Identification and Estimation of Non-Gaussian Structural Vector Autoregressions

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http://hdl.handle.net/10138/175471

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Lanne , M , Meitz , M & Saikkonen , P 2017 , ' Identification and Estimation of Non-Gaussian Structural Vector Autoregressions ' , Journal of Econometrics , vol. 196 , no. 2 , pp. 288-304 . https://doi.org/10.1016/j.jeconom.2016.06.002

Titel: Identification and Estimation of Non-Gaussian Structural Vector Autoregressions
Författare: Lanne, Markku; Meitz, Mika; Saikkonen, Pentti
Upphovmannens organisation: Department of Political and Economic Studies (2010-2017)
Economics
Helsinki Center of Economic Research (HECER) 2010-2012
Financial and Macroeconometrics
Helsinki Centre of Economic Research (HECER), alayksikkö 2013-2021
Department of Mathematics and Statistics
Datum: 2017-02
Språk: eng
Sidantal: 17
Tillhör serie: Journal of Econometrics
ISSN: 0304-4076
DOI: https://doi.org/10.1016/j.jeconom.2016.06.002
Permanenta länken (URI): http://hdl.handle.net/10138/175471
Abstrakt: Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not identified, and additional identifying restrictions are needed 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. Building upon this result, we introduce an identification scheme under which the maximum likelihood estimator of the parameters 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.
Subject: 112 Statistics and probability
511 Economics
Referentgranskad: Ja
Licens: cc_by_nc_nd
Användningsbegränsning: openAccess
Parallelpublicerad version: publishedVersion


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