Econometric Modeling Methods Research Articles

The impact of AI on enterprise energy management: from the perspective of carbon emissions

Given the significant impact of artificial intelligence (AI) technology on corporate energy management and the lack of research in this area, this paper employs text mining techniques to objectively assess the relative level of AI adoption among Chinese listed companies. Using econometric modelling methods, we verify these hypotheses and investigate both the direct and indirect effects of AI on corporate carbon emission intensity. our research finds that the carbon emission intensity of Chinese enterprises significantly decreased in the early stage, then stabilized, and has notably decreased again in recent years. The average level of AI among listed Chinese enterprises shows an overall upward trend, but the growth rate has slowed down. The level of AI in private enterprises is significantly higher than that in other types of enterprises, while the level of AI in state-owned enterprises is relatively lower. The level of AI in enterprises has a significant negative impact on carbon emission intensity, presenting an “S”-shaped relationship, characterized by initial emission reduction, mid-term rebound, and subsequent emission reduction. AI technology reduces the level of carbon emissions in enterprises by enhancing their green development standards and promoting technological innovation. There are significant differences in the impact of AI levels on carbon emission intensity across different types and regions of enterprises. The empirical conclusions remain robust after addressing endogeneity issues or variable substitution. This study provides important insights for corporate energy transitions and sustainable development, as well as for the formulation of government energy policies.

Analysis of Female Employment Situation Based on Data from Jilin Province in China

In recent years, Chinas GDP has continued to grow, yet the labor share has not increased in a consistent manner. This paper employs an empirical analysis based on data from Jilin Province, China. It uses questionnaires and econometric modeling methods to investigate the role of gender in economic life and the role of other factors (such as employment pressure, the role of unions, etc.) in this process. The questionnaire survey examines the current state of gender employment discrimination in China, focusing on the existence, causes, and influencing factors of employment discrimination. In constructing the econometric model, this paper assumes a negative relationship between the proportion of female employees and the labor share, while there is a positive relationship between female education level and the labor income share. The two hypotheses, based on common sense and literature, are then tested by an econometric model. The results indicate that the proportion of female employees is indeed negatively correlated with the share of labor income, suggesting that women face more significant employment challenges.

Determining the impact of the CRM system on the financial indicators of the enterprise using econometric methods

CRM systems help to improve customer relationship management, provide an opportunity for more accurate sales forecasting, optimization of marketing campaigns and increase in overall customer satisfaction. The purpose of the paper is to determine the impact of the CRM system on the financial indicatots of the enterprise. The study is based on the methods of econometric modeling, system analysis, financial analysis, works of domestic and foreign scientists, materials from periodicals, and data from the financial statements of the enterprise. A correlation analysis was conducted between the selected independent indicators to determine the degree of their relationship. Based on this, it was determined which factors have the highest correlation with each other, which made it possible to focus on the most important aspects of financial management. A regression multivariate model of the relationship between such factors as inventory, accounts receivable, sales costs, other operating expenses, and the resultant indicator - net working capital (NWC) - is built. The significance of the model as a whole and the significance of the regression coefficients are determined. It is predicted that the net working capital of the enterprise in 2023 will be less than in 2022, which is due to factors such as a high level of accounts receivable, sales costs and other operating expenses. After the integration of the CRM system, the company implemented a number of measures aimed at improving the management of these factors. In particular, the company introduced automation of receivables management processes, optimized sales and other operating expenses. As a result of these measures, the company's NWC increased at the end of 2023. This is confirmed by both the econometric model and the actual results.

АНАЛИЗ ТЕНДЕНЦИЙ В ДИНАМИКЕ ТРЕХ КЛЮЧЕВЫХ СОЦИАЛЬНЫХ ПОКАЗАТЕЛЕЙ ЭКОНОМИКИ РОССИЙСКОЙ ФЕДЕРАЦИИ (ПО ДАННЫМ ЗА 2005-2022 ГГ.)

