Partial identification of heteroskedastic structural vector autoregressions : theory and Bayesian inference / Helmut Lütkepohl, Fei Shang, Luis Uzeda, Tomasz Woźniak. : FB3-5/2025-14E-PDF

"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"--Abstract, page ii.

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