MODEL FOR FORECASTING OF VOLUME AND STRUCTURE "GREY IMPORT"

Abstract


The revenues of the Federal budget of the Russian Federation to a significant extent, is formed at the expense of customs payments. Hence, in the case of large amounts of "gray import", the state's economy can be significantly affected. The performance measures against "gray import" depends on the timely and adequate prediction of its structure and volumes. This forecast assumes the availability of appropriate models. However, the currently known models do not provide the timeliness and adequacy of the forecast.

The article suggests a model for the current prediction of volume and structure of "gray import". The model allowed stochastic representation of the "gray imports", simulating the processes of its formation. This simulation allows more adequately than the commonly used regression and expert models, considering available information about the "gray import" and thereby improves prediction accuracy its volume and structure. The information basis of the proposed model are data of the risk management system of the Federal customs service of the Russian Federation. The application of the proposed model allows near real-time to predict the structure and volume of "gray imports" and on this basis to develop the necessary measures to counter it.

Tatiana Nickolaevna Saurenko

tanya@saurenko.ru
SPIN-code: 2528-9508
RUDN University
Russian Federation

 

доктор экономических наук

Vladimir Georgievich Anisimov

an-33@yandex.ru
<p>Санкт-Петербургский политехнический университет Петра Великого</p>
Russian Federation

доктор технических наук, профессор кафедры информационных систем в экономике и менеджменте

Evgenii Georgievich Anisimov

an-33@rambler.ru
<p>директор НИИ РТА</p>
Russian Federation

доктор технических наук, доктор в. наук, профессор

Murat Romanovich Gapov

mgapov@gmail.com

кандидат экономических наук, заместитель министра экономического развития Карачаево-Черкесской республики

References in Roman alphabet are in a PDF file of the article

Views

Abstract - 5


Copyright (c) Saurenko T.N., Anisimov V.G., Anisimov E.G., Gapov M.R.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.