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<article xmlns="https://jats.nlm.nih.gov/publishing/1.1/" xmlns:xlink="http://www.w3.org/1999/xlink" xml:lang="ru" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" dtd-version="1.1" specific-use="eps-0.1"><front><journal-meta><journal-id journal-id-type="publisher">SciNotesIBI</journal-id><journal-id journal-id-type="ojs">SciNotesIBI</journal-id><journal-title-group><journal-title xml:lang="ru">Ученые записки Международного банковского института</journal-title><trans-title-group xml:lang="en"><trans-title>Proceedings of the International Banking Institute</trans-title></trans-title-group><abbrev-journal-title xml:lang="en">Proceedings of the International Banking Institute</abbrev-journal-title><abbrev-journal-title xml:lang="ru">Ученые записки Международного банковского института</abbrev-journal-title></journal-title-group><contrib-group/><publisher><publisher-name>Международный банковский институт</publisher-name><publisher-loc><country>RU</country><uri>https://www.ibispb.ru/</uri></publisher-loc></publisher><issn pub-type="ppub">2413-3345</issn><self-uri xlink:href="https://journal.ibispb.ru/index.php/SciNotesIBI"/></journal-meta><article-meta><article-id pub-id-type="publisher-id">478</article-id><article-id pub-id-type="EDN">PVPNZI</article-id><article-categories><subj-group subj-group-type="heading" xml:lang="ru"><subject>Статьи</subject></subj-group></article-categories><title-group><article-title xml:lang="ru">Ненаблюдаемые компоненты основных макроэкономических показателей: современные модели и методы оценки</article-title><trans-title-group xml:lang="en"><trans-title>Unobserved components of key macroeconomic indicators: modern approaches and estimation methods</trans-title></trans-title-group></title-group><contrib-group content-type="author"><contrib><name-alternatives><string-name specific-use="display">Дьячкова  Наталья Федоровна</string-name><name name-style="western" specific-use="primary"><surname>Dyachkova</surname><given-names>Natalya Feodorovna</given-names></name></name-alternatives><xref ref-type="aff" rid="aff-1"/><bio xml:lang="en"><p><strong>Candidate of Economic Sciences</strong></p>
<p>Senior Researcher</p>
<p>Center for Central Banks Studies, Russian Presidential Academy of National Economy and Public Administration (RANEPA) (Moscow, Russia)</p>
<p>Address: 119571, Moscow, Vernadsky Ave., 84. e-mail: dyachkova-nf@ranepa.ru</p></bio><bio xml:lang="ru"><p><strong>к.э.н.</strong></p>
<p>Старший научный сотрудник</p>
<p>Центр изучения проблем центральных банков, Российская академия народного хозяйства и государственной службы (РАНХиГС) при Президенте Российской Федерации (Москва, Россия)</p>
<p>Адрес: 119571, г. Москва, пр-кт. Вернадского, д. 84, стр. 1, e-mail: dyachkova-nf@ranepa.ru</p></bio></contrib><contrib><name-alternatives><string-name specific-use="display">Синельникова-Мурылёва Елена Владимировна</string-name><name name-style="western" specific-use="primary"><surname>Sinelnikova-Muryleva</surname><given-names>Elena Vladimirovna</given-names></name></name-alternatives><bio xml:lang="en"><p><strong>Candidate of Economic Sciences</strong></p>
<p>Leading Researcher</p>
<p>Center for Central Banks Studies, Russian Presidential Academy of National Economy and Public Administration (RANEPA) (Moscow, Russia)</p>
<p>Address: 119571, Moscow, Vernadsky Ave., 84. e-mail: e.sinelnikova@ranepa.ru</p>
<p> </p></bio><bio xml:lang="ru"><p><strong>к.э.н.</strong></p>
<p>Ведущий научный сотрудник</p>
<p>Центр изучения проблем центральных банков, Российская академия народного хозяйства и государственной службы (РАНХиГС) при Президенте Российской Федерации (Москва, Россия)</p>
