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

Author

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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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    Cited by:

    1. Fuest, Clemens & Hugger, Felix & Neumeier, Florian, 2022. "Corporate profit shifting and the role of tax havens: Evidence from German country-by-country reporting data," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 454-477.
    2. Oleg Badunenko & Daniel J. Henderson, 2024. "Production analysis with asymmetric noise," Journal of Productivity Analysis, Springer, vol. 61(1), pages 1-18, February.
    3. Castañeda Dower, Paul & Gerber, Theodore P. & Weber, Shlomo, 2022. "Firms, kinship networks, and economic growth in the Kyrgyz Republic," Journal of Comparative Economics, Elsevier, vol. 50(4), pages 997-1018.
    4. Delis, Manthos D. & Dioikitopoulos, Evangelos V. & Ongena, Steven, 2023. "Population diversity and financial risk-taking," Journal of Banking & Finance, Elsevier, vol. 151(C).
    5. Puerta-Cuartas, Alejandro & Ramírez-Hassan, Andrés, 2025. "A spatial one-sided error model to identify where unarrested criminals live," Economic Modelling, Elsevier, vol. 142(C).
    6. Richard Gearhart & Lyudmyla Sonchak-Ardan & Nyakundi Michieka, 2022. "The efficiency of COVID cases to COVID policies: a robust conditional approach," Empirical Economics, Springer, vol. 63(6), pages 2903-2948, December.
    7. Deniz, Pinar & Stengos, Thanasis, 2025. "Heterogeneity of institutions and model uncertainty in the income inequality nexus," European Journal of Political Economy, Elsevier, vol. 87(C).
    8. Alvarez, Sean P. & Geloso, Vincent & Scheck, Macy, 2024. "Revisiting the relationship between economic freedom and development to account for statistical deception by autocratic regimes," European Journal of Political Economy, Elsevier, vol. 85(C).
    9. Al-Azzam, Moh’d & Charfeddine, Lanouar, 2022. "Financing new entrepreneurship: Credit or microcredit?," Economics Letters, Elsevier, vol. 216(C).
    10. Daniel L. Millimet & Christopher F. Parmeter, 2025. "The impact of measurement error on trends in earnings inequality in the USA," Empirical Economics, Springer, vol. 69(5), pages 2727-2753, November.
    11. Valerie Mueller & Camila Páez-Bernal & Clark Gray & Karen Grépin, 2023. "The Gendered Consequences of COVID-19 for Internal Migration," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(4), pages 1-37, August.

    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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