voprecotestВопросы экономикиVoprosy Ekonomiki0042-8736Voprosy Ekonomiki, NP10.32609/0042-8736-2024-3-96-119voprecotest-4651Research ArticleМЕТОДОЛОГИЯ ЭКОНОМИЧЕСКОГО АНАЛИЗАMETHODOLOGY OF ECONOMIC ANALYSISПандемия, санкции и беспокойство в регионах России: наукастинг ожиданий деловой активностиPandemic, sanctions and anxiety in Russia’s regions: Business expectations nowcastinghttps://orcid.org/0000-0002-2405-8106ФедюнинаА. А.FedyuninaA. A.

Федюнина Анна Андреевна - к. э. н., вед. н. с. Центра исследований структурной политики (ЦИСП) НИУ ВШЭ.

Москва

Anna A. Fedyunina

Moscow

afedyunina@hse.ruhttps://orcid.org/0000-0003-2986-4825ЮревичМ. А.YurevichM. M.

Юревич Максим Андреевич - аналитик ЦИСП НИУ ВШЭ, н. с. Центра макроэкономических исследований Финансового университета.

Москва

Maksim A. Yurevich

Moscow

MAYurevich@fa.ruhttps://orcid.org/0000-0003-2580-0760ГородныйН. А.GorodnyN. A.

Городный Николай Антонович – м. н. с. ЦИСП НИУ ВШЭ.

Москва

Nikolay A. Gorodny

Moscow

na.gorodnyi@hse.ruНациональный исследовательский университет «Высшая школа экономики»РоссияHSE UniversityRussian FederationНациональный исследовательский университет «Высшая школа экономики»; Финансовый университет при Правительстве РФРоссияHSE University; Financial University under the Government of the Russian FederationRussian Federation2024100320240396119Copyright © Voprosy Ekonomiki, NP, 20242024Voprosy Ekonomiki, NPVoprosy Ekonomiki, NPhttps://www.vopreco.ru/jour/about/submissions#copyrightNoticehttps://www.vopreco.ru/jour/article/view/4651

В исследовании разработана методика наукастинга индекса деловых ожиданий, протестированная на данных по российской экономике в целом, а также по ее субъектам. В отличие от существующих работ, предлагается определять набор поисковых образов для наукастинга на основе байесовского усреднения и проводить агрегирование временных рядов по отдельным запросам. Построенные индексы продемонстрировали высокую степень адекватности и как инструмент отражения шоковых событий в экономической и политической жизни страны, и в качестве предиктора колебаний опросных индексов ожиданий. Применение методики позволило выявить факторы волатильности индексов деловых ожиданий в зависимости от уровня развития и отраслевой специализации субъектов РФ. В финансово­ экономических центрах высокоразвитых регионов и развитых регионах с диверсифицированной экономикой высокая волатильность, а в менее развитых аграрных и сырьевых регионах — низкая. Эти результаты могут быть полезны при принятии решений в области экономической политики и представляют интерес для исследователей проблем экономической стабильности и для прогнозирования.

The study develops a methodology of business expectations index nowcasting with testing on data for the Russian economy as a whole and its regions. This methodology differs from the existing solutions in that it introduces a Bayesian averaging approach to define a set of search patterns for nowcasting and solves the issue of aggregation of time series by individual queries. The developed indices have shown a high level of adequacy, serving as effective tools to reflect shock events in the country’s economic and political landscape and also as predictors of fluctuations in survey­based expectation indices. The application of the presented methodology has revealed the factors that affect the volatility of business expectations indices depending on the level of development and sectoral specialization of Russian regions. In particular, financial and economic centers of highly developed regions and developed regions with diversified economy show high volatility, while business expectations indices in less developed agrarian and commodity regions show low volatility. These results can be useful for economic policy decisions and are of interest to researchers concerned with economic stability and forecasting.

