The article is devoted to the description of procedures of economic and mathematical modeling of trends in the field of housing construction taking into account the peculiarities of various countries of the post-Soviet space. The results of analysis of well-known scientific publications on forecasting the dynamics of housing market indicators are presented. It has been shown that most domestic and foreign scientists as the most effective methods of modeling these indicators consider methods of analyzing time trends, in which polynomials of high (in some cases up to the fourth degree) order are used to approximate the available retrospective data. Other common approaches to solving this problem are the use of short-term forecasting based on moving average algorithms, as well as the use of the SARIMA model, which takes into account the trend and seasonal wave. The article shows that these methods do not fully take into account the profound changes in the construction complexes of the post-Soviet states caused by the significant structural transformation of their socio-economic systems. The authors proposed to use econometric models based on regressions with dummy variables to model the main indicators of housing construction, taking into account the complex structure of the external and internal environment of national construction complexes. It has been shown that in a significant number of practical situations, a fairly simple but effective way to take into account the components of the time series of the indicators under consideration in one complex model is to use the model of "change in growth (fall)" when choosing the time of the beginning (end) of a crisis situation as a characteristic point. The results of modeling the main indicators of housing construction for various countries of the post-Soviet space showed that the proposed model when constructing the medium-term forecast allows taking into account the situation component of the analyzed time series.
One of the key factors in the country’s GDP growth is reproducible capital, which lays the foundation for the production of products, works and services. Accordingly, the study of the state, structure and dynamics of the dominant component, fixed assets, is one of the priority tasks of statistics and econometrics. This implies the purpose of the study, which is to assess the predictive capabilities of econometric models. To achieve this goal, a pool of mathematical-statistical and econometric methods was used, in particular tabular and graphic, descriptive statistics, correlation-regression, adaptive modeling. The main results include: analysis of the structure of investments did not find new or hidden patterns, so investments are directed to the modernization or renewal of capital-intensive areas – these are buildings, structures and land (about 40% of the total investment), the main industries are industry and transport; visual analysis of the dynamics of the temporary series of investments in fixed assets showed the presence of a long-term, seasonal and situational component; the construction of 6 econometric models reflecting the complex dynamics of the macro indicator in question made it possible to distinguish two adaptive models belonging to the group; thus, the best forecast opportunities for complex dynamics of investments in Russian fixed assets are observed in the three-parameter exponential smoothing model and SARIMA (1,0,0)(1,1,0) [4]. The results obtained in the course of the study will be useful for scientists involved in modeling and predicting complex-structured time series
The article is devoted to the consideration of modern, innovative methods of promoting a new product on the market, which is a tool for strengthening the competitiveness of an enterprise, its efficiency in a dynamic economy. At the first step of the work, the authors consider and analyze practical methods and tools for creating and promoting new goods and services on the markets that meet modern consumer needs. The considered tools are aimed at expanding existing and developing new market niches. The article substantiates their ability to increase brand loyalty, while reducing possible risks. The next step of the work was the development of a model for promoting a new product in the company’s marketing system using the example of a specific enterprise, namely JSC NPP Almaz, the first stage of which was an analysis of the financial and economic activities of the enterprise in order to determine the possible costs of creating and promoting a new product. The authors draw attention to the possible risks when creating a new product and give recommendations on how to reduce them. Based on the analysis, the last stage of the work was the development of specific recommendations for modifying the tools for promoting a new product in the enterprise marketing system. The main base of the modification is the synthesis of traditional channels of product promotion, such as personal sales, participation in exhibitions, radio advertising with innovative Internet tools, among which are SEO optimization, SMM management, contextual advertising. The authors give arguments justifying the use of community marketing functionality in order to improve the quality of promotion of new products through social networking channels. As a result, an assessment is made of the possible economic effect of the proposed scheme for promoting a new product on the market and occupying new product niches.
In this paper, the authors conduct a comparative analysis of instrumental methods used in modeling stochastic processes, namely, component analysis of time series, fractal modeling and modeling using p-adic mathematics. As an object of study, the authors chose the dynamics of the MICEX index. At the first step of the work, the authors carry out a detailed component analysis of the time series, which made it possible to identify the main development trend in the form of a quadratic function; periodic fluctuations with a period of six levels and a cyclical component describing fluctuations in the world economy with a period of fifty-five levels. At the second step of the work, the authors simulate the dynamics of the MICEX index using a fractal theory based on the self-similarity of the development of the economic process, which showed the ergodicity of the series under study with a stable influence of only the last twenty-four levels. The third step of the work was the p-adic modeling of the patterns existing in the series under study, which allowed the authors to reduce the model error to 6.8%. As a result of the work, a forecast of the dynamics of the MICEX exchange rate at four levels is presented, presented in three scenarios: optimistic, realistic and pessimistic. As conclusions of the work, an analysis was made of the possibility of using the considered methods for multiple, medium and long-term forecasts; the complexity of the methods and the need to use special software products are evaluated.
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