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A Goodness-of-Identifiability Criterion for Parametric Statistical Models

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  • David Pacini

Abstract

This note sets out a goodness-of-identifiability criterion. This criterion quantifies the identifying strength of a parametric statistical model. Unlike the qualitative criterion for identifiability based only on the Fisher matrix, it applies to both regular and irregular points of the Fisher matrix. Unlike the qualitative criterion based only on the Hellinger distance, it quantifies set-identification. Â JEL classification numbers: C10, C50.

Suggested Citation

  • David Pacini, 2022. "A Goodness-of-Identifiability Criterion for Parametric Statistical Models," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 11(4), pages 1-1.
    • Handle: RePEc:spt:stecon:v:11:y:2022:i:4:f:11_4_1
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    References listed on IDEAS

    as
    1. Vijverberg, Wim P. M., 1993. "Measuring the unidentified parameter of the extended Roy model of selectivity," Journal of Econometrics, Elsevier, vol. 57(1-3), pages 69-89.
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    5. Sargan, J D, 1983. "Identification and Lack of Identification," Econometrica, Econometric Society, vol. 51(6), pages 1605-1633, November.
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    8. Arthur Lewbel, 2019. "The Identification Zoo: Meanings of Identification in Econometrics," Journal of Economic Literature, American Economic Association, vol. 57(4), pages 835-903, December.
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    More about this item

    Keywords

    Parametric statistical model; Identifiability; Fisher matrix; Hellinger distance.;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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