Structural vector autoregressive models with more shocks than variables identified via heteroskedasticity

Saved in:

Authors and Corporations:

Lütkepohl, Helmut (Author)

Type of Resource:

E-Book

Language:

English

published:

Berlin DIW Berlin, German Institute for Economic Research 2020

Series:

Subjects:

Structural vector autoregression

identification through heteroskedasticity

structural shocks

Graue Literatur

Source:

Verbunddaten SWB
Lizenzfreie Online-Ressourcen

Description
Summary:	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.
Physical Description:	1 Online-Ressource (circa 12 Seiten)