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Advances in Structural Vector Autoregressions with Imperfect Identifying Information

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  • Baumeister, Christiane
  • Hamilton, James

Abstract

This paper examines methods for structural interpretation of vector autoregressions when the identifying information is regarded as imperfect or incomplete. We suggest that a Bayesian approach offers a unifying theme for guiding inference in such settings. Among other advantages, the unified approach solves a problem with calculating elasticities that appears not to have been recognized by earlier researchers. We also call attention to some computational concerns of which researchers who approach this problem using other methods should be aware.

Suggested Citation

  • Baumeister, Christiane & Hamilton, James, 2020. "Advances in Structural Vector Autoregressions with Imperfect Identifying Information," CEPR Discussion Papers 14603, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:14603
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    2. Rubaszek, Michał & Szafranek, Karol & Uddin, Gazi Salah, 2021. "The dynamics and elasticities on the U.S. natural gas market. A Bayesian Structural VAR analysis," Energy Economics, Elsevier, vol. 103(C).

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    More about this item

    Keywords

    Structural vector autoregressions; Bayesian analysis; Identification; Elasticities; Sign restrictions; Proxy vars;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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