Evaluating the effects of heteroscedasticity on the economic growth of EU regions

The hypothesis of homoscedasticity of errors is convenient for the simplification of the estimation procedures. Unfortunately, this assumption is rather restrictive in the case of the analysis of spatially distributed data. Spatial units, in fact, can be very different in size and in other economic characteristics. This circumstance suggests the presence of heteroscedasticity in this typology of data. In this paper we study the effects of heteroscedasticity in regional economic convergence. We use two different estimators of the coefficient of variance and covariance matrix recently introduced in spatial econometrics literature that take into account the heteroscedasticity highlighted by the error terms. This methodology can be considered a suitable alternative to the identification of convergence clubs that represents a very popular approach for the analysis of structural economic differences between regions. The empirical analysis concerns the estimate of conditional economic convergence on EU NUTS 2 regions for the period 1981-2004.