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Structural Vector Autoregressions with Imperfect Identifying Information

Author

Listed:
  • Christiane Baumeister
  • James D. Hamilton

Abstract

The problem of identification is often the core challenge of empirical economic research. The traditional approach to identification is to bring in additional information in the form of identifying assumptions, such as restrictions that certain magnitudes have to be zero. In this paper, we suggest that what are usually thought of as identifying assumptions should more generally be described as information that the analyst had about the economic structure before seeing the data. Such information is most naturally represented as a Bayesian prior distribution over certain features of the economic structure.

Suggested Citation

  • Christiane Baumeister & James D. Hamilton, 2022. "Structural Vector Autoregressions with Imperfect Identifying Information," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 466-470, May.
    • Handle: RePEc:aea:apandp:v:112:y:2022:p:466-70
    DOI: 10.1257/pandp.20221044
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    Citations

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    Cited by:

    1. Christiane Baumeister, 2023. "Pandemic, War, Inflation: Oil Markets at a Crossroads?," NBER Working Papers 31496, National Bureau of Economic Research, Inc.
    2. Sascha A. Keweloh, 2023. "Uncertain Short-Run Restrictions and Statistically Identified Structural Vector Autoregressions," Papers 2303.13281, arXiv.org, revised Apr 2024.
    3. Sascha A. Keweloh & Mathias Klein & Jan Pruser, 2023. "Estimating Fiscal Multipliers by Combining Statistical Identification with Potentially Endogenous Proxies," Papers 2302.13066, arXiv.org, revised Feb 2024.

    More about this item

    JEL classification:

    • 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
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

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