наукастингделовая активностьпотребительские ожиданияожидания бизнесапандемия COVID­19российские регионыnowcastingbusiness activityconsumer expectationsbusiness expectationsCOVID­19 pandemicRussian regionsРабота выполнена в рамках проекта Фонда поддержки исследований будущего НИУ ВШЭThe study was prepared as part of a project of the HSE Research Foundation for the FutureReferencesАйвазян С. А. (2008). Байесовский подход в эконометрическом анализе // Прикладная эконометрика. № 1. С. 93—130.Aivazyan S. A. (2008). The Bayesian approach in econometric analysis. Applied Econometrics, No. 1, pp. 93—130. (In Russian).Айзатуллен В. С., Корягин Н. Д. (2013). Применение оценок доверия в экономике // Статистика и экономика. № 5. C. 18—21.Aizatullen V. S., Koryagin N. D. (2013). The application of confidence estimates in economics. Statistics and Economics, No. 5, pp. 18—21. (In Russian).Базаров Р. Т. (2013). Индекс потребительской уверенности: место и роль в российской экономике // Актуальные проблемы гуманитарных и естественных наук. № 10­1. С. 151—154.Bazarov R. T. (2013). Consumer confidence index: Place and role in the Russian economy. Aktualnye Problemy Gumanitarnykh i Estestvennykh Nauk, No. 10­1, pp. 151—154. (In Russian).Гавряшина Ю. В., Жердева Е. М. (2015). Развитие малого бизнеса в России в условиях кризиса // Стратегии бизнеса. № 11. С. 3—10.Гавряшина Ю. В., Жердева Е. М. (2015). Развитие малого бизнеса в России в условиях кризиса // Стратегии бизнеса. № 11. С. 3—10.Gavryashina Y. V., Zherdeva E. M. (2015). Development of small business in Russia in crisis situations. Business Strategies, No. 11, pp. 3—10. (In Russian).Григорьев Л. М., Урожаева Ю. В., Иванов Д. С. (2011). Синтетическая классификация регионов: основа региональной политики // Российские регионы: экономический кризис и проблемы модернизации / Под ред. Л. М. Григорьева, Н. В. Зубаревич, Г. Р. Хасаева. М.: Теис. С. 34—53.Grigoryev L. M., Urozhaeva Y. V., Ivanov D. S. (2011). Synthetic classification of regions: The basis for regional policy. In: L. M. Grigoryev, N. V. Zubarevich, G. R. Khasaev (eds.). Russian regions: Economic crisis and the problems of modernization. Moscow: Teis, pp. 34—53. (In Russian).Ермашкевич Н. С. (2019). Комплексный анализ государственных мер поддержки сектора малого и среднего предпринимательства в России // Российское предпринимательство. T. 20, № 1. С. 13—38. http://doi.org10.18334/rp.20.1.39727Ermashkevich N. S. (2019). Comprehensive analysis of government measures to support small and medium­sized businesses in Russia. Rossiyskoe Predprinimatelstvo, Vol. 20, No. 1, pp. 13—38. (In Russian). http://doi.org10.18334/rp.20.1.39727Завьялов Д. В., Сагинова О. В., Завьялова Н. Б. (2017). Проблемы и задачи развития малого и среднего предпринимательства в России // Российское предпринимательство. T. 18, № 3. C. 203—214. http://doi.org10.18334/rp.18.3.37285Zavyalov D. V., Saginova O. V., Zavyalova N. B. (2017). Challenges of small and medium­sized entrepreneurship development in Russia. Rossiyskoe Predprinimatelstvo, Vol. 18, No. 3, pp. 203—214. (In Russian). http://doi.org10.18334/rp.18.3.37285Левченко К. Н. (2021). Государственная поддержка малого предпринимательства в период пандемии COVID­19 в России // Инновационная наука. № 10­2. C. 50—56.Levchenko K. N. (2021). State support for small business during the COVID­19 pandemic in Russia. Innovation Science, No. 10­2, pp. 50—56. (In Russian).Петрова Д. А., Трунин П. В. (2020). Выявление настроений экономических агентов на основе поисковых запросов // Прикладная эконометрика. T. 3, № 59. C. 71—87. http://doi.org10.22394/1993­7601­2020­59­71­87Petrova D. A., Trunin P. V. (2020). Detection of economic agents’ sentiments based on search queries. Applied Econometrics, Vol. 3, No. 59, pp. 71—87. (In Russian). http://doi.org10.22394/1993­7601­2020­59­71­87Поршаков А., Дерюгина Е., Пономаренко А., Синяков А. (2015). Краткосрочное оценивание и прогнозирование ВВП России с помощью динамической факторной модели // Банк России. Серия докладов об экономических исследованиях. № 2.Porshakov A., Deryugina E., Ponomarenko A., Sinyakov A. (2015). Nowcasting and short­term forecasting of Russian GDP with a dynamic factor model. Bank of Russia Working Papers, No. 2. (In Russian).