MODERN PROBLEMS OF APPLYING MATHEMATICAL METHODS IN THE ASPECT OF ECONOMICS

The penetration of the mathematical apparatus into the economy created the basis for the development of methods of economic analysis, econometrics, mathematical programming, economic statistics, etc. Today, the interpenetration of different branches of knowledge continues, in particular, the application of mathematical methods in the natural and social sciences and in the economic sphere. Among mathematical methods of data processing are polynomial, linear, quadratic, trigonometric, exponential and combined dependencies, differential and algebraic equations. The statistical processing of data from the evaluation of the structure and dynamics of the phenomenon has gone in the direction of correlation analysis and forecasting. The deep penetration of mathematics into specific sciences and the success achieved through a combination of methods from different branches of knowledge is described by many researchers. The possibilities of applying mathematics are increasingly being explored in areas of knowledge where phenomena are poorly structured and characterized by the high complexity of sociology, political science, management and economics. The article presents a retrospective analysis of the development of scientific and applied research concerning the process of mathematics of science and the possibilities of using mathematical methods in economics in particular. Problems and constraints encountered in applying mathematical methods in economic research have been identified. Measures have been identified to ensure the adequacy of the development of economic and mathematical models from the standpoint of approaches to their construction, the improvement of management processes and the improvement of the training of specialists in economic fields.

Measuring the ICT Sector in the Digital Economy

The article addresses the problem of measuring the Information and Communication Technology (ICT) sector and its relationship to the digital economy as defined in the OECD «Guidelines for Supply-Use Tables for the Digital Economy». Analysis of various concepts of the digital economy shows that the ICT sector is its key element. It is stressed that, in line with the OECD recommendations, the measurement of the digital economy should be based on the SNA satellite account, the core element of which are the Digital Supply-Use Tables (Digital SUTs). This approach enables to reflect most fully within a single statistical model integration of all phases of digital products (goods and services) circulation in the national economy.It is noted that the construction of ICT Supply-Use Tables (SUTs) is a critical self-contained task, as it provides a measurement framework for both digital (regardless of the model to be adopted) and (more broadly) information economy.It is stressed that the ability to capture the use of ICT products as well as the cost of their production depends significantly on the identification and valuation of digital products and industries in the Digital SUTs. The identification of such industries is now a major challenge because of the lack of separate activities in the existing industrial classifications that are characteristic for their constituent units.The article concludes with a presentation of the pilot estimates of ICT Sector SUTs core indicators obtained by the authors at the Economic Statistics Centre of Excellence, HSE University. The contribution of the sector to the GDP of the Russian Federation is analysed and its inter-sectoral linkages, both on the demand and supply side, are assessed. It is concluded that the full implementation of the Digital SUTs depends on the inclusion in the new International Standard Industrial Classification (ISIC) the additions necessary to identify and evaluate digital products and industries.

Adaptation to the Risks of Digitalization: New Survival Trends for States in a Multipolar World

The purpose is to study the new survival trends for states in a multipolar world, determine the successfulness of adaptation to the digitalization of different growth poles, and develop the applied recommendations to improve the practice of adaptation to the risks of digitalization of these growth poles. Design/methodology/approach. The authors use the methods of economic statistics: variation analysis, trend analysis, correlation analysis, and regression analysis. Findings. The commonness of strategies of adaptation to the risks of digitalization for different poles of the world economy is substantiated, and two universal mechanisms—talent management and development of science—are found. The originality of this research is due to the consideration of digitalization from a new view—from the positions of setting states at the brink of survival due to the aggressive digital competition and high complexity of ensuring global competition in a quickly changing digital landscape. The uniqueness of this research is due to taking into account the specific features in a multipolar world. The practical implementation of the offered recommendations opens future perspectives for more successful survival trends in a multipolar world and the improvement of their adaptation to risks digitalization by 69.91% in G7 countries (on average) and by 88.40% in BRICS countries (on average).

Geospatial Data Disaggregation through Self-Trained Encoder–Decoder Convolutional Models

Datasets collecting demographic and socio-economic statistics are widely available. Still, the data are often only released for highly aggregated geospatial areas, which can mask important local hotspots. When conducting spatial analysis, one often needs to disaggregate the source data, transforming the statistics reported for a set of source zones into values for a set of target zones, with a different geometry and a higher spatial resolution. This article reports on a novel dasymetric disaggregation method that uses encoder–decoder convolutional neural networks, similar to those adopted in image segmentation tasks, to combine different types of ancillary data. Model training constitutes a particular challenge. This is due to the fact that disaggregation tasks are ill-posed and do not entail the direct use of supervision signals in the form of training instances mapping low-resolution to high-resolution counts. We propose to address this problem through self-training. Our method iteratively refines initial estimates produced by disaggregation heuristics and training models with the estimates from previous iterations together with relevant regularization strategies. We conducted experiments related to the disaggregation of different variables collected for Continental Portugal into a raster grid with a resolution of 200 m. Results show that the proposed approach outperforms common alternative methods, including approaches that use other types of regression models to infer the dasymetric weights.

Rescaled variance tests for seasonal stationarity

This paper introduces rescaled variance [V/S] tests for seasonal stationarity. The V/S statistic is designed by Giraitis, L., P. Kokoszka, R. Leipus, and G. Teyssière. 2003. “Rescaled Variance and Related Tests for Long Memory in Volatility and Levels.” Journal of Econometrics 112: 265–94 to be the mean corrected versions of the KPSS statistic. In the seasonal context, Canova, F., and B. E. Hansen. 1995. “Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability.” Journal of Business & Economic Statistics 13: 237–52 present the seasonal generalization of the KPSS statistic. In this regard, I aim to strengthen the work of Canova, F., and B. E. Hansen. 1995. “Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability.” Journal of Business & Economic Statistics 13: 237–52 [CH] by mean correction in the seasonal framework. I obtain the asymptotic distributions of the seasonal V/S tests. The V/S tests enjoy better power performance than the CH tests while exhibiting similiar size performance. Furthermore, by data pre-filtering, I propose robustified versions of the V/S statistics to eliminate the unattended unit root problem observed in the CH tests.

What Promotes Post-Earthquake Economic Recovery: The Role of Counterpart Assistance Policy After the Wenchuan Ms 8.0 Earthquake, China

Despite the economic statistics from recent years indicating outstanding economic recovery in disaster-affected areas after the Wenchuan Ms 8.0 Earthquake, the causes of these macro-economic changes remain ambiguous. The Chinese Government set up the counterpart assistance policy to aid post-disaster reconstruction after the Wenchuan Ms 8.0 Earthquake in 2008; however, whether the changes seen in the economic statistics can be attributed to this policy remains unclear. This article uses the difference-in-differences model to evaluate the effects of counterpart assistance on economic development in disaster areas. Thirty-nine severely affected counties were chosen as research objects and divided into a treatment group (18 recipient counties) and a control group (non-recipient counties). Empirical results indicate the counterpart assistance policy helped to significantly improve the real GDP and GDP growth rate per capita in the treatment group. Counterpart assistance influenced the real GDP principally by increasing investment in fixed assets, employment, urbanization level, and fiscal expenditure. The findings of this study deepen our understanding of counterpart assistance within the Chinese context.