Beware of "good" outliers and overoptimistic conclusions
AbstractThe 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.
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Bibliographic InfoPaper provided by ULB -- Universite Libre de Bruxelles in its series ULB Institutional Repository with number 2013/9907.
Date of creation: Jun 2009
Date of revision:
Publication status: Published in: Oxford Bulletin of Economics and Statistics (2009) v.71 n° 3,p.437-452
C12 • C21 • H11;
Other versions of this item:
- Catherine Dehon & Marjorie Gassner & Vincenzo Verardi, 2009. "Beware of 'Good' Outliers and Overoptimistic Conclusions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 437-452, 06.
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
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- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
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- H11 - Public Economics - - Structure and Scope of Government - - - Structure and Scope of Government
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