Смирнова А. А. (2021). О мерах государственной поддержки малого предпринимательства в период пандемии COVID­19 в России // Экономика, предпринимательство и право. T. 11, № 2. С. 285—298. http://doi.org10.18334/epp.11.2.111588Smirnova A. A. (2021). Measures of state support for small businesses during the COVID­19 pandemic in Russia. Ekonomika, Predprinimatelstvo i Pravo, Vol. 11, No. 2, pp. 285—298. (In Russian). http://doi.org10.18334/epp.11.2.111588Столбов М. И. (2011). Статистика поиска в Google как индикатор финансовой конъюнктуры // Вопросы экономики. № 11. С. 79—93. https://doi.org/10.32609/0042­8736­2011­11­79­93Stolbov M. I. (2011). Statistics of search queries in Google as an indicator of financial conditions. Voprosy Ekonomiki, No. 11, pp. 79—93. (In Russian). https://doi.org/10.32609/0042­8736­2011­11­79­93Столбов М. И., Щепелева М. А., Карминский А. М. (2021). Построение индекса глобального финансового стресса на основе синтеза исследований центральных банков и интенсивности поисковых запросов в Google. М.: Приоритет 2030; МГИМО Университет.Stolbov M. I., Shchepeleva M. A., Karminsky A. M. (2021). Construction of a global financial stress index based on a synthesis of central bank research and Google search intensity. Moscow: Priority 2030; MGIMO University. (In Russian).Ульянкин Ф. (2020). Прогнозирование российских макроэкономических показателей на основе информации в новостях и поисковых запросах // Деньги и кредит. T. 79, № 4. C. 75—97. https://doi.org/10.31477/rjmf.202004.75Ulyankin F. (2020). Forecasting Russian macroeconomic indicators based on information from news and search queries. Russian Journal of Money and Credit, Vol. 79, No. 4, pp. 75—97. https://doi.org/10.31477/rjmf.202004.75Фантаццини Д., Шаклеина М. В., Юрас И. А. (2018). Big Data в определении социального самочувствия населения России // Прикладная эконометрика. T. 50, № 2. C. 43—66.Fantazzini D., Shakleina M. V., Yuras I. A. (2018). Big Data for computing social well­being indices of the Russian population. Applied Econometrics, Vol. 50, No. 2, pp. 43—66. (In Russian).Цапенко И. П., Юревич М. А. (2022). Статистика онлайн­запросов в наукастинге миграции // Экономические и социальные перемены: факты, тенденции, прогноз. T. 15, № 1. C. 74—89. https://doi.org/10.15838/esc.2022.1.79.4Tsapenko I. P., Yurevich M. A. (2022). Nowcasting migration using statistics of online queries. Economic and Social Changes: Facts, Trends, Forecast, Vol. 15, No. 1, pp. 74—89. https://doi.org/10.15838/esc.2022.1.79.4Юревич М. А. (2021). Инфляционные ожидания и инфляция: наукастинг и прогнозирование //journal of Economic Regulation. T. 12, № 2. C. 22—35. https://doi.org/10.17835/2078­5429.2021.12.2.022­035Yurevich M. A. (2021). Inflation expectations and inflation: Nowcasting and forecasting. Journal of Economic Regulation, Vol. 12, No. 2, pp. 22—35. (In Russian). https://doi.org/10.17835/2078­5429.2021.12.2.022­035Шуляк Е. (2022). Макроэкономическое прогнозирование с использованием данных социальных сетей // Деньги и кредит. Т. 81, № 4, С. 86—112.Shulyak E. (2022). Macroeconomic forecasting using data from social media. Russian Journal of Money and Finance, Vol. 81, No. 4, pp. 86—112.Algan Y., Beasley E., Guyot F., Higa K., Murtin F., Senik C. (2016). Big Data measures of well­being: Evidence from a Google well­being index in the United States. OECD Statistics Working Papers, No. 2016/03. https://doi.org/10.1787/5jlz9hpg0rd1­enAlgan Y., Beasley E., Guyot F., Higa K., Murtin F., Senik C. (2016). Big Data measures of well­being: Evidence from a Google well­being index in the United States. OECD Statistics Working Papers, No. 2016/03. https://doi.org/10.1787/5jlz9hpg0rd1­enAntenucci D., Cafarella M., Levenstein M., Ré C., Shapiro M. D. (2014). Using social media to measure labor market flows. NBER Working Paper, No. w20010. https://doi.org/10.3386/w20010Antenucci