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<article xmlns:ali="http://www.niso.org/schemas/ali/1.0/" 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" article-type="research-article" xml:lang="ru"><front><journal-meta><issn publication-format="print">2072-6414</issn><issn publication-format="electronic">2411-1406</issn></journal-meta><article-meta><article-id pub-id-type="doi">10.17059/ekon.reg.2024-1-1</article-id><title-group xml:lang="en"><article-title>A Medium-Term Interindustry Econometric Model of the Moscow Economy</article-title></title-group><title-group xml:lang="ru"><article-title>Среднесрочная межотраслевая эконометрическая модель экономики города Москвы</article-title></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-2542-6190</contrib-id><name-alternatives><name xml:lang="en"><surname>Nikitin</surname><given-names>Kirill  M.</given-names></name><name xml:lang="ru"><surname>Никитин</surname><given-names>Кирилл Михайлович </given-names></name></name-alternatives><email>kirill.nikitin@tax-policy.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-0806-9777</contrib-id><name-alternatives><name xml:lang="en"><surname>Shirov</surname><given-names>Alexander A.</given-names></name><name xml:lang="ru"><surname>Широв</surname><given-names>Александр Александрович </given-names></name></name-alternatives><email>schir@ecfor.ru</email><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4174-6023</contrib-id><name-alternatives><name xml:lang="en"><surname>Chaplina </surname><given-names>Yulia Yu. </given-names></name><name xml:lang="ru"><surname>Чаплина </surname><given-names>Юлия Юрьевна </given-names></name></name-alternatives><email>yuliya.chaplina@tax-policy.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-4054-1955</contrib-id><name-alternatives><name xml:lang="en"><surname>Polzikov </surname><given-names>Dmitry A. </given-names></name><name xml:lang="ru"><surname>Ползиков </surname><given-names>Дмитрий Александрович </given-names></name></name-alternatives><email>dmitry.polzikov@gmail.com</email><xref ref-type="aff" rid="aff2"/><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-3825-831X</contrib-id><name-alternatives><name xml:lang="en"><surname>Potapenko </surname><given-names>Vadim V. </given-names></name><name xml:lang="ru"><surname>Потапенко</surname><given-names>Вадим Викторович </given-names></name></name-alternatives><email>vadvpotap@gmail.com</email><xref ref-type="aff" rid="aff2"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">LLC Center for Tax Policy</institution></aff><aff><institution xml:lang="ru">ООО «Центр налоговой политики»</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Institute of Economic Forecasting of RAS</institution></aff><aff><institution xml:lang="ru">Институт народнохозяйственного прогнозирования РАН</institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Moscow Institute of Physics and Technology,</institution></aff><aff><institution xml:lang="ru">Московский физико-технический институт (национальный исследовательский университет)</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2024-03-28" publication-format="electronic"/><volume>20</volume><issue>1</issue><fpage>1</fpage><lpage>15</lpage><history><date date-type="received" iso-8601-date="30.06.2023"/><date date-type="accepted" iso-8601-date="21.12.2023"/></history><permissions><copyright-statement xml:lang="en">Copyright © 2024 Kirill  M. Nikitin, Alexander A. Shirov, Yulia Yu. Chaplina, Dmitry A.  Polzikov, Vadim V. Potapenko</copyright-statement><copyright-statement xml:lang="ru">Copyright © 2024 Кирилл Михайлович Никитин, Александр Александрович Широв, Юлия Юрьевна Чаплина, Дмитрий Александрович Ползиков, Вадим Викторович Потапенко</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Kirill  M. Nikitin, Alexander A. Shirov, Yulia Yu. Chaplina, Dmitry A.  Polzikov, Vadim V. Potapenko</copyright-holder><copyright-holder xml:lang="ru">Кирилл Михайлович Никитин, Александр Александрович Широв, Юлия Юрьевна Чаплина, Дмитрий Александрович Ползиков, Вадим Викторович Потапенко</copyright-holder><ali:free_to_read/><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><license-p>CC BY 4.0</license-p></license></permissions><self-uri content-type="html" mimetype="text/html" xlink:title="article webpage" xlink:href="https://www.economyofregions.org/ojs/index.php/er/article/view/647">https://www.economyofregions.org/ojs/index.php/er/article/view/647</self-uri><self-uri content-type="pdf" mimetype="application/pdf" xlink:title="article pdf" xlink:href="https://www.economyofregions.org/ojs/index.php/er/article/download/647/270">https://www.economyofregions.org/ojs/index.php/er/article/download/647/270</self-uri><abstract xml:lang="en"><p>As the largest Russian region with high socio-economic indicators, Moscow affects economic development of the whole country. Therefore, the present study aims to develop a model to forecast main indicators of the Moscow economy. To accomplish the task, it is necessary to: a) choose a suitable model and forecasting methods; b) transform