Process of ranking countries by level of development

The paper presents the process of ranking and classifying countries using the I-distance method. The I-distance method is a method of classification and multidimensional ranking based on the distance values between selected indicators. The selection of indicators was carried out using the principal components analysis, whereby the statistical software SPSS (Statistical Package for Social Sciences), the latest version 21th PASW Statistics, is used. The application of the I-distance determines the relative efficiency indicators. Classification and ranking are conducted based on the economic development using macroeconomic indicators for the selected European countries

I distance application in the ranking of Group 8 and European Union countries by level of development

According to the analyses published by the international organizations, the most developed countries are those from Group 8. The group of highly developed countries is in matter, which consists of: Japan, USA, Russia, Great Britain, Italy, Germany, France and Canada. The goal of the work is to determine the ranking list of the selected countries according to the level of development in 2021 based on a certain number of macroeconomic factors. For the purposes of realizing the formulated goal, the I distance method was applied. A decision for the I distance method comes from the fact that this model satisfies all the conditions characteristic for the nature of distance, that is, for the multidimensional phenomenon of development. Based on the ranking list of Group 8 countries, the United States of America is in the first place, followed by Germany, France, the United Kingdom, Italy, Canada, the Russian Federation and Japan. Speaking about the EU countries, the Netherlands has the highest level of development according to the selected indicators, followed by Ireland, Belgium, Spain, Poland, Sweden, Austria, Denmark, Czech Republic, Luxembourg etc. The coming future will probably bring changes when it comes to the ranking on the ranking list. Changes can be expected due to the war events, demographic trends, technological achievements, and generally the replacement of the leading positions when it comes to resources. Namely, it is certain that the countries that adapt faster to other energy sources as well as to more economical use of the existing ones, will have a leading role on a global scale

Fuzzification - Decision Making in Terms of Uncertainty

The theory of fuzzy sets allows to analyze insufficiently precise, accurate, complete phenomena which can not be modeled by the theory of probability or interval mathematics. We define fuzzy sets as sets where the boundary of the set is unclear and depends on subjective estimation or individual preference. In addition to the standard interpretation scale, described above, a set of numbers to each qualitative attribute must be assigned. In addition to the standard interpretation scale a set of numbers to each qualitative attribute must be assigned. First of all, it is necessary to determine the procedure for determining fuzzy numbers describing the attributes. One of the imperfections of the fuzzy sets is subjectivism when defining the boundaries of fuzzy sets and functions of belonging, which can significantly influence the final decision. The decision maker’s subjectivity is also present in the determination of weighted coefficients. However, in case of giving weight, fixed values are necessary. Some decisions require multidisciplinary knowledge, so the decision-making process includes more group decision-makers, who independently give their grades

(In)consistency of the Selection of the Method of Multiplicative Decision-Making

The paper analyzes the main causes of the (in)consistency of the selected method of multiplicative decision-making: data normalization, weight coefficients and the application of the Likert scale for the purpose of measuring quantitative attributes. Normalized data in the methods of multitributive decision making represents the substitute for a subjective attribute ratings by decision makers. Since they are calculated on the basis of mathematical transformations of empirical data, one gains the impression that the choices basen on normalized values are „objective”. Therefore, the sensitivity analysis of the results has dealt exclusively with effects of weight coefficients on the final choices so far, while the potential impact of normalization is complitely ignored; meanwhile, the deformations caused by the normalization of data have been attributed to the effects of weight coefficients and their inevitable subjectivism. We intent to point out at the deformations of empirical values that are the result of normalization and which call into question the application of normalized values as a decision base. It can be proven that the normalized values are an unrealiable information base for decision-making. In addition, the (in)consistency of selection methods of multi-attributive decision-making is also influenced by changes in the method of measuring and formulating attributes

Analysis of Macroeconomic Factors Effect to Gross Domestic Product of Bosnia and Herzegovina Using the Multiple Linear Regression Model

This paper presents the application of the multiple regression analysis model in macroeconomic research using the model of Bosnia and Herzegovina in the period from 2005 to 2018. The objective of the research is to evaluate the effects of macroeconomic factors (independent variables) to gross domestic product (dependent variable), and based on theoretical and methodological research. Applying the Enter method, out of six independent variables, they are all included in the regression model, whereas the sequence of inclusion in the model is the following: foreign direct investments, Import, Export, Growth rate, unemployment and inflation. Numerous research indicate positive connection between gross domestic product as the dependent variable and foreign direct investments, Import, Export, Growth rate, unemployment and inflation, as independent variables. Other factors negligibly explain the most important indicator of economic activities of a country. Our assignment is to either confirm or reject the abovementioned statement

SPECIFICS OF THE APPLICATIONS OF MULTIPLE REGRESSION MODEL IN THE ANALYSES OF THE EFFECTS OF GLOBAL FINANCIAL CRISES

This paper aims to present the specifics of the application of multiple linear regression model. The economic (financial) crisis is analyzed in terms of gross domestic product which is in a function of the foreign trade balance (on one hand) and the credit cards, i.e. indebtedness of the population on this basis (on the other hand), in the USA (from 1999. to 2008). We used the extended application model which shows how the analyst should run the whole development process of regression model. This process began with simple statistical features and the application of regression procedures, and ended with residual analysis, intended for the study of compatibility of data and model settings. This paper also analyzes the values of some standard statistics used in the selection of appropriate regression model. Testing of the model is carried out with the use of the Statistics PASW 17 program

SPECIFICS OF THE APPLICATIONS OF MULTIPLE REGRESSION MODEL IN THE ANALYSES OF THE EFFECTS OF GLOBAL FINANCIAL CRISES

This paper aims to present the specifics of the application of multiple linear regression model. The economic (financial) crisis is analyzed in terms of gross domestic product which is in a function of the foreign trade balance (on one hand) and the credit cards, i.e. indebtedness of the population on this basis (on the other hand), in the USA (from 1999. to 2008). We used the extended application model which shows how the analyst should run the whole development process of regression model. This process began with simple statistical features and the application of regression procedures, and ended with residual analysis, intended for the study of compatibility of data and model settings. This paper also analyzes the values of some standard statistics used in the selection of appropriate regression model. Testing of the model is carried out with the use of the Statistics PASW 17 program