D., Cafarella M., Levenstein M., Ré C., Shapiro M. D. (2014). Using social media to measure labor market flows. NBER Working Paper, No. w20010. https://doi.org/10.3386/w20010Askitas N., Zimmermann K. F. (2009). Google econometrics and unemployment forecasting. Applied Economics Quarterly, Vol. 55, No. 2, pp. 107—120. https://doi.org/10.3790/aeq.55.2.107Askitas N., Zimmermann K. F. (2009). Google econometrics and unemployment forecasting. Applied Economics Quarterly, Vol. 55, No. 2, pp. 107—120. https://doi.org/10.3790/aeq.55.2.107Atkins A., Niranjan M., Gerding E. (2018). Financial news predicts stock market volatility better than close price. Journal of Finance and Data Science, Vol. 4, No. 2, pp. 120—137. https://doi.org/10.1016/j.jfds.2018.02.002Atkins A., Niranjan M., Gerding E. (2018). Financial news predicts stock market volatility better than close price. Journal of Finance and Data Science, Vol. 4, No. 2, pp. 120—137. https://doi.org/10.1016/j.jfds.2018.02.002Baker S. R., Fradkin A. (2017). The impact of unemployment insurance on job search: Evidence from Google search data. Review of Economics and Statistics, Vol. 99, No. 5, pp. 756—768. https://doi.org/10.1162/REST_a_00674Baker S. R., Fradkin A. (2017). The impact of unemployment insurance on job search: Evidence from Google search data. Review of Economics and Statistics, Vol. 99, No. 5, pp. 756—768. https://doi.org/10.1162/REST_a_00674Baker S. R., Bloom N., Davis S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, Vol. 131, No. 4, pp. 1593—1636. https://doi.org/10.1093/qje/qjw024Baker S. R., Bloom N., Davis S. J. (2016). Measuring economic policy uncertainty. Quarterly Journal of Economics, Vol. 131, No. 4, pp. 1593—1636. https://doi.org/10.1093/qje/qjw024Bangwayo­Skeete P. F., Skeete R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed­data sampling approach. Tourism Management, Vol. 46, pp. 454—464. https://doi.org/10.1016/j.tourman.2014.07.014Bangwayo­Skeete P. F., Skeete R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed­data sampling approach. Tourism Management, Vol. 46, pp. 454—464. https://doi.org/10.1016/j.tourman.2014.07.014Beck K. (2019). What drives business cycle synchronization? BMA results from the European Union. Baltic Journal of Economics, Vol. 19, No. 2, pp. 248—275. https://doi.org/10.1080/1406099X.2019.1652393Beck K. (2019). What drives business cycle synchronization? BMA results from the European Union. Baltic Journal of Economics, Vol. 19, No. 2, pp. 248—275. https://doi.org/10.1080/1406099X.2019.1652393Beck K., Możdżeń M. (2020). Institutional determinants of budgetary expenditures. A BMA­based re­evaluation of contemporary theories for OECD countries. Sustainability, Vol. 12, No. 10, article 4104. https://doi.org/10.3390su12104104Beck K., Możdżeń M. (2020). Institutional determinants of budgetary expenditures. A BMA­based re­evaluation of contemporary theories for OECD countries. Sustainability, Vol. 12, No. 10, article 4104. https://doi.org/10.3390su12104104Bergström R. (1995). The relationship between manufacturing production and different business survey series in Sweden 1968—1992. International Journal of Forecasting, Vol. 11, No. 3, pp. 379—393. https://doi.org/10.1016/0169­2070 (95)00601­7Bergström R. (1995). The relationship between manufacturing production and different business survey series in Sweden 1968—1992. International Journal of Forecasting, Vol. 11, No. 3, pp. 379—393. https://doi.org/10.1016/0169­2070 (95)00601­7Bilgin M. H., Demir E., Gozgor G., Karabulut G., Kaya H. (2019). A novel index of macroeconomic uncertainty for Turkey based on Google­Trends. Economics Letters, Vol. 184, article 108601. https://doi.org/10.1016/j.econlet.2019.108601Bilgin M. H., Demir E., Gozgor G., Karabulut G., Kaya H. (2019). A novel index of macroeconomic uncertainty for Turkey based on Google­Trends. Economics Letters, Vol. 184, article 108601. https://doi.org/10.1016/j.econlet.2019.108601Bontempi M. E., Frigeri M., Golinelli R., Squadrani M. (2021). EURQ: A new web