Data‐driven identification in SVARs—When and how can statistical characteristics be used to unravel causal relationships?
Structural vector autoregressive analysis aims to trace the contemporaneous linkages among multiple economic time series back to underlying orthogonal structural shocks. Traditionally, researchers rely on economically motivated restrictions to identify these shocks. However, in the presence of heter...
| Publicado en: | Economic Inquiry Vol. 60; no. 2; pp. 668 - 694 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Apr2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=155474546&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 155474546 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00952583 EIQ jtl: Economic Inquiry issn: 00952583 maglogo: Y pubinfo: dt: Apr2022 vid: 60 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 155474546 10.1111/ecin.13035 ppf: 668 ppct: 26 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 1.1MB tig: atl: Data‐driven identification in SVARs—When and how can statistical characteristics be used to unravel causal relationships? aug: au: Herwartz, Helmut Lange, Alexander Maxand, Simone affil: Department of Economics, University of Goettingen, Goettingen, Germany Department of Economics, Humboldt‐Universität zu Berlin, Berlin, Germany su: Trace analysis Time series analysis Vector analysis Heteroscedasticity Data structures sug: subj: Trace analysis Time series analysis Vector analysis Heteroscedasticity Data structures keyword: heteroskedasticity independent components model selection non‐Gaussianity structural shocks heteroskedasticity independent components model selection non‐Gaussianity structural shocks ab: Structural vector autoregressive analysis aims to trace the contemporaneous linkages among multiple economic time series back to underlying orthogonal structural shocks. Traditionally, researchers rely on economically motivated restrictions to identify these shocks. However, in the presence of heteroskedasticity or non‐Gaussian independent components, only these statistical properties allow a locally unique identification. In this paper, we compare alternative statistical identification procedures under distinct covariance changes and distributional frameworks. We find that statistical identification schemes are robust under distinct data structures to some extent and support researchers in detecting shocks that feature an economic underpinning. The detection of independent components appears most flexible. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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