The necessity to forecast the industrial production volume is associated with the growing influence and importance of this industry for the development of both individual regions and the country as a whole. The industrial sector occupies a special place among the socio-economic development factors of the region, which is why forecasting the dynamics of its indicators is of particular value. Therefore, the problem of improving the accuracy of the data obtained becomes even more urgent. The analysis of the sources make it possible to conclude that it is advisable to use mixed models combining the strengths of different approaches. In this article, for the greatest reliability of the calculated values, we propose to use the advantages of various methods – regression analysis and extrapolation trends. Based on the correlation analysis, we have indicators determined that have showed a high degree of mutual consistency with the dependent variable. We have carried out regression analysis, based on the results of which a multiple regression model was developed including indicators of the volume of industrial production, investments in fixed assets of industrial production and the number of people employed in industry. The quality of the developed model is evaluated using the Fisher criterion and the determination coefficient. We use polynomial and exponential to calculate the expected values of independent variables. The predicted value of industrial production indicators was obtained by sequentially substituting the values found into the multiple regression equation. Thus, we have determined the methodological and analytical basis for further research continuation in the chosen direction. As the next steps, we will analyze and evaluate the role of the factor of industrial production and other economic sectors in the regions of Russia’s Northwest, after which the key stage of the work is to develop an integral indicator of the economic dynamics of the regions, which will serve as a leading operational indicator
Keywords
forecasting, vologda oblast, industrial production, economic modeling, Vologda Oblast, regression models, industrial employment