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When BLUE is not best: non-normal errors and the linear model

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  • Baissa, Daniel K.
  • Rainey, Carlisle

Abstract

Researchers in political science often estimate linear models of continuous outcomes using least squares. While it is well known that least-squares estimates are sensitive to single, unusual data points, this knowledge has not led to careful practices when using least-squares estimators. Using statistical theory and Monte Carlo simulations, we highlight the importance of using more robust estimators along with variable transformations. We also discuss several approaches to detect, summarize, and communicate the influence of particular data points.

Suggested Citation

  • Baissa, Daniel K. & Rainey, Carlisle, 2020. "When BLUE is not best: non-normal errors and the linear model," Political Science Research and Methods, Cambridge University Press, vol. 8(1), pages 136-148, January.
    • Handle: RePEc:cup:pscirm:v:8:y:2020:i:1:p:136-148_10
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    Cited by:

    1. Concetta Cardillo & Orlando Cimino & Marcello De Rosa & Martina Francescone, 2023. "The Evolution of Multifunctional Agriculture in Italy," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    2. Thomas Laloux & Lara Panning, 2021. "Why Defend Something I Don’t Agree with? Conflicts within the Commission and Legislative Amendments in Trilogues," Politics and Governance, Cogitatio Press, vol. 9(3), pages 40-51.

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