Identification and estimation of non-Gaussian structural vector autoregressions
Mika Meitz () and
Pentti Saikkonen ()
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not identified, and additional identifying restrictions are typically imposed in applied work. We show that the Gaussian case is an exception in that a SVAR model whose error vector consists of independent non-Gaussian components is, without any additional restrictions, identified and leads to (essentially) unique impulse responses. We also introduce an identification scheme under which the maximum likelihood estimator of the non-Gaussian SVAR model is consistent and asymptotically normally distributed. As a consequence, additional economic identifying restrictions can be tested. In an empirical application, we find a negative impact of a contractionary monetary policy shock on financial markets, and clearly reject the commonly employed recursive identifying restrictions.
Keywords: Structural vector autoregressive model; identification; impulse responses; non-Gaussianity (search for similar items in EconPapers)
JEL-codes: C13 C32 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13) Track citations by RSS feed
Downloads: (external link)
Journal Article: Identification and estimation of non-Gaussian structural vector autoregressions (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2015-16
Access Statistics for this paper
More papers in CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Bibliographic data for series maintained by ().