Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/300202 
Year of Publication: 
2024
Series/Report no.: 
Working Paper No. 15.2024
Publisher: 
Fondazione Eni Enrico Mattei (FEEM), Milano
Abstract: 
This paper presents new results on the identification of heteroskedastic structural vector autoregressive (HSVAR) models. Point identification of HSVAR models fails when some shifts in the variances of the structural shocks are suspected to be statistically indistinguishable from each other. This paper presents a new strategy that allows researchers to continue using HSVAR models in this empirically relevant case. We show that a combination of heteroskedasticity and zero restrictions can recover point identification in HSVAR models even in the absence of heterogeneous variance shifts. We derive the identified sets for impulse responses and show how to compute them. We perform inference on the impulse response functions, building on the robust Bayesian approach developed for set-identified SVARs. To illustrate our proposal, we present an empirical example based on the literature on the global crude oil market, where standard identification is expected to fail under heteroskedasticity.
Subjects: 
Heteroskedastic SVAR
point and set identification
robust Bayesian approach
JEL: 
C11
C32
C51
Q41
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.