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Identification and estimation of non-Gaussian structural vector autoregressions

Listed author(s):
  • Markku Lanne

    ()

    (University of Helsinki and CREATES)

  • Mika Meitz

    ()

    (University of Helsinki)

  • Pentti Saikkonen

    ()

    (University of Helsinki)

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.

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File URL: ftp://ftp.econ.au.dk/creates/rp/15/rp15_16.pdf
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Paper provided by Department of Economics and Business Economics, Aarhus University in its series CREATES Research Papers with number 2015-16.

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Length: 69
Date of creation: 30 Mar 2015
Handle: RePEc:aah:create:2015-16
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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