Multicollinearity in cross-sectional regressions

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 this paper is to examine more closely this question paying attention to the impact of multicollinearity. It is well known that the reliability of estimators (leastsquares or maximum-likelihood) gets worse as the linear relationships between the regressors become more acute. 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 when collinear variables are added to the regression. For this purpose, we plan and solve a Monte Carlo simulation. The conclusions point to the fact that these statistics react in different ways to the problems posed. ‡ Acknowledgements: This work has been carried out with the financial support of project SEC 200202350 of the Spanish Ministerio de Educatión. The authors also wish to thank Ana Angulo for her invaluable and disinterested collaboration.