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Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference

Author

Listed:
  • Helmut Lutkepohl

    (Freie Universit\"at Berlin and DIW Berlin)

  • Fei Shang

    (South China University of Technology and Yuexiu Capital Holdings Group)

  • Luis Uzeda

    (Bank of Canada)

  • Tomasz Wo'zniak

    (University of Melbourne)

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.

Suggested Citation

  • Helmut Lutkepohl & Fei Shang & Luis Uzeda & Tomasz Wo'zniak, 2024. "Partial Identification of Heteroskedastic Structural VARs: Theory and Bayesian Inference," Papers 2404.11057, arXiv.org.
    • Handle: RePEc:arx:papers:2404.11057
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