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

Published online by Cambridge University Press:  09 October 2018

Daniel K. Baissa  and
Carlisle Rainey
Daniel K. Baissa
Affiliation:
Department of Government, Harvard University, 1737 Cambridge St., Cambridge, MA 02138, USA
Carlisle Rainey*
Affiliation:
Department of Political Science, Florida State University, Room 531B, Bellamy Building, 113 Collegiate Loop, Tallahassee, FL 32306, USA
*
*Corresponding author. Email: [email protected]
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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.

Type
Original Articles
Information
Political Science Research and Methods , Volume 8 , Issue 1 , January 2020 , pp. 136 - 148
Copyright
Copyright © The European Political Science Association 2018 

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