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Identifying Structural Vector Autoregressions via Changes in Volatility

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  • Helmut Lütkepohl

Abstract

Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modelling. Identification is often achieved by imposing restrictions on the impact or long-run effects of shocks or by considering sign restrictions for the impulse responses. In a number of articles changes in the volatility of the shocks have also been used for identification. The present study focusses on the latter device. Some possible setups for identification via heteroskedasticity are reviewed and their potential and limitations are discussed. Two detailed examples are considered to illustrate the approach.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.412670.de/dp1259.pdf
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Bibliographic Info

Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 1259.

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Length: 33 p.
Date of creation: 2012
Date of revision:
Handle: RePEc:diw:diwwpp:dp1259

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Keywords: Markov switching model; vector autoregression; heteroskedasticity; vector GARCH; conditional heteroskedasticity;

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References

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Cited by:
  1. Kohonen, Anssi, 2013. "On detection of volatility spillovers in overlapping stock markets," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 140-158.

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