title: Comparative statistical analysis of official foreign trade results
metadata.name: dc.contributor.author
metadata.value: Shanygin, Sergey I.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Rizvanova, Elvira R.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2017-01-30T09:13:24Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2017-01-30T09:13:24Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2016-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Shanygin S. I., Rizvanova E. R. Comparative statistical analysis of official foreign trade results. St Petersburg University Journal of Economic Studies, 2016, issue 4, pp. 49–74.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: 10.21638/11701/spbu05.2016.403
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/5981
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: The problem of information reliability securing occurs when making international comparison, and it becomes a particularly pressing one when several sources are used. The official data on foreign trade results in a country nearly always contain discrepancies complicating the macroeconomic analysis. It is traditionally considered that import data are more reliable than the export data; however, there are no any more accurate methods for discrepancies assessment. The article presents a statistical method of comprehensive comparative analysis of foreign trade results based on two sources of information, aimed to assess existing digressions and to detect any patterns in change of their values. The method is based on mirror statistics for export and import commodity structures and is tested using the information on foreign trade in Russia and Norway. To exclude any infl uence of errors conditioned by specific correlation in characteristics of these countries’ customs accounting, only the official data from Norwegian sources, presented in different classification systems, were used.
  Refs 20. Figs 6. Table 1.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: St Petersburg University Journal of Economic Studies;Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: commodity structure
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: mirror statistics
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: SITC (Standard International Trade Classification)
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: FTCG (Foreign Trade Classification of Goods)
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: statistical discrepancy
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: international customs practice
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Comparative statistical analysis of official foreign trade results
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 03-Shanygin.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/5981/1/03-Shanygin.pdf
resources.format: Adobe PDF
resources.size: 520,66 kB