://doi.org/10.18637/jss.v097.i05
URI: http://hdl.handle.net/10138/335987
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.
Subject: 112 Statistics and probability
113 Computer and information sciences
SVAR models
identification
independent components
non-Gaussian maximum likelihood
changes in volatility
smooth transition covariance
R
STRUCTURAL VECTOR AUTOREGRESSIONS
INDEPENDENT COMPONENT ANALYSIS
MONETARY-POLICY SHOCKS
CONDITIONAL HETEROSKEDASTICITY
STATISTICAL IDENTIFICATION
MODELS
BOOTSTRAP
INFERENCE
DYNAMICS
TESTS
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