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Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable

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  • Millimet, Daniel L.
  • Parmeter, Christopher F.

Abstract

While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, nonclassical measurement error can lead to estimates, which are biased and inference which lacks power. Here, we consider a particular type of nonclassical measurement error: skewed errors. Unfortunately, skewed measurement error is likely to be a relatively common feature of many outcomes of interest in political science research. This study highlights the bias that can result even from relatively “small” amounts of skewed measurement error, particularly, if the measurement error is heteroskedastic. We also assess potential solutions to this problem, focusing on the stochastic frontier model and Nonlinear Least Squares. Simulations and three replications highlight the importance of thinking carefully about skewed measurement error as well as appropriate solutions.

Suggested Citation

  • Millimet, Daniel L. & Parmeter, Christopher F., 2022. "Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable," Political Analysis, Cambridge University Press, vol. 30(1), pages 66-88, January.
    • Handle: RePEc:cup:polals:v:30:y:2022:i:1:p:66-88_4
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    2. Delis, Manthos D. & Dioikitopoulos, Evangelos V. & Ongena, Steven, 2023. "Population diversity and financial risk-taking," Journal of Banking & Finance, Elsevier, vol. 151(C).

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    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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