The work is devoted to the development of methods and computer tools for the analysis of key socio-economic indicators of the Russian Federation, per capita monetary income, expenses and wages of employees of the organization, based on statistical data for 18 years (for 2005-2022). To analyze the relationships and trends of key indicators, both classical statistical methods were used (visualization using various graphs and calculation coefficients), as well as methods of econometric modeling. The developed tools and computer model are based on the identification of relationships and dependencies between time series of key socio-economic indicators using methods of linear, hyperbolic, power, exponential and parabolic correlation and regression. The methodology also includes the calculation and analysis of coefficients characterizing the ratio of wages to income; expenses to income and expenses to wages, as well as visualization and analysis of changes in these relations for 2005-2022. Quantitative analysis in economics involves the development and comparison of a large number of options that differ in the initial data, the form of communication and other parameters. In this case, it becomes necessary to repeatedly perform the same computational operations. The computer model developed by the authors, based on MS Excel, allows you to automate the execution of the same type of calculations for various indicators and their ratios. The proposed toolkit has been developed and tested on the example of three key social indicators of the economy of the Russian Federation for 2005-2022). For all three key social indicators under consideration, it was possible to construct statistically significant models of various types, quantitatively characterizing their relationship, which can be assessed as positive and quite close.

ПЛАТІЖНИЙ БАЛАНС: МЕТОДИ ПРОГНОЗУВАННЯ

The article develops a model for forecasting the main items of the balance of payments, makes a forecast of the balance of payments of Ukraine, and develops recommendations on ways to improve the accuracy of the forecast of the balance of payments. An analysis of current trends in the development of Ukraine’s foreign trade, as well as the impact of global economic processes on the country’s balance of payments, has been carried out. Various methods of econometric modeling have been used to estimate future changes in the balance of payments. The main factors influencing the dynamics of exports and imports, in particular, fluctuations in world prices for raw materials, changes in international trade and political risks, are studied. The results of the study allow us to identify the most vulnerable items of the balance of payments and develop recommendations for the government and business to increase the sustainability of the country’s foreign economic activity. Considerable attention is paid to the analysis of the impact of internal economic reforms on the balance of payments. It is determined how these reforms can affect international transactions and the overall balance of payments. The issues of regulation of financial flows and their impact on the balance of payments, in particular, the role of international investments in the formation of the balance of payments, are considered separately. The measures that can be implemented to stabilize financial flows and reduce the negative impact of external factors on the country’s economy have been studied. Analyzing the relationship between sustainable development strategies and the balance of payments, the article examines how different initiatives affect international trade and investment. The recommendations presented in the article are aimed at increasing the stability and competitiveness of the Ukrainian economy, as well as strengthening its position in the international market. The developed methodology for forecasting the balance of payments allowed to build a model for forecasting the balance of payments, which provides for the study of each of the items of the economy, taking into account the factors that influence them both from the domestic economic side and from abroad.

Econometric Modeling of Environmental Pollution Factors and Their Impact on Life Expectancy in Uzbekistan

The article examines the impact of environmental pollution factors on life expectancy in Uzbekistan. The study is based on econometric modeling methods, including panel data on the country’s regions for the period of 2010-2023. The area of protected natural areas, the volume of pollutant emissions into the atmosphere, the provision of doctors and hospital beds, and access to drinking water were utilized as factors influencing life expectancy. Despite the identification of some relationships, the modeling results showed limited explanatory power of the selected models, which is associated with the low quality and incompleteness of statistical data. The study revealed significant regional differentiation and polarization in environmental indicators, which requires measures to improve the quality of data and take into account additional factors.

Assessment of the impact of socio-economic indicators of the Volga Federal District of the Russian Federation on the number of payers of the Professional income tax

This article examines the impact of socio-economic indicators of the Volga Federal District (hereinafter – VFD) on the number of payers of the special tax - «Professional income tax (hereinafter – PIT)». The relevance of the article is due to the need to assess various socio-economic effects that cause an increase in the number of PIT payers in the regions of the Russian Federation. The purpose of this article is to identify these socio-economic indicators and then assess their impact on increasing the number of PIT payers in the region, which allows us to develop measures aimed at tax management in a particular region. Using econometric modeling methods, the authors assessed the impact on the number of PIT payers of such socio-economic indicators of the regions of the Volga Federal District as the number of individual entrepreneurs (hereinafter referred to as sole proprietors), GRP, the execution of regional budgets on PIT revenues, unemployment and nominal accrued wages. The materials used in the research process were obtained from open sources of official state statistics, consolidated tax and budget reports. The main results of this study are: - the indicator of the number of sole proprietors has a positive relationship with the number of PIT payers, which allows us to conclude that in regions with a larger number of individual entrepreneurs, the number of NPD payers is also growing; - the indicator of the number of unemployed has an inverse relationship with the number of PIT payers in the regions of the Russian Federation; Based on the results of an econometric study, the author concludes that a number of socio-economic indicators of the regions of the Volga Federal District have a significant impact on the number of PIT payers.

