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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">voprecotest</journal-id><journal-title-group><journal-title xml:lang="ru">Вопросы экономики</journal-title><trans-title-group xml:lang="en"><trans-title>Voprosy Ekonomiki</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">0042-8736</issn><publisher><publisher-name>Voprosy Ekonomiki, NP</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.32609/0042-8736-2024-9-28-49</article-id><article-id custom-type="elpub" pub-id-type="custom">voprecotest-4797</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>МИКРОЭКОНОМИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MICROECONOMICS</subject></subj-group></article-categories><title-group><article-title>Факторы жесткости цен онлайн-ретейлеров и структурные сдвиги</article-title><trans-title-group xml:lang="en"><trans-title>Online retailers’ price rigidity factors and structural breaks</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2459-5614</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Джаохадзе</surname><given-names>Е. Д.</given-names></name><name name-style="western" xml:lang="en"><surname>Dzhaokhadze</surname><given-names>E. D.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Джаохадзе Елена Джурховна , м. н. с. лаборатории анализа институтов и финансовых рынков Института прикладных экономических исследований (ИПЭИ) РАНХиГС</p><p> </p></bio><email xlink:type="simple">dzhaokhadze-ed@ranepa.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-7494-2728</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Синельникова-Мурылева</surname><given-names>Е. В.</given-names></name><name name-style="western" xml:lang="en"><surname>Sinelnikova-Muryleva</surname><given-names>E. V.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Синельникова-Мурылева Елена Владимировна, к. э. н., с. н. с. Центра изучения проблем центральных банков ИПЭИ РАНХиГС</p></bio><email xlink:type="simple">el.sinelnikova@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Российская академия народного хозяйства и государственной службы&#13;
при Президенте РФ</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Russian Presidential Academy of National Economy and Public Administration</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2024</year></pub-date><pub-date pub-type="epub"><day>04</day><month>09</month><year>2024</year></pub-date><volume>0</volume><issue>9</issue><fpage>28</fpage><lpage>49</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Voprosy Ekonomiki, NP, 2024</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="ru">Voprosy Ekonomiki, NP</copyright-holder><copyright-holder xml:lang="en">Voprosy Ekonomiki, NP</copyright-holder><license xlink:href="https://www.vopreco.ru/jour/about/submissions#copyrightNotice" xlink:type="simple"><license-p>https://www.vopreco.ru/jour/about/submissions#copyrightNotice</license-p></license></permissions><self-uri xlink:href="https://www.vopreco.ru/jour/article/view/4797">https://www.vopreco.ru/jour/article/view/4797</self-uri><abstract><p>Современные информационные технологии позволяют агрегировать и исследовать значительные массивы данных о ценах онлайн-ретейлеров. Онлайн-данные обладают такими преимуществами, как высокая частотность и возможность наблюдения в режиме реального времени. На ежедневных детализированных микроданных о ценах с сентября 2020 по май 2023 г. (включительно) протестированы теоретические и эмпирические факторы ценовой жесткости посредством логистической регрессии. Неоднородность данных определяет необходимость оценок для периодов до и после февраля 2022 г. Для первого подпериода обнаружено, что использование онлайнретейлерами привлекательных цен способствовало повышению их жесткости, при этом вероятность изменения цены была тем выше, чем больше цен товаров-конкурентов было скорректировано. Цены изменялись с периодичностью от одной до четырех недель. Во втором подпериоде для ценообразования онлайн-ретейлеров факторы размера рынка и привлекательной цены перестали быть значимыми. Изменение цен по-прежнему носило периодический характер и зависело от поведения конкурентов, но в меньшей степени, чем в 2020—2021 гг. </p></abstract><trans-abstract xml:lang="en"><p>Modern information technologies make it possible to aggregate and study a significant amount of data on the prices of online retailers. Online data has the advantages of high frequency and real-time monitoring. Based on daily granular micro data on prices since September 2020 to May 2023 (including), we test theoretical and empirical drivers of price stickiness using a logistic regression approach. The heterogeneity of the data leads us to conduct estimates on two sub-periods: before and after February 2022. In the first sub-period we found that the use of attractive prices by online retailers contributed to increased price rigidity, while the probability of price change was higher as the more productscompetitors adjusted prices. Price changes ranged from one to four weeks. In the second sub-period, the factors of market size and attractive prices became insignificant for the pricing of online retailers. Price changes continued to be periodic and dependent on the behavior of competitors but to a lesser extent than in 2020—2021. </p></trans-abstract><kwd-group xml:lang="ru"><kwd>жесткость цен</kwd><kwd>факторы жесткости цен</kwd><kwd>онлайн-цены</kwd><kwd>высокочастотные данные</kwd><kwd>большие данные</kwd><kwd>логит-модель</kwd><kwd>структурный сдвиг</kwd></kwd-group><kwd-group xml:lang="en"><kwd>price rigidity</kwd><kwd>price rigidity factors</kwd><kwd>online prices</kwd><kwd>high-frequency data</kwd><kwd>big data</kwd><kwd>logit model</kwd><kwd>structural break</kwd></kwd-group><funding-group><funding-statement xml:lang="ru">Статья подготовлена в рамках выполнения научно-исследовательской работы государственного задания РАНХиГС.</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Божечкова А., Евсеев А. (2020). 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