Partial Identification of Heteroskedastic Structural Vector Autoregressions: Theory and Bayesian Inference

Helmut Lütkepohl, Fei Shang, Luis Uzeda and Tomasz Woźniak

Staff Working Papers from Bank of Canada

Abstract: We consider structural vector autoregressions that are identified through stochastic volatility. Our analysis focuses on whether a particular structural shock can be identified through heteroskedasticity without imposing any sign or exclusion restrictions. Three contributions emerge from our exercise: (i) a set of conditions that ensures the matrix containing structural parameters is either partially or globally unique; (ii) a shrinkage prior distribution for the conditional variance of structural shocks, centred on the hypothesis of homoskedasticity; and (iii) a statistical procedure for assessing the validity of the conditions outlined in (i). Our shrinkage prior ensures that the evidence for identifying a structural shock relies predominantly on the data and is less influenced by the prior distribution. We demonstrate the usefulness of our framework through a fiscal structural model and a series of simulation exercises.

Keywords: Econometric and statistical methods; Fiscal policy (search for similar items in EconPapers)
JEL-codes: C11 C12 C32 E62 (search for similar items in EconPapers)
Pages: 63 pages
Date: 2025-05
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.34989/swp-2025-14 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:25-14

Access Statistics for this paper

More papers in Staff Working Papers from Bank of Canada 234 Wellington Street, Ottawa, Ontario, K1A 0G9, Canada. Contact information at EDIRC.
Bibliographic data for series maintained by ().