The authors would like to thank Gisèle Hites and their other colleagues at ECARES (European Center for Advanced Research in Economics and Statistics), CKE (Centre for Knowledge Economics) and CRED (Centre for Research in Economic Development) as well as two anonymous referees for helpful comments.Vincenzo Verardi is Associate Researcher of the FNRS and gratefully acknowledges their financial support. All remaining errors are the authors’ responsibility.
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Beware of ‘Good’ Outliers and Overoptimistic Conclusions†
Article first published online: 11 MAR 2009
DOI: 10.1111/j.1468-0084.2009.00543.x
© Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2009
Additional Information
How to Cite
Dehon, C., Gassner, M. and Verardi, V. (2009), Beware of ‘Good’ Outliers and Overoptimistic Conclusions. Oxford Bulletin of Economics and Statistics, 71: 437–452. doi: 10.1111/j.1468-0084.2009.00543.x
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Publication History
- Issue published online: 1 APR 2009
- Article first published online: 11 MAR 2009
- Final Manuscript Received: September 2008
- Abstract
- Article
- References
- Cited By
Keywords:
- C12;
- C21;
- H11
Abstract
The main goal of this paper is to warn practitioners of the danger of neglecting outliers in regression analysis, in particular, good leverage points (i.e. points lying close to the regression hyperplane but outlying in the x-dimension). While the types of outliers which do influence regression estimates (vertical outliers and bad leverage points) have been extensively investigated, good leverage points have been largely ignored, probably because they do not affect the estimated regression parameters. However, their effect on inference is far from negligible. We propose a step-by-step procedure to identify and treat all types of outliers. The paper of Persson and Tabellini [American Economic Review (2004) Vol. 94, pp. 25–46] linking the degree of proportionality of an electoral system to the size of government is discussed to illustrate how the choice of a measure and the existence of atypical observations may substantially influence results.