available regional statistics into an appropriate form; c) select an algorithm for assessing the impact of detailed budget expenditures on the Moscow economy; d) combine the obtained results to construct a forecasting model. The proposed medium-term forecasting model of the Moscow economy includes both interindustry and econometric approaches. The study justified the use of cross-validation metrics for selecting optimal econometric forecasting models. An algorithm for converting budget expenditure data from detailed expenditure codes into economic activities and product data was developed. We assessed the impact of Moscow’s budget expenditures on the economy considering intra-city interindustry connections. According to the model calculations, two complex macroeconomic forecasts were used as scenarios: the base forecast of the Ministry of Economic Development of Russia (April 2023) and the lower-growth forecast of the Institute of Economic Forecasting of the Russian Academy of Sciences (March 2023). The scenario of the Ministry of Economic Development assumes that, in 2023–2025, the Russian gross domestic product (GDP) in constant prices will increase by 1.2, 2.0 and 2.6 %, respectively. The gross regional product (GRP) of Moscow is expected to increase by 0.5, 0.8 and 1.2 %, respectively. The calculations show that, depending on these scenarios, Moscow’s GRP in current prices will grow up to 30.9-31.7 trillion roubles by 2025. Moscow budget expenditure multiplier for GRP is estimated as 0.76-0.77 for 2023-2025.</p></abstract><abstract xml:lang="ru"><p>Москва — крупнейший регион России, лидирующий по многим социально-экономическим показателям и в силу своего масштаба влияющий на экономику страны в целом. Это определяет цель исследования — построение модели для прогнозирования основных индикаторов развития московской экономики. Реализация данной цели предполагает выполнение следующих основных задач: а) выбор оптимального типа модели и прогностических методов, б) преобразование доступной региональной статистики в форму, позволяющую применять эти методы, в) нахождение алгоритма для учета влияния детализированных бюджетных расходов на московскую экономику, г) комбинацию результатов выполнения предыдущих задач для построения прогнозной модели. В статье описана разработанная авторами среднесрочная сценарная модель экономики Москвы, в рамках которой сочетаются межотраслевой и эконометрический подходы к прогнозированию экономического развития. Обосновывается использование наилучших кросс-валидационных метрик для выбора оптимальных с точки зрения прогнозирования эконометрических моделей. Разработан алгоритм перевода сумм расходов по детализированным кодам расходов бюджетной классификации в отраслевой разрез. Предложен подход к оценке влияния расходов московского бюджета на экономику c учетом внутригородских межотраслевых связей. Приведены результаты модельных расчетов, в рамках которых в качестве сценариев используются два комплексных макроэкономических прогноза: базовый прогноз Минэкономразвития России (апрель 2023 г.) и инерционный прогноз ИНП РАН (март 2023 г.). Сценарий Минэкономразвития в числе прочего предполагает прирост ВВП России в постоянных ценах в 2023–2025 гг. в 1,2, 2,0 и 2,6 % соответственно. Прогнозные темпы прироста московского ВРП в этом сценарии — 0,5, 0,8 и 1,2 % соответственно. Согласно выполненным расчетам, к 2025 г. московский ВРП в текущих ценах увеличится в зависимости от сценария до 30,9–31,7 трлн руб., а мультипликатор московских бюджетных расходов на ВРП в 2023–2025 гг. будет оставаться на уровне 0,76-0,77.</p></abstract><kwd-group xml:lang="en"><kwd>GRP forecast, regional budget expenditures, regional industry structure, INFORUM models, input-output analysis, cross-validation</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>прогнозирование ВРП, региональные бюджетные расходы, региональная отраслевая структура, модели типа «Инфорум», таблицы «затраты - выпуск», кросс-валидация</kwd></kwd-group></article-meta></front><body/><back><ack xml:lang="en"><p>The authors would like to express their gratitude to D. M. Ksenofontov (Institute of Economic Forecasting of RAS) for his valuable contribution to this study.</p></ack><ack xml:lang="ru"><p>Авторы выражают признательность Д. М. Ксенофонтову (ИНП РАН) за значимый вклад в результаты исследования.</p></ack><ref-list><ref id="en-ref1"><label>1</label><mixed-citation xml:lang="en">Almon, C. (1996). Regression with Just the Facts. Working Paper, 12. http://inforumweb.inforumecon.com/papers/wp/wp/1996/wp96014.pdf</mixed-citation></ref><ref id="en-ref2"><label>2</label><mixed-citation xml:lang="en">Almon, C. (2016). Inforum models: Origin, evolution and byways avoided. Trans. from English. Problemy prognozirovaniya [Studies on Russian Economic Development], 27 (2), 119-126. https://doi.org/10.1134/S1075700716020039 (In Russ.)</mixed-citation></ref><ref id="en-ref3"><label>3</label><mixed-citation xml:lang="en">Almon, C. (2017). The Craft of Economic Modeling (3rd ed.). 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