Methodology сomparative statistical analysis of Russian industry based on cluster analysis - Sergey S. Shishulin (Статистика и экономика, 2017)
The article is devoted to researching of the possibilities of applying multidimensional statistical analysis in the study of industrial production on the basis of comparing its growth rates and structure with other developed and developing countries of the world. The purpose of this article is to determine the optimal set of statistical methods and the results of their application to industrial production data, which would give the best access to the analysis of the result.Data includes such indicators as output, output, gross value added, the number of employed and other indicators of the system of national accounts and operational business statistics. The objects of observation are the industry of the countrys of the Customs Union, the United States, Japan and Erope in 2005-2015. As the research tool used as the simplest methods of transformation, graphical and tabular visualization of data, and methods of statistical analysis. In particular, based on a specialized software package (SPSS), the main components method, discriminant analysis, hierarchical methods of cluster analysis, Ward’s method and k-means were applied.The application of the method of principal components to the initial data makes it possible to substantially and effectively reduce the initial space of industrial production data. Thus, for example, in analyzing the structure of industrial production, the reduction was from fifteen industries to three basic, well-interpreted factors: the relatively extractive industries (with a low degree of processing), high-tech industries and consumer goods (medium-technology) sectors. At the same time, as a result of comparison of the results of application of cluster analysis to the initial data and data obtained on the basis of the principal components method, it was established that clustering industrial production data on the basis of new factors significantly improves the results of clustering.As a result of analyzing the parameters of data partitioning into clusters using k-means and hierarchical methods using different distances, it was determined that the best result is obtained when using a combination of these methods, when in the first stage the number of clusters is determined by analyzing the visualization of hierarchical algorithms (dendrogram construction) , On the basis of which the division by the method of k-means is made. At the same time, a significant improvement in the quality of the partition is achieved by eliminating the emissions in the clustered data, and then including them in the analyzed set using discriminant analysis.The application of this approach to the data of the structure of industrial production ensured good results. The resulting clusters are uniform in composition and meaningfully interpreted: the first cluster includes countries with low rates of output of the extractive industry relative to the cumulative output of the economy, with a sufficiently high value of this indicator in other sectors. In general, this group can be designated as a country with a developed industrial production of a high-tech type. The second group of countries with respect to other groups is characterized by a generally low share of industry in the economy, and in particular by lower rates of extractive industries. The third group of countries includes countries with a high resource base, which is characterized by a high share in the output of extractive industries.