ПРОГНОЗ ФАКТОРОВ, ОКАЗЫВАЮЩИХ ВЛИЯНИЕ НА ПОКАЗАТЕЛИ ИННОВАЦИОННОЙ АКТИВНОСТИ РЕГИОНОВ РОССИИ

the article examines the problem of limitations of existing rating models in forecasting and analyzing the development of innovation processes in a regional context due to limited possibilities for integrating various factors, such as educational systems and financial instruments. The need to develop and implement new forecast models that take into account multiple aspects related to education and the financial component of regional development is emphasized. The study also addresses the importance of socio-economic development in determining the readiness of regions to introduce innovations and create an innovative economic environment. The article presents an analysis of the factors influencing the innovative development of regions, with an emphasis on the level of monopolization of the market environment as an important factor affecting the dynamics of innovation, and also puts forward proposals for stimulating technological innovation and ensuring their financial support to improve financial stability at the state and regional levels. Using econometric modeling methods, the authors assess the impact of various variables. Studying the impact of these factors on the dependent variable allows us to determine their significance for the economic process and the development of resource management strategies. It is shown that investments in innovative research, human resources, educational structure and technological parameters of production have a significant impact on innovative activity. The study is conducted using econometric modeling techniques, including multiple linear regression and short regression models to identify significant factors. Models are checked for compliance with the conditions of the Gauss-Markov theorem, heteroscedasticity and autocorrelation of residuals using appropriate statistical tests. The results of the analysis confirm the statistical significance and adequacy of the models, and also allow us to identify key factors. At the same time, opportunities for improving the coordination of actions to develop the innovative potential of regions are identified based on the results of econometric modeling.

Modeling of Functional Relationships of Regional Economic Systems Based on Small Samples Based on Bayesian Intelligent Measurements

Regional economies, which are economic subsystems of a socio-ecological-economic system, are characterized by rapidly changing conditions affecting their functioning. Therefore, traditional approaches based on long-term trends and the construction of appropriate models are not enough for modeling and need to be over updated, including through combination with other approaches that take into account the volatility of conditions. Open sources datasets are fraught with uncertainty (different sources may have different values for the same indicator). So, some indicators are being constantly adjusted: changes are introduced into datasets from the previous periods; evaluation periods are not sufficient for model construction that would be methodologically valid from the point of view of mathematical statistics, Additionally, the results of functioning depend on influencing factors as well as measurement results. These problems lead to the need to resort to probabilistic estimates and the incompleteness and fuzziness dataset, as well as to obtain samples of sufficient volume from low-power data. The purpose of the study is to model the relationship between the volume of gross domestic product and the factors characterizing the functioning of the economic subsystem of the regions, under the conditions of uncertainty and limited data in a given period of time. The case study is constructed on The Tula region dataset. The working hypothesis is that it is possible to construct a methodologically correct econometric models using the dataset of a specific region of the Russian Federation over a certain period of time. We applied the methods of econometric modeling and the methodology of Bayesian intelligent measurements, as well as the methodology for generating a sufficient amount of data on small samples. For the Tula region and dataset for 2022, nonlinear models for the GDP by region volumes (14 sections of the NACE) were built. This made it possible to test the presented methodology based on a combination of econometric and Bayesian approaches to modeling the complex systems functioning, with conclusions having both theoretical and practical value for the region's sustainable development.

Research on Digital Economy Driving High Quality Development of China's Manufacturing Industry

