Structural Vector Autoregressive Models with more Shocks than Variables Identiﬁed via Heteroskedasticity
12 Pages Posted: 29 May 2020
Date Written: May 2020
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: Suggested Citation