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A Necessary and Sufficient Identification Rule for Structural Models Estimated in Practice.

E E Rigdon

    Multivariate Behavioral Research
    |January 21, 2016
    PubMed
    Summary

    This study introduces a simple graphical system for identifying structural equation models. This method avoids complex statistical estimation for common block-recursive models.

    Area of Science:

    • Statistics
    • Econometrics
    • Social Sciences

    Background:

    • Structural equation models (SEMs) are widely used in various scientific disciplines.
    • Identifying the structural component of SEMs is crucial for valid analysis.
    • Existing identification techniques have limitations for commonly estimated models.

    Purpose of the Study:

    • To present a straightforward classification system for identifying the structural component of SEMs.
    • To focus on block-recursive models with a maximum of two equations per block.
    • To offer a graphical technique that bypasses the need for model estimation.

    Main Methods:

    • Development of a novel graphical classification system.
    • Application of the system to block-recursive structural equation models.

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  • Comparative review of existing identification techniques (econometric, information matrix).

    • Main Results:

    • The proposed classification system provides a necessary and sufficient condition for identification.
    • The graphical technique is applicable to commonly estimated block-recursive SEMs.
    • Limitations of econometric and information matrix techniques are highlighted for these models.

    Conclusions:

    • The graphical classification system offers a practical and efficient method for SEM identification.
    • This approach simplifies the analysis of specific types of structural equation models.
    • Researchers can benefit from a non-estimation-based identification strategy.