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Identification of Structural Vector Autoregressions Through Higher Unconditional Moments

Author

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  • Alain Guay

    (University of Quebec in Montreal)

Abstract

This paper pursues two objectives. First, we determine the sufficient condition for local, statistical identification of SVAR processes through the third and fourth unconditional moments of the reduced-form innovations. Our findings provide novel insights when the entire system is not identified, as they highlight which subset of structural parameters is identified and which is not. Second, we elaborate a tractable testing procedure to verify whether the identification condition holds, prior to the estimation of the structural parameters of the SVAR process. To do so, we design a new bootstrap procedure that improves the small sample properties of rank tests for the symmetry and kurtosis of the structural shocks.

Suggested Citation

  • Alain Guay, 2020. "Identification of Structural Vector Autoregressions Through Higher Unconditional Moments," Working Papers 20-19, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    • Handle: RePEc:bbh:wpaper:20-19
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    File URL: https://chairemacro.esg.uqam.ca/wp-content/uploads/sites/146/MS2019348_paper.pdf
    File Function: Revised version, 2020
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    References listed on IDEAS

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    Cited by:

    1. Christian Gourieroux & Joann Jasiak, 2023. "Dynamic deconvolution and identification of independent autoregressive sources," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 151-180, March.

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    More about this item

    Keywords

    Bootstrap procedure; excess kurtosis; identification condition; rank test; skewness; structural vector autoregression.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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