Work

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

Year: 2025

Type: preprint

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.

Source: RePEc: Research Papers in Economics

DOI: 10.34989/swp-2025-14

Authors: Lütkepohl, Helmut, Shang, Fei, Uzeda, Luis, Woźniak, Tomasz

Institutions: Freie Universität Berlin, Bank of Canada, The University of Melbourne

Language: English

Topic: Monetary Policy and Economic Impact

Subfield: General Economics, Econometrics and Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W6927702341

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