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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">10.32609/0042-8736-2013-7-63-81</article-id><article-id custom-type="elpub" pub-id-type="custom">voprecotest-555</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>ФИНАНСОВАЯ ЭКОНОМИКА</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>FINANCIAL ECONOMICS</subject></subj-group></article-categories><title-group><article-title>Предсказание поворотных точек бизнес-цикла: помогают ли переменные финансового сектора?</article-title><trans-title-group xml:lang="en"><trans-title>Predicting Turning Points of the Business Cycle: Do Financial Sector Variables Help?</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>Пестова</surname><given-names>А. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Pestova</surname><given-names>A.</given-names></name></name-alternatives><bio xml:lang="ru"><p>ведущий эксперт Центра макроэкономического анализа и краткосрочного прогнозирования (ЦМАКП), аспирант НИУ ВШЭ (Москва)</p></bio><email xlink:type="simple">apestova@forecast.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Центр макроэкономического анализа и краткосрочного прогнозирования (ЦМАКП); НИУ ВШЭ (Москва)</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Center for Macroeconomic Analysis and Short-term Forecasting, National Research University Higher School of Economics (Moscow, Russia)</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2013</year></pub-date><pub-date pub-type="epub"><day>20</day><month>07</month><year>2013</year></pub-date><volume>0</volume><issue>7</issue><fpage>63</fpage><lpage>81</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Voprosy Ekonomiki, NP, 2013</copyright-statement><copyright-year>2013</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/555">https://www.vopreco.ru/jour/article/view/555</self-uri><abstract><p>Цель данной работы — построение опережающих индикаторов поворотных точек бизнес-цикла по широкому набору стран, включая Россию, за длительный период времени. Мы используем модели дискретного выбора с зависимой переменной состояния экономики: рецессия, нет рецессии. Данные модели позволяют определить вероятность изменения макроэкономической динамики с положительной на отрицательную и наоборот. Эмпирический анализ показал, что учет переменных финансового сектора позволяет существенно улучшить предсказательную силу моделей поворотных точек бизнес-цикла. При этом модели с переменными реального и финансового секторов, построенные в работе, превосходят «наивные» модели, учитывающие только опережающий индикатор ВВП по методологии ОЭСР, по причине либо более низкого уровня «шума» (модель входа в рецессию), либо более высокой предсказательной силы (модель выхода из рецессии).</p></abstract><trans-abstract xml:lang="en"><p>The objective of this study is to develop a system of leading indicators of the business cycle turning points for a wide range of countries, including Russia, over a period of more than thirty years. We use a binary choice model with the dependent variable of the state of economy: the recession, there is no recession. These models allow us to assess how likely is the change of macroeconomic dynamics from positive to negative and vice versa. Empirical analysis suggests that the inclusion of financial sector variables into equation can significantly improve the predictive power of the models of the turning points of business cycles. At the same time, models with financial and real sector variables obtained in the paper outperform the "naive" models based only on the leading indicator of GDP in the OECD methodology due to either a lower level of noise (recession model) or a higher predictive power (model of the recovery from recession).</p></trans-abstract><kwd-group xml:lang="ru"><kwd>бизнес-циклы</kwd><kwd>опережающие индикаторы</kwd><kwd>поворотные точки</kwd><kwd>модели бинарного выбора</kwd><kwd>макроэкономический кризис</kwd></kwd-group><kwd-group xml:lang="en"><kwd>business cycles</kwd><kwd>leading indicators</kwd><kwd>turning points</kwd><kwd>binary response models</kwd><kwd>macroeconomic crisis</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">Демидов О. (2008). Различные индексы прогнозирования экономической активности в России // Квантиль. № 5. 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