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Structural vector autoregressive models with more shocks than variables identified via heteroskedasticity

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  • Lütkepohl, Helmut

Abstract

In conventional structural vector autoregressive models it is assumed that there are at most as many structural shocks as there are variables in the model. It is pointed out that heteroskedasticity can be used to identify more shocks than variables. Results are provided that allow a researcher to assess how many shocks can be identified from specific forms of heteroskedasticity.

Suggested Citation

  • Lütkepohl, Helmut, 2020. "Structural vector autoregressive models with more shocks than variables identified via heteroskedasticity," Economics Letters, Elsevier, vol. 195(C).
    • Handle: RePEc:eee:ecolet:v:195:y:2020:i:c:s0165176520302834
    DOI: 10.1016/j.econlet.2020.109458
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    References listed on IDEAS

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    More about this item

    Keywords

    Structural vector autoregression; Identification through heteroskedasticity; Structural shocks;
    All these keywords.

    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

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