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The Consequences Of Model Misspecification In Regression Analysis.

J Deegan

    Multivariate Behavioral Research
    |January 30, 2016
    PubMed
    Summary

    This study characterizes error forms from model misspecification in ordinary least squares regression. It evaluates the consequences of these errors for unbiased coefficient estimation.

    Area of Science:

    • Econometrics
    • Statistical modeling

    Background:

    • Ordinary least squares (OLS) regression relies on model-data correspondence for unbiased coefficient estimates.
    • Model misspecification can violate this assumption, leading to biased results.

    Purpose of the Study:

    • To systematically characterize error forms arising from model misspecification in single-equation regression models.
    • To evaluate the impact of identified error forms on coefficient unbiasedness.

    Main Methods:

    • The study focuses on theoretical analysis of single-equation models.
    • It involves characterizing the structure of errors under various misspecification scenarios.

    Main Results:

    • Identified distinct error forms resulting from specific types of model misspecification.

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  • Evaluated the direct consequences of these error forms on the unbiasedness property of OLS estimators.

    • Conclusions:

    • Understanding and characterizing misspecification errors is crucial for reliable regression analysis.
    • The findings provide a framework for diagnosing and potentially mitigating bias in OLS estimations.