MATHEMATICAL MODELING
Scientometrics and management science
Modeling of activity characteristics of sectoral and regional subsystems
Suvorov R., Devyatkin D., Usenko N., Otmakhova Ju. Review of Methods for Data-Driven Export Potential Analysis
Computer analysis of texts
Suvorov R., Devyatkin D., Usenko N., Otmakhova Ju. Review of Methods for Data-Driven Export Potential Analysis

Abstract.

The paper presents a survey of approaches and data sources that may be useful for forecast of export growth potential of a country. We considered main studies in the area of analysis of international trade flows. The observed materials were divided into three main groups according to their intended purpose. We suggested a data-driven problem of discovering «growth points” to improve export potential of a country. Conclusions were drawn on further directions of the research using mining of the databases of international trade statistics and semantic search on collections of scientific and technical documents.

Keywords:

export growth potential, patent search, data mining, international trade, customs statistics, growth points, technologies.

PP. 75-85.

REFERENCES

1. Export-import of basic commodities in January-December 2015. Official website of the Russian Customs Service / – Available at: http://
www.customs.ru/index.php?option=com_content&view=article&id=22570#_ftn1, (accessed December 23, 2016).
2. Russia has overtaken the United States on the export of grain, 2016 / Available at: http://vz.ru/news/2016/7/20/822641.html, (accessed December 23, 2016).
3. FAOSTAT / UN Food and Agriculture Organization. – Available at: http://faostat.fao.org/beta/en/#data (accessed December 23, 2016).
4. Ivanova L., Tverskaya G, Tochki rosta i drajvery rosta: k voprosu o soderzhanii ponyatij [Points of growth and drivers of growth: the question of the content of the concepts]// Zhurnal institucionalnyh issledovanij [Journal of Institutional Research]. – 2015. –Vol. 7(2). – pp. 120-133.
5. Yoon B. On the development of a technology intelligence tool for identifying technology opportunity //Expert Systems with Applications. – 2008. – Vol. 35(1). – pp. 124-135.
6. Daim T. U. et al. Forecasting emerging technologies: Use of bibliometrics and patent analysis // Technological Forecasting and Social Change. – 2006. – Vol. 73(8). – pp. 981-1012.
7. Andersen B. The hunt for S-shaped growth paths in technological innovation: a patent study //Journal of Evolutionary Economics. – 1999. – Vol. 9(4). –
pp. 487-526.
8. Green R. T., Allaway A. W. Identification of export opportunities: A shift-share approach //The Journal of Marketing. – 1985. – pp. 83-88.
9. Duenas M., Fagiolo G. Modeling the International- Trade Network: a gravity approach //Journal of Economic Interaction and Coordination. – 2013. – Vol. 8(1). – p. 155-178.
10. Snijders T. A. B. Models for longitudinal network data //Models and methods in social network analysis. – 2005. – Vol. 1. – pp. 215-247.
11. Jaud M., Kukenova M., Strieborny M. Financial Development and Sustainable Exports: Evidence from Firm-product Data //The World Economy. – 2015. – Vol. 38(7). – pp. 1090-1114.
12. Serdyukova Yu., Usenko N. Strategicheskie prioritety integracionnogo vzaimodejstviya Rossii i Belarusi s pozicii obespecheniya prodovolstvennoj bezopasnosti [Strategic priorities of the integration between Russia and Belarus in terms of ensuring food security.]// Ehkonomicheskie i socialnye peremeny fakty tendencii prognoz [Economic and social changes: facts, trends and outlook]. – 2013. – No. 3 (27).
13. Prihodchenko O. Vyyavlenie ehksportnyh i importnyh prioritetov respubliki Belarus na osnove modelirovaniya mezhotraslevyh svyazej [Detection of export and import priorities of the Republic of Belarus on the basis of modeling of inter-branch relations]// Belorusskij zhurnal mezhdunarodnogo prava i mezhdunarodnyh otnoshenij [Belarusian Journal of International Law and International Relations]. – 2002.
14. Zakshevskaya E., Litvinenko T. Mirovye tendencii v proizvodstve i sbyte myasa KRS [Global trends in the production and marketing of beef] //Mezhdunarodnyj selskohozyajstvennyj zhurnal [International Journal of Agricultural]. – 2016. – No. 5.
15. Grater S. et al. Linking export opportunities of products and services: the case of South Africa.
16. Cuyvers L. et al. A decision support model for the planning and assessment of export promotion activities by government export promotion institutions—the Belgian case //International Journal of Research in Marketing. – 1995. – Vol. 12(2). – pp. 173-186.
17. Cuyvers L. Identifying export opportunities: the case of Thailand //International Marketing Review. – 2004. – Vol. 21(3). – pp. 255-278.
