The paper is devoted to the development of methods for point and interval forecasting of the integral index of regional competitiveness. We stick to one of the most commonly used approaches to assessing the level of competitiveness based on its advantages over the others. As a result of this approach, the integral competitiveness index appears to be bounded, i.e. has a lower and upper limit. Due to this particular feature, it is proposed to carry out the forecasting of competitiveness index using multivariate logistic regression. The parameters of such model are determined using OLS through an inverse logarithmic transformation of the dependent variable. To calculate interval forecasts for the model, we proposed a new probability distribution for the errors of the nonlinear regression equation in the class of logistic curves. According to the proposed method, we calculated and forecasted the regional competitiveness level for the Russian Federation until 2020. The analysis of the data revealed some features of the regions distribution in terms of competitiveness level and indicated regional development trends. The paper has been prepared within the research project ¹1675: "Methodological and analytical tools for solving problems of spatial development of Russian economy under conditions of modern reforms" in terms of the basic part of the state order in the field of scientific activity of Russian Ministry of Education.

The regional differentiation makes impossible the sustainable socio-economic development of the subjects of the Russian Federation without the monitoring public governance results in space and time. Despite the comprehensive approach of the current procedure, approved by the federal government, it does not adequately assess the executive authorities effectiveness. Its main problem is the impossibility to assume such important administrative function as forecasting the social and economic development of Russian territorial subjects. The authors propose an alternative methodology on the basis of the system economic theory. This technique is implemented in several consecutive stages. Firstly, we develop the system of 30 indicators. Secondly, we normalize the values of the indicators using the method of pattern. Thirdly, we calculate the index of the social and economic development of Russian regions for 2011–2015 assuming that the indicators are equal. Last, we group Russian regions into clusters according to the level of their social and economic development using neural network technologies (Kohonen selforganizing maps). Only 9 in 80 subjects of the Russian Federation (RF) had the degree of realizing the social and economic potential higher than 40 % during the period under consideration. In 2011–2015, the most of regions had a low and lower than average level of social and economic development (with an aggregate share about 64.3 %). It means that, under current conditions, the majority of the RF regions have considerable reserves for realizing their social-economic potential. In particular, the absence of the territorial subjects with a high level of social and economic development proves that. The authors have simulated the social and economic situation of the RF subjects by means of an adequate Bayesian neural networks. The obtained results can be used as the basis for further research in the field of evaluating executive authorities effectiveness and forecasting the level of social and economic development of Russian regions.