search­based uncertainty index. Economica, Vol. 88, No. 352, pp. 969—1015. https://doi.org/10.1111/ecca.12372Bontempi M. E., Frigeri M., Golinelli R., Squadrani M. (2021). EURQ: A new web search­based uncertainty index. Economica, Vol. 88, No. 352, pp. 969—1015. https://doi.org/10.1111/ecca.12372Bruno G., Otranto E. (2008). Models to date the business cycle: The Italian case. Economic Modelling, Vol. 25, No. 5, pp. 899—911. https://doi.org/10.1016/j.econmod.2007.11.009Bruno G., Otranto E. (2008). Models to date the business cycle: The Italian case. Economic Modelling, Vol. 25, No. 5, pp. 899—911. https://doi.org/10.1016/j.econmod.2007.11.009Castelnuovo E., Tran T. D. (2017). Google it up! A Google Trends­based uncertainty index for the United States and Australia. Economics Letters, Vol. 161, pp. 149—153. https://doi.org/10.1016/j.econlet.2017.09.032Castelnuovo E., Tran T. D. (2017). Google it up! A Google Trends­based uncertainty index for the United States and Australia. Economics Letters, Vol. 161, pp. 149—153. https://doi.org/10.1016/j.econlet.2017.09.032Cesaroni T., Iezzi S. (2017). The predictive content of business survey indicators: Evidence from SIGE. Journal of Business Cycle Research, Vol. 13, pp. 75—104. https://doi.org/10.1007/s41549­017­0015­8Cesaroni T., Iezzi S. (2017). The predictive content of business survey indicators: Evidence from SIGE. Journal of Business Cycle Research, Vol. 13, pp. 75—104. https://doi.org/10.1007/s41549­017­0015­8Chatziantoniou I., Degiannakis S., Eeckels B., Filis G. (2016). Forecasting tourist arrivals using origin country macroeconomics. Applied Economics, Vol. 48, No. 27, pp. 2571—2585. https://doi.org/10.1080/00036846.2015.1125434Chatziantoniou I., Degiannakis S., Eeckels B., Filis G. (2016). Forecasting tourist arrivals using origin country macroeconomics. Applied Economics, Vol. 48, No. 27, pp. 2571—2585. https://doi.org/10.1080/00036846.2015.1125434Chen M. H. (2015). Understanding the impact of changes in consumer confidence on hotel stock performance in Taiwan. International Journal of Hospitality Management, Vol. 50, pp. 55—65. https://doi.org/10.1016/j.ijhm.2015.07.010Chen M. H. (2015). Understanding the impact of changes in consumer confidence on hotel stock performance in Taiwan. International Journal of Hospitality Management, Vol. 50, pp. 55—65. https://doi.org/10.1016/j.ijhm.2015.07.010Chernis T., Sekkel R. (2018). Nowcasting Canadian economic activity in an uncertain environment. Bank of Canada Staff Discussion Paper, No. 2018­9.Chernis T., Sekkel R. (2018). Nowcasting Canadian economic activity in an uncertain environment. Bank of Canada Staff Discussion Paper, No. 2018­9.Choi H., Varian H. (2012). Predicting the present with Google Trends. Economic Record, Vol. 88, pp. 2—9. https://doi.org/10.1111/j.1475­4932.2012.00809.xChoi H., Varian H. (2012). Predicting the present with Google Trends. Economic Record, Vol. 88, pp. 2—9. https://doi.org/10.1111/j.1475­4932.2012.00809.xClaveria O., Pons E., Ramos R. (2007). Business and consumer expectations and macroeconomic forecasts. International Journal of Forecasting, Vol. 23, No. 1, pp. 47—69. https://doi.org/10.1016/j.ijforecast.2006.04.004Claveria O., Pons E., Ramos R. (2007). Business and consumer expectations and macroeconomic forecasts. International Journal of Forecasting, Vol. 23, No. 1, pp. 47—69. https://doi.org/10.1016/j.ijforecast.2006.04.004Conti A. M., Rondinelli C. (2015). Easier said than done: The divergence between soft and hard data. Economic Research and International Relations Area, No. 258. Bank of Italy.Conti A. M., Rondinelli C. (2015). Easier said than done: The divergence between soft and hard data. Economic Research and International Relations Area, No. 258. Bank of Italy.Curme C., Preis T., Stanley H. E., Moat H. S. (2014). Quantifying the semantics of search behavior before stock market moves. Proceedings of the National Academy of Sciences, Vol. 111, No. 32, pp. 11600—11605. https://doi.org/10.1073/pnas.1324054111Curme C., Preis T., Stanley H. E., Moat