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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">finance</journal-id><journal-title-group><journal-title xml:lang="ru">Финансы: теория и практика/Finance: Theory and Practice</journal-title><trans-title-group xml:lang="en"><trans-title>Finance: Theory and Practice</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2587-5671</issn><issn pub-type="epub">2587-7089</issn><publisher><publisher-name>Financial University under The Government of Russian Federation</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26794/2587-5671-2015-0-4-116-121</article-id><article-id custom-type="elpub" pub-id-type="custom">finance-177</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ФИНАНСЫ, ДЕНЕЖНОЕ ОБРАЩЕНИЕ И КРЕДИТ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>FINANCES, MONETARY ADDRESS AND CREDIT</subject></subj-group></article-categories><title-group><article-title>МОДЕЛИ ПРОГНОЗИРОВАНИЯ ОБЪЕМА ПРОСРОЧЕННОЙ ЗАДОЛЖЕННОСТИ ПО КРЕДИТАМ</article-title><trans-title-group xml:lang="en"><trans-title>FORECASTING MODELS THE VOLUME OF OVERDUE DEBT ON LOANS</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Карабутов</surname><given-names>Н. Н.</given-names></name><name name-style="western" xml:lang="en"><surname>Karabutov</surname><given-names>N. N.</given-names></name></name-alternatives><email xlink:type="simple">kn22@yandex.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Феклин</surname><given-names>В. Г.</given-names></name><name name-style="western" xml:lang="en"><surname>Feklin</surname><given-names>V. G.</given-names></name></name-alternatives><email xlink:type="simple">vfeklin@fa.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Московский государственный технический университет радиотехники, электроники и автоматики</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Moscow State Engineering University of Radio Engineering</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Финансовый университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Financial University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2015</year></pub-date><pub-date pub-type="epub"><day>10</day><month>10</month><year>2017</year></pub-date><volume>0</volume><issue>4</issue><fpage>116</fpage><lpage>121</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Карабутов Н.Н., Феклин В.Г., 2017</copyright-statement><copyright-year>2017</copyright-year><copyright-holder xml:lang="ru">Карабутов Н.Н., Феклин В.Г.</copyright-holder><copyright-holder xml:lang="en">Karabutov N.N., Feklin V.G.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://financetp.fa.ru/jour/article/view/177">https://financetp.fa.ru/jour/article/view/177</self-uri><abstract><p>Динамика задолженности по кредитам во многом характеризует развитие реального сектора экономики, а рост просроченной задолженности указывает на ухудшение этого развития. В связи с этим в условиях экономической нестабильности особенно актуальными являются мониторинг и прогнозирование объема просроченной задолженности. Официальная статистика Центрального банка Российской Федерации показывает, что в период с января 2011 г. по декабрь 2013 г. наблюдалось устойчивое снижение доли просроченной задолженности, а в начале 2014 г. произошла смена направления тренда. Набольший рост просроченной задолженности наблюдается с начала 2015 г., что объясняется проявлением кризисных явлений в российской экономике. В статье построены модели прогнозирования объема просроченной задолженности по кредитам юридических лиц и индивидуальных предпринимателей, оценены прогнозирующие свойства построенных моделей, показано преимущество применения идентификационного подхода к выбору структуры модели.</p></abstract><trans-abstract xml:lang="en"><p>Dynamics of debt on loans is important characteristic of the development of the real sector of the economy. Growth of arrears indicates negative trend of the economic development of the real sector of the economy. In connection with the above monitoring and forecasting of the volume of the overdue debt has a very importance in the conditions of economic instability. We used the Official statistics of the Central Bank of the Russian Federation to show a steady decline in the share of overdue debt in the period from January 2011 to December 2013, and the change of this trend in the beginning of 2014. Greatest growth of overdue debts since the beginning of 2015, which was a manifestation of the crisis phenomena in the Russian economy.In this article we constructed models for predicting the volume of overdue debt on loans to legal entities and individual entrepreneurs. There was evaluated the predictive properties of the constructed models and showed the advantage of the use of the identification approach to the choice of model structure.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>кредит</kwd><kwd>просроченная задолженность</kwd><kwd>параметрическая идентификация</kwd><kwd>регрессионная мо- дель</kwd><kwd>лаговые переменные</kwd><kwd>прогнозирование</kwd></kwd-group><kwd-group xml:lang="en"><kwd>loan</kwd><kwd>overdue debt</kwd><kwd>parametric identification</kwd><kwd>regression model</kwd><kwd>lagged variables</kwd><kwd>forecasting</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Статистика Центрального банка Российской Федерации / Statistics of the Central Источник: www.kremlin.ru Bank of the Russian Federation [Statistika Central’nogo banka Rossijskoj Federacii]. 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