Investment attractiveness modeling using multidimensional statistical analysis methods
| Autor: | Dean Linok, Volodymyr Shinkarenko, Maksym Matskul |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Attractiveness
05 social sciences 050301 education Latent variable Investment (macroeconomics) Objective assessment lcsh:Social Sciences lcsh:H Principal component method 0502 economics and business Principal component analysis Economics Econometrics Statistical analysis Set (psychology) 0503 education 050203 business & management |
| Zdroj: | SHS Web of Conferences, Vol 65, p 04007 (2019) |
| ISSN: | 2261-2424 |
| Popis: | The article examines the investment attractiveness of the main branches of the food industry of Ukraine as a latent variable. For the first time in this area, a combination of various methods of multivariate statistical analysis is used for research (cluster analysis and factor analysis – the principal component method). These methods made it possible to use a large number of various indicators of the activities of industries to characterize investment attractiveness. As a result, the set of the branches was divided into three groupsclusters: “leaders” are the most attractive sectors for investment, “middle peasants” are attractive branches for investment, and “outsiders” are the least attractive branches for investment. The generalizing factors (principal components), which influence the resulting factor - investment attractiveness, were found. The interrelation of the generalizing factors and initial indicators is established. As a result of the research, it was possible to make an objective assessment of the investment attractiveness (as a latent indicator) of the main branches of the food industry in Ukraine, using instead of a multitude of indicators only three latent factors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|