H. S. (2014). Quantifying the semantics of search behavior before stock market moves. Proceedings of the National Academy of Sciences, Vol. 111, No. 32, pp. 11600—11605. https://doi.org/10.1073/pnas.1324054111de Mendonça H. F., de Oliveira D. S. (2019). Firms’ confidence and Okun’s law in OECD countries. Economic Modelling, Vol. 78, pp. 98—107. https://doi.org/10.1016/j.econmod.2018.08.015de Mendonça H. F., de Oliveira D. S. (2019). Firms’ confidence and Okun’s law in OECD countries. Economic Modelling, Vol. 78, pp. 98—107. https://doi.org/10.1016/j.econmod.2018.08.015Dominitz J., Manski C. F. (2004). How should we measure consumer confidence? Journal of Economic Perspectives, Vol. 18, No. 2, pp. 51—66. https://doi.org/10.1257/0895330041371303Dominitz J., Manski C. F. (2004). How should we measure consumer confidence? Journal of Economic Perspectives, Vol. 18, No. 2, pp. 51—66. https://doi.org/10.1257/0895330041371303Donadelli M. (2015). Google search­based metrics, policy­related uncertainty and macro­ economic conditions. Applied Economics Letters, Vol. 22, No. 10, pp. 801—807. https://doi.org/10.1080/13504851.2014.978070Donadelli M. (2015). Google search­based metrics, policy­related uncertainty and macro­ economic conditions. Applied Economics Letters, Vol. 22, No. 10, pp. 801—807. https://doi.org/10.1080/13504851.2014.978070Donadelli M., Gerotto L. (2019). Non­macro­based Google searches, uncertainty, and real economic activity. Research in International Business and Finance, Vol. 48, pp. 111—142. https://doi.org/10.1016/j.ribaf.2018.12.007Donadelli M., Gerotto L. (2019). Non­macro­based Google searches, uncertainty, and real economic activity. Research in International Business and Finance, Vol. 48, pp. 111—142. https://doi.org/10.1016/j.ribaf.2018.12.007Dong X., Bollen J. (2015). Computational models of consumer confidence from large­scale online attention data: Crowd­sourcing econometrics. PloS ONE, Vol. 10, No. 3, article e0120039. https://doi.org/10.1371/journal.pone.0120039Dong X., Bollen J. (2015). Computational models of consumer confidence from large­scale online attention data: Crowd­sourcing econometrics. PloS ONE, Vol. 10, No. 3, article e0120039. https://doi.org/10.1371/journal.pone.0120039Dzielinski M. (2012). Measuring economic uncertainty and its impact on the stock market. Finance Research Letters, Vol. 9, No. 3, pp. 167—175. https://doi.org/10.1016/j.frl.2011.10.003Dzielinski M. (2012). Measuring economic uncertainty and its impact on the stock market. Finance Research Letters, Vol. 9, No. 3, pp. 167—175. https://doi.org/10.1016/j.frl.2011.10.003Ferrara L., Simoni A. (2023). When are Google data useful to nowcast GDP? An approach via preselection and shrinkage. Journal of Business & Economic Statistics, Vol. 41, No. 4, pp. 1188—1202. https://doi.org/10.1080/07350015.2022.2116025Ferrara L., Simoni A. (2023). When are Google data useful to nowcast GDP? An approach via preselection and shrinkage. Journal of Business & Economic Statistics, Vol. 41, No. 4, pp. 1188—1202. https://doi.org/10.1080/07350015.2022.2116025Fisher K. L., Statman M. (2003). Consumer confidence and stock returns. Journal of Portfolio Management, Vol. 30, No. 1, pp. 115—127. https://doi.org/10.3905/jpm.2003.319925Fisher K. L., Statman M. (2003). Consumer confidence and stock returns. Journal of Portfolio Management, Vol. 30, No. 1, pp. 115—127. https://doi.org/10.3905/jpm.2003.319925Fondeur Y., Karamé F. (2013). Can Google data help predict French youth unemployment? Economic Modelling, Vol. 30, pp. 117—125. https://doi.org/10.1016/j.econmod.2012.07.017Fondeur Y., Karamé F. (2013). Can Google data help predict French youth unemployment? Economic Modelling, Vol. 30, pp. 117—125. https://doi.org/10.1016/j.econmod.2012.07.017Garcia­López M. À., Jofre­Monseny J., Martínez­Mazza R., Segú M. (2020). Do short­term rental platforms affect housing markets? Evidence from Airbnb in Barcelona. Journal of Urban Economics, Vol. 119, article 103278. https://doi.org/10.1016/j.jue.2020.103278Garcia­López M. À., Jofre­Monseny