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A topological view on the identification of structural vector autoregressions

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  • Neusser, Klaus

Abstract

The notion of the group of orthogonal matrices acting on the set of all feasible identification schemes is used to characterize the identification problem arising in structural vector autoregressions. This approach presents several conceptual advantages. First, it provides a fundamental justification for the use of the normalized Haar measure as the natural uninformative prior. Second, it allows to derive the joint distribution of blocks of parameters defining an identification scheme. Finally, it provides a coherent way for studying perturbations of identification schemes which becomes relevant, among other things, for the specification of vector autoregressions with time-varying covariance matrices.

Suggested Citation

  • Neusser, Klaus, 2016. "A topological view on the identification of structural vector autoregressions," Economics Letters, Elsevier, vol. 144(C), pages 107-111.
    • Handle: RePEc:eee:ecolet:v:144:y:2016:i:c:p:107-111
    DOI: 10.1016/j.econlet.2016.05.003
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    1. Waggoner, Daniel F. & Zha, Tao, 2003. "Likelihood preserving normalization in multiple equation models," Journal of Econometrics, Elsevier, vol. 114(2), pages 329-347, June.
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    6. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
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    Cited by:

    1. Scott R. Baker & Tucker S. McElroy & Xuguang S. Sheng, 2020. "Expectation Formation Following Large, Unexpected Shocks," The Review of Economics and Statistics, MIT Press, vol. 102(2), pages 287-303, May.
    2. Maria Bolboaca & Sarah Fischer, 2019. "News Shocks: Different Effects in Boom and Recession?," Working Papers 19.01, Swiss National Bank, Study Center Gerzensee.

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    More about this item

    Keywords

    SVAR; Identification; Group action; Haar measure; Perturbation;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: 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

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