Abstract

AbstractIn the course of the research, the authors formed a factor model for the assessment of the regional investment potential, model specification, and its parameters assessment, which check the quality and interpretation of the results obtained. The authors defined the indicators characterizing manufacturing, demographic, credit, fiscal potentials of the region, and its activity results. The factor analysis for the investment potential is conducted on the basis of regression formulas with the addition of stochastic values. To neutralize the impact of regional disproportions on the factor model of investment potential, the authors clusterized the data obtained by k-means methods and identified four clusters. Within the identified clusters, the authors defined the determinants capable of influencing the distribution of the all-Russian regional investment potential. The factor model quality was assessed for an individual cluster on the basis of the determination factor and the average standard error. The factor regressive model of the investment potential integrating the data for all regions not divided into cluster contains the result-based values only as explanatory variables. They are the balanced financial result and the volume of manufactured goods sold. No resource factor is significant. The regressive model built with the adjustment to the heteroscedasticity helped to determine resource factors significantly affecting the investment potential, that is, demographic factors, consumption of households, and the share of knowledge-intensive industries in the GDP. The wholesome regressive analysis of other clusters is not deemed to be possible because of data lack. However, we were able to find out that the determinates with the value that is significantly deviated from average Russian values have a significant effect on the investment potential.KeywordsInvestment potentialRegionMonte-Carlo methodRegression modelClusterK-means method

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