J., Martínez­Mazza R., Segú M. (2020). Do short­term rental platforms affect housing markets? Evidence from Airbnb in Barcelona. Journal of Urban Economics, Vol. 119, article 103278. https://doi.org/10.1016/j.jue.2020.103278Gerasimenko V. V., Golovanova E. (2021). Evaluation of consumer behaviour on the Internet under the conditions of pandemic shock based on search activity in the luxury segment. Population and Economics, Vol. 5, No. 2, pp. 16—28. https://doi.org/10.3897/popecon.5.e63315Gerasimenko V. V., Golovanova E. (2021). Evaluation of consumer behaviour on the Internet under the conditions of pandemic shock based on search activity in the luxury segment. Population and Economics, Vol. 5, No. 2, pp. 16—28. https://doi.org/10.3897/popecon.5.e63315Goel S., Hofman J. M., Lahaie S., Pennock D. M., Watts D. J. (2010). Predicting consumer behavior with Web search. Proceedings of the National Academy of Sciences, Vol. 107, No. 41, pp. 17486—17490. https://doi.org/10.1073/ pnas.1005962107Goel S., Hofman J. M., Lahaie S., Pennock D. M., Watts D. J. (2010). Predicting consumer behavior with Web search. Proceedings of the National Academy of Sciences, Vol. 107, No. 41, pp. 17486—17490. https://doi.org/10.1073/ pnas.1005962107González­Aguado C., Moral­Benito E. (2013). Determinants of corporate default: A BMA approach. Applied Economics Letters, Vol. 20, No. 6, pp. 511—514. https://doi.org/10.1080/13504851.2012.718051González­Aguado C., Moral­Benito E. (2013). Determinants of corporate default: A BMA approach. Applied Economics Letters, Vol. 20, No. 6, pp. 511—514. https://doi.org/10.1080/13504851.2012.718051Guizzardi A., Stacchini A. (2015). Real­time forecasting regional tourism with business sentiment surveys. Tourism Management, Vol. 47, pp. 213—223. https://doi.org/10.1016/j.tourman.2014.09.022Guizzardi A., Stacchini A. (2015). Real­time forecasting regional tourism with business sentiment surveys. Tourism Management, Vol. 47, pp. 213—223. https://doi.org/10.1016/j.tourman.2014.09.022Guzman G. (2011). Internet search behavior as an economic forecasting tool: The case of inflation expectations. Journal of Economic and Social Measurement, Vol. 36, No. 3, pp. 119—167. https://doi.org/10.3233/JEM­2011­0342Guzman G. (2011). Internet search behavior as an economic forecasting tool: The case of inflation expectations. Journal of Economic and Social Measurement, Vol. 36, No. 3, pp. 119—167. https://doi.org/10.3233/JEM­2011­0342Hampson D. P., Ma S., Wang Y., Han M. S. (2021). Consumer confidence and conspicuous consumption: A conservation of resources perspective. International Journal of Consumer Studies, Vol. 45, No. 6, pp. 1392—1409. https://doi.org/10.1111/ijcs.12661Hampson D. P., Ma S., Wang Y., Han M. S. (2021). Consumer confidence and conspicuous consumption: A conservation of resources perspective. International Journal of Consumer Studies, Vol. 45, No. 6, pp. 1392—1409. https://doi.org/10.1111/ijcs.12661Hunneman A., Verhoef P. C., Sloot L. M. (2015). The impact of consumer confidence on store satisfaction and share of wallet formation. Journal of Retailing, Vol. 91, No. 3, pp. 516—532. https://doi.org/10.1016/j.jretai.2015.02.004Hunneman A., Verhoef P. C., Sloot L. M. (2015). The impact of consumer confidence on store satisfaction and share of wallet formation. Journal of Retailing, Vol. 91, No. 3, pp. 516—532. https://doi.org/10.1016/j.jretai.2015.02.004Jansen W. J., Nahuis N. J. (2003). The stock market and consumer confidence: European evidence. Economics Letters, Vol. 79, No. 1, pp. 89—98. https://doi.org/10.1016/S0165­1765(02)00292­6Jansen W. J., Nahuis N. J. (2003). The stock market and consumer confidence: European evidence. Economics Letters, Vol. 79, No. 1, pp. 89—98. https://doi.org/10.1016/S0165­1765(02)00292­6Keane M., Neal T. (2021). Consumer panic in the COVID­19 pandemic. Journal of Econometrics, Vol. 220, No. 1, pp. 86—105. https://doi.org/10.1016/j.jeconom.2020.07.045Keane M., Neal T. (2021). Consumer panic in the COVID­19 pandemic. Journal of Econometrics, Vol. 220, No. 1, pp. 