Authors

Problems of dynamic modeling of the commissioning of residential buildings in the post-Soviet countries

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. Read more...

Using econometric models to forecast fixed asset investments

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. Read more...