[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v244y2024i2s0304407624001490.html
   My bibliography  Save this article

Specification tests for non-Gaussian structural vector autoregressions

Author

Listed:
  • Amengual, Dante
  • Fiorentini, Gabriele
  • Sentana, Enrique
Abstract
We propose specification tests for independent component analysis and structural vector autoregressions that assess the cross-sectional independence of non-Gaussian shocks by comparing their joint cumulative distribution with the product of their marginals at both discrete and continuous grids of argument values, the latter yielding a consistent test. We explicitly consider the sampling variability from computing the shocks using consistent estimators. We study the finite sample size of resampled versions of our tests in simulation exercises and show their non-negligible power against a variety of empirically plausible alternatives. Finally, we apply them to a dynamic model for three popular volatility indices.

Suggested Citation

  • Amengual, Dante & Fiorentini, Gabriele & Sentana, Enrique, 2024. "Specification tests for non-Gaussian structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 244(2).
  • Handle: RePEc:eee:econom:v:244:y:2024:i:2:s0304407624001490
    DOI: 10.1016/j.jeconom.2024.105803
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407624001490
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jeconom.2024.105803?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Consistent tests; Copulas; Finite normal mixtures; Independence tests; Pseudo maximum likelihood estimators; Volatility indices;
    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:244:y:2024:i:2:s0304407624001490. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.