Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference

Helmut Lütkepohl, Fei Shang, Luis Uzeda and Tomasz Woźniak

No 2081, Discussion Papers of DIW Berlin from DIW Berlin, German Institute for Economic Research

Abstract: We consider structural vector autoregressions identified through stochastic volatility. Our focus is on whether a particular structural shock is identified by heteroskedasticity without the need to impose any sign or exclusion restrictions. Three contributions emerge from our exercise: (i) a set of conditions under which the matrix containing structural parameters is partially or globally unique; (ii) a statistical procedure to assess the validity of the conditions mentioned above; and (iii) a shrinkage prior distribution for conditional variances centred on a hypothesis of homoskedasticity. Such a prior ensures that the evidence for identifying a structural shock comes only from the data and is not favoured by the prior. We illustrate our new methods using a U.S. fiscal structural model.

Keywords: Identification through heteroskedasticity; stochastic volatility; non-centred parameterisation; shrinkage prior; normal product distribution; tax shocks (search for similar items in EconPapers)
JEL-codes: C11 C12 C32 E62 (search for similar items in EconPapers)
Pages: 57 p.
Date: 2024
New Economics Papers: this item is included in nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference (2024)
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