18. Cuyvers L., Viviers W. (ed.). Export Promotion-A Decision Support Model Approach. – AFRICAN SUN MeDIA, 2012.
19. Sgrignoli P. The World Trade Web: A Multiple- Network Perspective //arXiv preprint arXiv:1409.3799. – 2014.
20. Lall S., Weiss J., Zhang J. The “sophistication” of exports: a new trade measure //World Development. – 2006. – Vol. 34(2). – pp. 222-237.
21. Bernard A. B., Jensen J. B. Why some firms export //Review of Economics and Statistics. – 2004. – Vol. 86(2). – pp. 561-569.
22. Barigozzi M., Fagiolo G., Garlaschelli D. Multinetwork of international trade: A commodityspecific analysis //Physical Review E. – 2010. – Vol. 81(4). – pp. 046104.
23. Peluso S. et al. International Trade: a Reinforced Urn Network Model. – 2016. – No. 1601.03067.
24. Fronczak A. Structural Hamiltonian of the international trade network //No. – 2012. – Vol. 1. – №. arXiv: 1205.4589. – pp. 31-46.
25. Shen B., Zhang J., Zheng Q. Exploring multi-layer flow network of international trade based on flow distances //arXiv preprint arXiv: 1504.02361. – 2015.
26. Shi P. et al. Hierarchicality of trade flow networks reveals complexity of products //PloS one. – 2014. – Vol. 9(6). – p. e98247.
27. Kelle M. et al. Cross-border and Foreign Affiliate Sales of Services: Evidence from German Microdata //The World Economy. – 2013. – Vol. 36(11). – pp. 1373-1392.
28. Choquette E., Meinen P. Export spillovers: Opening the black box //The World Economy. – 2015. – Vol. 38(12). – pp. 1912-1946.
29. Mosejko V., Azmina Yu. Mnogofaktornaya ocenka ehksportnogo potenciala malyh i srednih predpriyatij regiona [Multifactor assessment of the export potential of small and mediumsized enterprises in the region]// Vestnik volgogradskogo gosudarstvennogo universiteta seriya 3 ehkonomika. ehkologiya [Journal of Volgograd State University. Series 3: The Economy. The Ecology.]. – 2012. – No. 2.
30. Kalinina L. Perspektivnye napravleniya ehksporta yaic i yajceproduktov proizvedennyh v irkutskoj oblasti [Promising directions for export eggs and egg products produced in the Irkutsk region]// Vestnik Vostochno-sibirskogo gosudarstvennogo universiteta tekhnologij i upravleniya [Journal of the East Siberian State University of Technology and Management]. – 2015. – Vol. 1(52). – pp. 201-591.
31. Gaulier G., Zignago S. Baci: international trade database at the product-level (the 1994-2007 version). – 2010.
32. Solleder O. et al. Panel Export Taxes (PET) Dataset: New Data on Export Tax Rates //Graduate Institute of International and Development Studies Working Paper. – 2013. – Vol. 7. – p. 2013.
33. Solleder O. Market access and trade – free data from ITC/UNCTAD/WTO / Olga Solleder. – Available at: http://olga.solleder.org/dataon-trade-and-market-access.html, (accessed December 23, 2016).
34. UN Comtrade: International Trade Statistics / UN. – Available at: http://comtrade.un.org/data/, (accessed December 23, 2016).
35. CHELEM – International Trade Database / CEPII. – Available at: http://www.cepii.fr/%5C/anglaisgraph/bdd/chelem/internatrade/itpresent.htm (accessed December 23, 2016).
36. Trade Costs Dataset / World Bank. – Available at: http://data.worldbank.org/data-catalog/tradecosts-dataset, (accessed December 23, 2016).
37. ASD «Dostup TSVT» [AAS “Access CSFT”] / Federal Customs Service. – Available at: http:// stat.customs.ru, (accessed December 23, 2016).
38. Trade Map – Trade Statistics for Interntational Business Development / UN International Trade Centre. – Available at: http://trademap.org/%20(accessed December 23, 2016).
39. Federal Institute of Industrial Property – Available at: http://www1.fips.ru/wps/wcm/connect/content_ru/ru (accessed December 23, 2016).
40. Patel J. et al. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques // Expert Systems with Applications. – 2015. – Vol. 42(1). – pp. 259-268.
41. Sheremetyeva S. Natural language analysis of patent claims //Proceedings of the ACL-2003 workshop on Patent corpus processing – Vol. 20. – Association for Computational Linguistics, 2003. – pp. 66-73.
42. Devyatkin D.., Smirnov I., Sochenkov I., Tikhomirov I. Sovremennye metody kompyuternoj lingvistiki dlya patentnogo poiska i analiza informacii [Natural language processing methods for patent search and patent mining] //Intellektualnaya sobstvennost promyshlennaya sobstvennost specialnyj vypusk [Intellectual property, industrial property – special issue] 2016, p.71-77.
43. Dean J., Ghemawat S. MapReduce: simplified data processing on large clusters //Communications of the ACM. – 2008. – Vol. 51(1). – pp. 107-113.
 

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