Open Access
| Issue |
SHS Web Conf.
Volume 71, 2019
Eurasia: Sustainable Development, Security, Cooperation – 2019
|
|
|---|---|---|
| Article Number | 02004 | |
| Number of page(s) | 4 | |
| Section | National Interests and National Development Strategies | |
| DOI | https://doi.org/10.1051/shsconf/20197102004 | |
| Published online | 25 November 2019 | |
- V. Pavlyushina, V. Brilliantova, N. Kulaeva, Bulletin of current trends in Russian economy. Personal consumption of goods, 47 (2019). URL: http://ac.gov.ru/files/publication/a/21585.pdf. Accessed: 12. 07.2019. [Google Scholar]
- N.I. Merkushova, E.N. Bobrova, Statistical research of retail growth factors. Bulletin of Samara State University of Economics, 9 (143),73-76 (2016). [in Rus.]. [Google Scholar]
- M. Čižmešija, Z. Orlović, Consumer confidence indicator as a leading indicator of changes in retail trade turnover. Ekonomski Pregled, 69(1), 3-19 (2018). DOI: 10.32910/ep.69.1.1. [CrossRef] [Google Scholar]
- J. Arnerić, A. Čeh Časni, M. Čular, Adjusting for calendar effects of real retail trade turnover time series. Croatian Operational Research Review, 6 (2),305-319 (2015). DOI: 10.17535/crorr.2015.0024. [CrossRef] [Google Scholar]
- A.I. Nikitenko, N.A. Reent, Mathematical and statistical analysis of the retail trade turnover in Russia. Math Application to Economic and Technical Research, 1 (6),116-123 (2016). [in Rus.]. [Google Scholar]
- A.A. Sheveleva, Analysis and forecasting of retail trade volumes. Proceedings of young scientists of Altai State University, 15, 173-176 (2018). [in Rus.]. [Google Scholar]
- S.V. Konikhin, Application of neural network modeling to forecast retain turnover in the process of investment decision making. Modern Economy Success, 4, 42-46 (2017). [in Rus.]. [Google Scholar]
- GKS, Short-term economic indicators of the Russian Federation. (2019). URL: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1140080765391. Accessed: 30. 08.2019. [in Rus.]. [Google Scholar]
- E.I. Sukhanova, S.Y. Shirnaeva, Statistic-econometric approach towards the research of macroeconomic indicators dynamics. In: S.I. Ashmarina (Ed.), Proceedings of the 13th International Scientific and Practical Conference “Problems of Enterprise Development: Theory and Practice” (pp. 248-252), Samara: Samara State University of Economics (2014). [in Rus.]. [Google Scholar]
- E.I. Sukhanova, S.Y. Shirnaeva, N.A. Zaychikova, Modeling and forecasting financial performance of business: Statistical and econometric approach. In: V.V. Mantulenko (Ed.), International Scientific Conference “Global Challenges and Prospects of the Modern Economic Development” (GCPMED-2019) (pp. 487-496). European Proceedings of Social and Behavioural Sciences, 57 (2019). London: Future Academy. DOI: 10.15405/epsbs.2019.03.48. [Google Scholar]
- E.I. Sukhanova, S.Y. Shirnaeva, Different approaches to macroeconomic processes simulation and forecasting. Fundamental Research, 12-2, 406-411 (2015). [in Rus.]. [Google Scholar]
- E.I. Sukhanova, S.Y. Shirnaeva, A.G. Mokronosov, Econometric models for forecasting of macroeconomic indices. International Journal of Environmental and Science Education, 11(16), 9191-9205 (2016). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.