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Outliers in Cross-Sectional Regression


  • Jørgen Lauridsen


  • Jesus Mur



The robustness of the results coming from an econometric application depends to a great extent on the quality of the sampling information. This statement is a general rule that becomes especially relevant in a spatial context where data usually have lots of irregularities. The purpose of our paper is to examine more closely this question paying attention to one point in particular, namely outliers. The presence of outliers in the sample may be useful, for example in order to break some multicollinearity relations but they may also result in other inconsistencies. The main aspect of our work is that we resolve the discussion in a spatial context, looking closely into the behaviour shown, under several unfavourable conditions, by the most outstanding misspecification tests. For this purpose, we plan and solve a Monte Carlo simulation. The conclusions point to the fact that these statistics react in a different way to the problems posed.

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  • Jørgen Lauridsen & Jesus Mur, 2004. "Outliers in Cross-Sectional Regression," ERSA conference papers ersa04p27, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa04p27

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    References listed on IDEAS

    1. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    2. Pena, Daniel, 1990. "Influential Observations in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 235-241, April.
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