Partially identified heteroskedastic SVARs

Emanuele Bacchiocchi, Andrea Bastianin, Toru Kitagawa and Elisabetta Mirto
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Toru Kitagawa: Department of Economics, Brown University
Elisabetta Mirto: Department of Economics, Management and Quantitative Methods, University of Milan

No 2024.15, Working Papers from Fondazione Eni Enrico Mattei

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.

Keywords: Heteroskedastic SVAR; point and set identification; robust Bayesian approach (search for similar items in EconPapers)
JEL-codes: C11 C32 C51 Q41 (search for similar items in EconPapers)
Date: 2024-06
New Economics Papers: this item is included in nep-ets
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Related works:
Working Paper: Partially identified heteroskedastic SVARs (2024)
Working Paper: Partially identified heteroskedastic SVARs (2024)
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