Robust inference for non-Gaussian SVAR models

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• dc.contributor.author Hoesch, Lukas
• dc.contributor.author Lee, Adam
• dc.contributor.author Mesters, Geert
• dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
• dc.date.accessioned 2024-11-14T10:09:46Z
• dc.date.available 2024-11-14T10:09:46Z
• dc.date.issued 2022-10-01
• dc.date.modified 2024-11-14T10:08:44Z
• dc.description.abstract All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non-Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, we propose a robust semi-parametric approach to conduct hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits non-Gaussianity when it is present, but yields correct size / coverage regardless of the distance to the Gaussian distribution. Empirically we revisit two macroeconomic SVAR studies where we document mixed results. For the oil price model of Kilian and Murphy (2012) we find that non-Gaussianity can robustly identify reasonable confidence sets, whereas for the labour supply-demand model of Baumeister and Hamilton (2015) this is not the case. Moreover, these exercises highlight the importance of using weak identification robust methods to asses estimation uncertainty when using non-Gaussianity for identification.
• dc.format.mimetype application/pdf*
• dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=1847
• dc.identifier.citation
• dc.identifier.uri http://hdl.handle.net/10230/68599
• dc.language.iso eng
• dc.relation.ispartofseries Economics and Business Working Papers Series; 1847
• dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
• dc.subject.keyword weak identification
• dc.subject.keyword semi-parametric inference
• dc.subject.keyword hypothesis testing
• dc.subject.keyword impulse responses
• dc.subject.keyword independent component analysis
• dc.subject.keyword Macroeconomics and International Economics
• dc.title Robust inference for non-Gaussian SVAR models
• dc.title.alternative
• dc.type info:eu-repo/semantics/workingPaper