Structural Vector Autoregressive Models with more Shocks than Variables Identified via Heteroskedasticity

12 Pages Posted: 29 May 2020

Date Written: May 2020

Abstract

In conventional structural vector autoregressive (VAR) models it is assumed that there are at most as many structural shocks as there are variables in the model. It is pointed out that heteroskedasticity can be used to identify more shocks than variables. However, even if there is heteroskedasticity, the number of shocks that can be identified is limited. A number of results are provided that allow a researcher to assess how many shocks can be identified from specific forms of heteroskedasticity.

Keywords: Structural vector autoregression, identification through heteroskedasticity, structural shocks

JEL Classification: C32

Suggested Citation

Lütkepohl, Helmut, Structural Vector Autoregressive Models with more Shocks than Variables Identified via Heteroskedasticity (May 2020). DIW Berlin Discussion Paper No. 1871, Available at SSRN: https://ssrn.com/abstract=3610599 or http://dx.doi.org/10.2139/ssrn.3610599

Helmut Lütkepohl (Contact Author)

Free University of Berlin (FUB)

Otto Suhr Institut for Political Science\
Ihnestrasse 21
Berlin
Germany

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