<p>Адрес: 119571, г. Москва, пр-кт. Вернадского, д. 84, стр. 1, e-mail: e.sinelnikova@ranepa.ru</p></bio></contrib></contrib-group><aff id="aff-1"><institution content-type="orgname">Российская академия народного хозяйства и государственной службы (РАНХиГС) при Президенте Российской Федерации (Москва, Россия)</institution></aff><pub-date date-type="collection"><year>2026</year></pub-date><pub-date date-type="pub" publication-format="epub"><day>30</day><month>09</month><year>2026</year></pub-date><issue seq="6">3 (57)</issue><issue-id>39</issue-id><fpage>93</fpage><lpage>117</lpage><pub-history><event event-type="received"><event-desc>Received: <date date-type="received" iso-8601-date="2026-09-30T09:50:28+00:00"><day>30</day><month>9</month><year>2026</year></date></event-desc></event></pub-history><permissions><copyright-statement>Copyright (c) 2026 Proceedings of the International Banking Institute</copyright-statement><copyright-year>2026</copyright-year><copyright-holder>Proceedings of the International Banking Institute</copyright-holder><license xlink:href="https://creativecommons.org/licenses/by-nc/4.0"><license-p>&lt;a rel="license" href="https://creativecommons.org/licenses/by-nc/4.0/"&gt;&lt;img alt="Лицензия Creative Commons" src="//i.creativecommons.org/l/by-nc/4.0/88x31.png" /&gt;&lt;/a&gt;&lt;p&gt;Это произведение доступно по &lt;a rel="license" href="https://creativecommons.org/licenses/by-nc/4.0/"&gt;лицензии Creative Commons «Attribution-NonCommercial» («Атрибуция — Некоммерческое использование») 4.0 Всемирная&lt;/a&gt;.&lt;/p&gt;</license-p></license></permissions><self-uri xlink:href="https://journal.ibispb.ru/index.php/SciNotesIBI/article/download/478/484/1683" content-type="application/pdf"/><self-uri xlink:href="https://journal.ibispb.ru/index.php/SciNotesIBI/article/view/478"/><abstract><p>Цель исследования: рассмотрение ненаблюдаемых макроэкономических компонент и определение методов их оценки, использующихся в современной теоретической литературе.</p>
<p>Методология исследования: на основе анализа современной литературы проводится обобщение теоретических подходов к определению и эмпирических подходов к оценке ненаблюдаемых компонент основных макроэкономических показателей. В центре рассмотрения находятся выпуск, безработица и инфляция, а также их трендовые составляющие (потенциальный выпуск, естественная безработица и трендовая инфляция) и разрывы.</p>
<p>Результаты: даются определения соответствующим показателям, а также обсуждается их применение и оценка в макроэкономических полуструктурных моделях. Рассматриваются методы фильтрации и декомпозиции данных на тренд и цикл. Проводится сравнительный анализ исследований потенциального выпуска и разрыва выпуска при помощи моделей ненаблюдаемых компонент.</p>
<p>Выводы: показано, что полуструктурные модели ненаблюдаемых компонент активно используются для оценки разрыва выпуска, трендовой инфляции и определения естественного уровня безработицы. В сравнении с DSGE-моделями, полуструктурные модели более просты в построении и дают лучшую иллюстрацию механизмов для отдельных макропоказателей.</p></abstract><trans-abstract xml:lang="en"><p>Objective. The study aims to examine unobserved macroeconomic components and identify the methods used to estimate them in the contemporary theoretical literature.</p>
<p>Methodology. Based on a review of recent literature, the paper summarizes theoretical approaches to defining and empirical approaches to estimating the unobserved components of key macroeconomic variables. The analysis focuses on output, unemployment, and inflation, as well as their trend components (potential output, the natural rate of unemployment, and trend inflation) and the corresponding gaps.</p>
<p>Results. The paper defines the relevant indicators and discusses their application and estimation in semi-structural macroeconomic models. It reviews methods for filtering and decomposing data into trend and cyclical components. It also provides a comparative analysis of studies estimating potential output and the output gap using unobserved-components models.</p>