86—105. https://doi.org/10.1016/j.jeconom.2020.07.045Khan H., Upadhayaya S. (2020). Does business confidence matter for investment? Empirical Economics, Vol. 59, pp. 1633—1665. https://doi.org/10.1007/s00181­019­01694­5Khan H., Upadhayaya S. (2020). Does business confidence matter for investment? Empirical Economics, Vol. 59, pp. 1633—1665. https://doi.org/10.1007/s00181­019­01694­5Kupfer A., Zorn J. (2020). A language­independent measurement of economic policy uncertainty in eastern European countries. Emerging Markets Finance and Trade, Vol. 56, No. 5, pp. 1166—1180. https://doi.org/10.1080/1540496X.2018.1559140Kupfer A., Zorn J. (2020). A language­independent measurement of economic policy uncertainty in eastern European countries. Emerging Markets Finance and Trade, Vol. 56, No. 5, pp. 1166—1180. https://doi.org/10.1080/1540496X.2018.1559140Lehmann R. (2023). The forecasting power of the ifo business survey. Journal of Business Cycle Research, Vol. 19, No. 1, pp. 43—94. https://doi.org/10.1007/s41549­022­00079­5Lehmann R. (2023). The forecasting power of the ifo business survey. Journal of Business Cycle Research, Vol. 19, No. 1, pp. 43—94. https://doi.org/10.1007/s41549­022­00079­5Lehmann R., Reif M. (2020). Tracking and predicting the German economy: Ifo vs. PMI. CESifo Working Paper, No. 8145. https://doi.org/10.2139/ssrn.3552385Lehmann R., Reif M. (2020). Tracking and predicting the German economy: Ifo vs. PMI. CESifo Working Paper, No. 8145. https://doi.org/10.2139/ssrn.3552385Malgarini M. (2012). Industrial production and confidence after the crisis: What’s going on? MPRA Рaper, No. 53813.Malgarini M. (2012). Industrial production and confidence after the crisis: What’s going on? MPRA Рaper, No. 53813.Mazurek J., Mielcová E. (2017). Is consumer confidence index a suitable predictor of future economic growth? An evidence from the USA. Economics and Management, Vol. 20, No. 2, pp. 30—45. https://doi.org/10.15240/tul/001/2017­2­003Mazurek J., Mielcová E. (2017). Is consumer confidence index a suitable predictor of future economic growth? An evidence from the USA. Economics and Management, Vol. 20, No. 2, pp. 30—45. https://doi.org/10.15240/tul/001/2017­2­003Nikolopoulos K., Punia S., Schäfers A., Tsinopoulos C., Vasilakis C. (2021). Forecasting and planning during a pandemic: COVID­19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, Vol. 290, No. 1, pp. 99—115. https://doi.org/10.1016/j.ejor.2020.08.001Nikolopoulos K., Punia S., Schäfers A., Tsinopoulos C., Vasilakis C. (2021). Forecasting and planning during a pandemic: COVID­19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, Vol. 290, No. 1, pp. 99—115. https://doi.org/10.1016/j.ejor.2020.08.001Olkiewicz M. (2022). The impact of economic indicators on the evolution of business confidence during the COVID­19 pandemic period. Sustainability, Vol. 14, No. 9, pp. 1—17. https://doi.org/10.3390/su14095073Olkiewicz M. (2022). The impact of economic indicators on the evolution of business confidence during the COVID­19 pandemic period. Sustainability, Vol. 14, No. 9, pp. 1—17. https://doi.org/10.3390/su14095073Ou Y. C., de Vries L., Wiesel T., Verhoef P. C. (2014). The role of consumer confidence in creating customer loyalty. Journal of Service Research, Vol. 17, No. 3, pp. 339—354. https://doi.org/10.1177/1094670513513925Ou Y. C., de Vries L., Wiesel T., Verhoef P. C. (2014). The role of consumer confidence in creating customer loyalty. Journal of Service Research, Vol. 17, No. 3, pp. 339—354. https://doi.org/10.1177/1094670513513925Pagano M., Wagner C., Zechner J. (2020). Disaster resilience and asset prices. Unpublished manuscript. Available at: https://doi.org/10.48550/arXiv.2005.08929Pagano M., Wagner C., Zechner J. (2020). Disaster resilience and asset prices. Unpublished manuscript. Available at: https://doi.org/10.48550/arXiv.2005.08929Patel J. C., Khurana P., Sharma Y. K., Kumar B., Ragumani S. (2018). Chronic lifestyle diseases display seasonal sensitive comorbid trend in human population evidence from Google Trends. PLoS ONE, