Partial Identification of Structural Vector Autoregressions with Non-Centred Stochastic Volatility

Helmut L\"utkepohl, Fei Shang, Luis Uzeda and Tomasz Wo\'zniak
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Helmut L\"utkepohl: Freie Universit\"at Berlin and DIW Berlin
Fei Shang: South China University of Technology and Yuexiu Capital Holdings Group
Tomasz Wo\'zniak: University of Melbourne
Authors registered in the RePEc Author Service: Helmut Lütkepohl

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Abstract: We consider structural vector autoregressions that are identified through stochastic volatility under Bayesian estimation. Three contributions emerge from our exercise. First, we show that a non-centred parameterization of stochastic volatility yields a marginal prior for the conditional variances of structural shocks that is centred on homoskedasticity, with strong shrinkage and heavy tails -- unlike the common centred parameterization. This feature makes it well suited for assessing partial identification of any shock of interest. Second, Monte Carlo experiments on small and large systems indicate that the non-centred setup estimates structural parameters more precisely and normalizes conditional variances efficiently. Third, revisiting prominent fiscal structural vector autoregressions, we show how the non-centred approach identifies tax shocks that are consistent with estimates reported in the literature.

Date: 2024-04, Revised 2025-10
New Economics Papers: this item is included in nep-ecm and nep-ets
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