Regional Competitiveness Evaluation of Visegrad Four Countries through Econometric Panel Data Model

Competitiveness and its evaluation have a significant position in the European Union, but also in the world. Effective analysis and evaluation of competitiveness must be based on pre-defined concept of competitiveness. In the case of regional competitiveness evaluation, we find, due to a lack of uniform approach, the problem with the basic concept and definition of regional competitiveness. Because of the absence of mainstream approach to regional competitiveness evaluation, there is a space for individual approach application. Example of such an approach can be portrait by macro-econometric modelling using regional panel data model that presents some link between micro and macro-econometric modelling and allows the elimination of imbalances caused by the data sets aggregation. This aim of the paper is formulation of an econometric panel data model with techniques using dummy variables for assessing regional competitiveness of the Visegrad Four countries. Theoretical background of the paper is based on the knowledge of theoretical concept and issues of regional competitiveness and productivity in the context of growth theories. The empirical part of the paper is focused on the application of panel nonlinear regression model for 35 regions at NUTS level 2 of the Visegrad Four. The level of regional competitiveness is analyzed by selected indicators evaluated performance of the EU growth strategies objectives until 2010. Selection of explanatory variables in the panel model appropriately reflects the level of competitive potential in the NUTS 2 regions of the Visegrad Four in the reference period 2000 - 2008. Use of econometric panel data model seems to be appropriate, since it marks better capture of the dynamics of changes and fixed or stochastic effects that have occurred in the proposed explanatory variables. Based on the estimation of the panel model, econometric and economic verification, final part of the paper includes a comparison of results for all explanatory variables in NUTS 2 regions, which are cross-sectional and time used to determine the order of influence of each NUTS 2 region of Visegrad Four to the overall competitiveness of the European Union. For purposes of the model, the overall EU competitiveness is approximated with the average volume of GDP for 271 NUTS 2 regions in EU27, according to the NUTS 2006 classification methodology.