Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/75605
Authors: 
Sturm, Jan-Egbert
Year of Publication: 
2000
Series/Report no.: 
CESifo Working Paper 288
Abstract: 
We argue that in modelling cross-country growth models one should first identify so-called outlying observations. For the data set of Sala-i-Martin, we use the least median of squares (LMS) estimator to identify outliers. As LMS is not suited for inference, we then use reweighted least squares (RLS) for our cross-country growth models. We identify 27 variables that are significantly related to economic growth. Subsequently, applying Sala-i-Martin's approach for the data set without outliers hardly reveals any additional information. Variables that are insignificant according to the RLS method are generally not significantly related to economic growth under the Sala-i-Martin approach.
Subjects: 
Sensitivity analysis
outliers
economic growth
JEL: 
C21
C52
O40
Document Type: 
Working Paper

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