In the presence of outliers in a dataset, a least squares estimation may not be the most adequate choice to get representative results. Indeed estimations could have been excessively infuenced even by a very limited number of atypical observations. In this article, we propose a new Hausman-type test to check for this. The test is based on the trade-off between robustness and effciency and allows to conclude if a least squares estimation is appropriate or if a robust method should be preferred. An economic example is provided to illustrate the usefulness of the test.
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Paper provided by Université Libre de Bruxelles, Ecares in its series ECARES Working Papers with number
2008_006.