With the profound changes in the global industrial economic situation, the industrial competition pattern is further adjusted, China's original reliance on the "demographic dividend" effect of the manufacturing industry expansion path is facing the challenge of low-end locking and marginalization, the urgent need to cultivate new kinetic energy. Get rid of the inherent low-cost competition and scale expansion mode, promote the digital transformation of the manufacturing industry, are the road to high-quality development of the manufacturing industry under the new development pattern. On the one hand, the digital economy has reconstructed the layout of the manufacturing industry chain in digital space and physical space, effectively unimpeded the domestic macrocycle; on the other hand, the digital economy can promote China's manufacturing industry to the high-end of the value chain, and win a larger profit space and the right to speak for the manufacturing industry to integrate into the international cycle. Therefore, under the new development pattern of internal and external double cycle, digital economy has become the new kinetic energy for the upgrading of China's manufacturing industry. This project combs through the domestic digital economy to drive the high-quality development of the manufacturing industry related literature, summarizes the economy to drive the high-quality development of the manufacturing industry measurement method, organizes the recent years of China's manufacturing industry development related research, consolidates the research foundation and expands the research perspective, constructs the "scale - structure", We construct a four-dimensional analytical framework of "scale-structure" and "circulation-external circulation" for the high-quality development of manufacturing industry, and explore the theoretical mechanism of digitalization-driven high-quality development of manufacturing industry. Through the use of econometric modeling methods to carry out empirical analysis of the digital economy driven manufacturing high quality, according to the empirical results, more scientific formulation of the digital economy driven China's manufacturing industry upgrading countermeasures recommendations.

Forecasting Household Energy Consumption in European Union Countries: An Econometric Modelling Approach

The article raises issues regarding the consumption of energy from both fossil and renewable sources in households. The research was carried out on the basis of data obtained from the Eurostat database, which covered the period from 1995 to 2021 and concerned the European Union countries. Increasing energy consumption and, thus, increasing household expenses affect their standard of living. The purpose of the analysis was to construct two econometric models for electricity consumption. The first model referred to the consumption of energy from fossil sources and the second from renewable sources. A forecast of energy consumption in households was also constructed on the basis of estimated models. Econometric modelling methods (multiple regression) and time-series forecasting methods (linear regression method, exponential smoothing models) were applied for the study. Research shows that the main factor that models energy consumption in households, both from fossil and renewable sources, is the final consumption expenditure of households (Euro per capita). The set of indicators for the models varies depending on the type of energy source. The forecast shows that the share of energy consumption obtained from fossil sources will decrease systematically, while the share of energy consumption from renewable sources will continue to increase systematically.

Russian Arctic Mineral Resources Sustainable Development in the Context of Energy Transition, ESG Agenda and Geopolitical Tensions

The Arctic region is well-known as a source of energy and mineral resources. Developing these resources by means of responsible practices could provide the world’s economy with strategically and critically important types of minerals. As a result, the role of the Arctic’s resource potential and the approaches used to develop it are gradually changing. In current conditions, it is crucial to strike a balance between existing needs and long-term demand for traditional energy sources or critical materials, such as rare and rare-earth minerals. Achieving this balance will result in sustainable growth, meeting ESG agenda targets while considering national security priorities, and devising novel approaches to sustainable project financing. However, there is controversy between existing strategies and plans based on low-carbon development and reality, which creates new macroeconomic and geopolitical obstacles that need to be overcome. The primary goal of this research is to substantiate the necessity of transforming the Russian Arctic’s mineral resource exploration in the context of global and national changes. The article presents a comprehensive analysis of the contemporary trends and tendencies influencing the exploration of the Arctic’s resources, as well as a general vision of the current situation in the energy sector. Research methods include content analysis and synthesis, generalization, decomposition, elements of strategic analysis, planning and forecasting, statistical methods, econometric modeling methods, and scenario planning. An upgraded concept of sustainable development based on six key components is proposed and discussed. Recommendations are provided on how to improve the existing system of long-term planning and forecasting to reduce the high level of macroeconomic and geopolitical uncertainty. The role of Russian Artic resources is determined in light of the shift in resource use.

Моделирование процессов сложной социально-экономической системы при выборе стратегических приоритетов развития

To change the economic processes and stabilize the socio-political situation, the authorities need an effective set of tools and methods of socio-economic management. They require new and better means of strategic planning that provide a controlled development of the national economy. This research features advanced forms and methods of econometric modeling in the regional socio-economic system. The research relied on the strategizing methodology developed by Professor V.L. Kvint, as well as the Strategist’s Office software that makes it possible to construct mathematical models of economic processes for a particular region. The predictive analytical model covered ten basic economic processes. This article introduces some factors and indicators defined for the transport infrastructure based on the index approach. The experimental part focused on the case of the North-Western and Volga Federal Districts. The set of tasks for regional development strategizing ranges from the theoretical and methodological provisions to the development of a digital twin of a regional economic system designed with the help of tools that provide information support for analysis, assessment, and forecasting. Regional strategizing is considered as a model of a regional socio-economic system defined as a structure that includes a number of autonomous subsystems with certain objective and measurable indicators.