<p>Conclusions. The findings show that semi-structural unobserved components models are widely used to estimate the output gap, trend inflation, and the natural rate of unemployment. Compared with DSGE models, semi-structural models are easier to construct and provide a clearer representation of the mechanisms underlying individual macroeconomic variables.</p></trans-abstract><trans-abstract xml:lang="en&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: justify; text-indent: 35.4pt; line-height: 130%;&quot;&gt;&lt;strong&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 12.0pt; line-height: 130%; mso-ansi-language: EN-US;&quot;&gt;Objective.&lt;/span&gt;&lt;/strong&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 12.0pt; line-height: 130%; mso-ansi-language: EN-US;&quot;&gt; The study aims to examine unobserved macroeconomic components and identify the methods used to estimate them in the contemporary theoretical literature.&lt;/span&gt;&lt;/p&gt;&#10;&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: justify; text-indent: 35.4pt; line-height: 130%;&quot;&gt;&lt;strong&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 12.0pt; line-height: 130%; mso-ansi-language: EN-US;&quot;&gt;Methodology.&lt;/span&gt;&lt;/strong&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 12.0pt; line-height: 130%; mso-ansi-language: EN-US;&quot;&gt; Based on a review of recent literature, the paper summarizes theoretical approaches to defining and empirical approaches to estimating the unobserved components of key macroeconomic variables. The analysis focuses on output, unemployment, and inflation, as well as their trend components (potential output, the natural rate of unemployment, and trend inflation) and the corresponding gaps.&lt;/span&gt;&lt;/p&gt;&#10;&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: justify; text-indent: 35.4pt; line-height: 130%;&quot;&gt;&lt;strong&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 12.0pt; line-height: 130%; mso-ansi-language: EN-US;&quot;&gt;Results.&lt;/span&gt;&lt;/strong&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 12.0pt; line-height: 130%; mso-ansi-language: EN-US;&quot;&gt; The paper defines the relevant indicators and discusses their application and estimation in semi-structural macroeconomic models. It reviews methods for filtering and decomposing data into trend and cyclical components. It also provides a comparative analysis of studies estimating potential output and the output gap using unobserved-components models.&lt;/span&gt;&lt;/p&gt;&#10;&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: justify; text-indent: 35.4pt; line-height: 130%;&quot;&gt;&lt;strong&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 12.0pt; line-height: 130%; mso-ansi-language: EN-US;&quot;&gt;Conclusions.&lt;/span&gt;&lt;/strong&gt;&lt;span lang=&quot;EN-US&quot; style=&quot;font-size: 12.0pt; line-height: 130%; mso-ansi-language: EN-US;&quot;&gt; The findings show that semi-structural unobserved components models are widely used to estimate the output gap, trend inflation, and the natural rate of unemployment. Compared with DSGE models, semi-structural models are easier to construct and provide a clearer representation of the mechanisms underlying individual macroeconomic variables.&lt;/span&gt;&lt;/p&gt;"/><kwd-group xml:lang="ru"><title>Ключевые слова</title><kwd>потенциальный выпуск</kwd><kwd>естественный уровень безработицы</kwd><kwd>NAIRU</kwd><kwd>полуструктурные модели</kwd><kwd>разрыв выпуска</kwd><kwd>инфляционный разрыв</kwd></kwd-group><kwd-group xml:lang="en"><title>Keywords</title><kwd>potential output</kwd><kwd>natural rate of unemployment</kwd><kwd>NAIRU</kwd><kwd>unobserved components model</kwd><kwd>business cycles</kwd><kwd>output gap</kwd><kwd>inflation gap</kwd></kwd-group><funding-group><award-group><funding-source xml:lang="en">The article was written on the basis of the RANEPA state assignment research programme.</funding-source></award-group><award-group><funding-source xml:lang="ru">Данная статья подготовлена в рамках государственного задания РАНХиГС.</funding-source></award-group></funding-group><counts><page-count count="25"/></counts><custom-meta-group><custom-meta><meta-name>issue-cover</meta-name><meta-value><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://journal.ibispb.ru/public/journals/1/cover_issue_39_ru.jpg"/></meta-value></custom-meta></custom-meta-group><custom-meta-group/></article-meta></front><body/><back><ref-list><ref id="R1"><mixed-citation xml:lang="ru_RU">Friedman M. 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