Impact of econometric modeling and perspectives of economic security of the cross-industry complex

The paper presents a forecast of the economic security of the inter-industry complex through the construction of a simulation model. The authors considered the possibility of using an econometric model in predicting the level of economic security of the inter-industry complex. The goal was to form a definition of the "inter-industry complex", as well as to study the issues of conceptual and fundamental methods of econometric modeling and forecasting the development of regional industry markets in dynamics. A range of issues related to the main components of economic security in the inter-industry complex has been allocated for scientific work in order to analyze the impact of the components of economic security on the integral indicator. The paper uses a methodology for predicting the structural and spatial-temporal dynamics of interbranch complexes, which includes new and refined methods of modeling and forecasting. As a result, the authors proposed the definition of "inter-industry complex", "economic security in the inter-industry complex", as well as the general provisions of the methodology for econometric modeling and forecasting the level of economic security of the inter-industry complex. The paper presents a full-scale simulation model that allows you to set, evaluate and make a decision using large nonlinear data. This kind of system contains dynamic and retarded data, which makes it possible to apply econometric modeling in automatic calculation.

Econometric analysis and forecast of the development of the fruit and vegetable network in the conditions of digitalization of the economy

In the article, the dynamics of the development of the fruit and vegetable industry in the republic under the conditions of the digitalization of the economy and the pandemic, the analysis of the statistical indicators of the results achieved in the industry, the specific aspects of the industry and the main directions of the priority development of the fruit and vegetable industry, as well as the issues of the development of the production and export of fruit and vegetable products, are researched. Also, in the article, the gross yield of fruit and vegetable products and the factors that strongly affect it are analyzed and forecasted using the correlation-regression analysis methods of econometric modeling, and practical recommendations and suggestions are given.

Modeling the academic performance of students based on intelligent analysis of educational data

Subject. The article considers the application of machine learning methods to analyze students' academic performance. Objectives. The aims are to identify factors influencing the academic performance of students, detect hidden patterns, useful and interpretable knowledge about the results of educational process and its participants, using the intellectual analysis of educational data. Methods. The study rests on methods of econometric modeling, multidimensional classification, and big data clustering. Results. The developed models of intellectual analysis of educational data enable to perform a comparative analysis of students and forecast the level of student’s mastering an educational program, depending on factors like the total score of entrance tests, average score of academic performance, basis and form of education, course, the level of training, student’s gender and age. Conclusions. The results of the application of machine learning methods to analyze academic performance will help differentiate effective teaching methods and technologies for groups of students with different levels of academic results, timely take corrective actions regarding students from risk group. Eventually, this will contribute to retention of students and improvement of the educational process quality.

Models and Forecasts of the Export Potential of the Regional Economic System

Introduction. The export potential of the regional agricultural market causes great interest from both the scientific community and government agencies. Many scientific studies are devoted to the search for methods to increase the export potential in order to ensure the sustainable development of regional economic systems. The article proposes and tests the original author’s algorithm for creating a dynamic econometric model for forecasting the volumes of production, sales and exports of the main types of livestock products at the regional level. The purpose of the article is to assess the export potential of the main products of the regional agricultural market based on the built dynamic econometric models. Materials and Methods. The research is based on a set of empirical data of result and input variables characterizing the production, sales and export of the main livestock products in the region for the period 2010–2020. Research methods include dynamic analysis, econometric modeling and forecasting. The dynamic analysis carried out made it possible to assess the change in the production, sale and export of the main agricultural products in the region, to assess the current trends. Based on the constructed econometric models, the most significant factors influencing the resulting variables were identified, the specifications and verification of the models were carried out. The method of extrapolation of the identified trends made it possible to evaluate the predicted values of the resulting variables for the medium term. Results. Based on the selected input variables, models of production, sale and export of milk, wool and eggs by agricultural producers in the region were built. From a variety of alternative models, models with the best statistical quality characteristics were selected. The high level of quality of the obtained models made it possible to use them for predictive calculations of the levels of resulting variables for the period 2021–2026. Comparison of the results of the forecasts made it possible to identify the types of livestock products that already have a high level of exportability. In addition, types of products with a low level of exportability were identified, which have a high potential for increase. Discussion and Conclusion. As a result of using econometric modeling methods, dynamic models were obtained that made it possible to obtain a forecast for the development of livestock in a region with a high export potential in the near future. The practical significance of this article lies in the possibility of influencing the production, sale and export of livestock products in the region through a change in the corresponding set of factor variables of the models. Thus, the resulting dynamic models can be used both by agricultural producers for planning economic activities, and by regional authorities when drawing up regional development programs.

Examining the Regional Disparity of Agricultural Development: A Distribution Dynamics Approach

Many scholars have argued that the increased disparity in agricultural development among countries is the crux of the regional inequality problem and exerts adverse effects on individuals’ well-being. This study thus aims to examine the distribution dynamics of global agricultural development. Specifically, it examines whether the disparity in agricultural development among countries vanishes over time and whether convergence can be achieved. On that account, a new distribution dynamics analysis method based on the stochastic kernel approach is employed at the country level. The proposed model can address the inadequacies of traditional econometric modelling methods and visualisation tools in the distribution dynamics literature. The research outcomes are as follows. Firstly, the global agricultural income disparity is primarily due to the Global South countries’ low productivity level, which lowers the world average, indicating that these countries need more fiscal and financial aid from Global North countries to boost their agricultural sector productivity levels. Secondly, regarding income division, upper-middle-income countries have been above the average output levels, indicating the invalidity of the middle-income trap for these countries’ agricultural sectors. This finding suggests that increased investment in agricultural productivity can overcome the middle-income trap. Thirdly, from a geographical perspective, Europe, Central Asia, and North America have a technological edge in the agricultural sector. In contrast, East Asia and Pacific countries have the potential to boost agricultural sector productivity. As a result, this study helps policymakers to design better schemes to improve the development of agriculture for each group and country type to improve the development of agriculture for each group and country type.

Instrumental analysis of the impact of economic factors on the level of a country’s economic innovativeness

The rapid development of innovative activity in Ukraine and the instability of the economic and political environment determine the relevance of studying the problems of innovative development in close connection with global and local factors of economic growth. The purpose of the research is to study the budgetary policy factors that have a dominant influence on the level of national innovative development, and their impact on the level of economic growth in order to form directions for innovation policy development based on instrumental analysis methods. The paper uses methods of econometric modeling and adaptive methods of forecasting. The constructed conceptual model includes three aggregated stages of research. The study addressed the problem of assessing the level of a country’s economic innovativeness and determining the factors affecting it; it was found that this level grows with an increase in the share of private investment, a decrease in the rate of GDP growth, an increase in the share of taxes in the budget structure, a decrease in the money supply, an increase in education and public security expenditures. The impact of the state budgetary policy was analyzed and the value of the indicator of economic growth and the level of the country’s economic innovativeness was forecasted. The practical significance of the results is that a gradual reduction in the level of the country’s economic innovativeness is possible in periods of long-term economic growth. To avoid such negative consequences, European governments should pay more attention to internal problems and external trends of innovation activity

Эконометрическое моделирование продуктовых границ рынков

The relevance of the study is due to the special importance of correctly determining the product boundaries of the cross-border markets of the Eurasian Economic Union (EAEU) in the process of antimonopoly regulation. An erroneous definition of such parameters leads to an incorrect definition of the product, and, as a result, to a narrowing or expansion of the scope and geography of its circulation, an increase or blurring of the shares of entities operating in the markets, a false level of concentration, as well as a distortion of the criteria for delineating the competence of the EEC and national authorities in terms of abuse of dominance. This process can be much more complicated when it comes to goods that are subject to technical regulations, and due to their specificity, there are a significant number of types of goods that are generally similar in some respects, but differ in others. In this regard, within the framework of the study, an attempt was made to assess the possibilities of using certain methods of econometric modeling when establishing product boundaries for cross-border markets for such goods using the example of non-electric initiation systems. The research hypothesis is based on the following assumptions. First: technical regulations and standards not only act as market entry barriers, but also have a decisive influence on the definition of a market within which goods equivalent to each other circulate. Second: when determining the product boundaries of markets operating in the system of technical regulation, along with traditional and normatively fixed approaches, multiple regression models can be used and a correlation between the cost of a product and its characteristics, as well as empirical analysis data that allows segmenting goods according to specific boundaries, can be used.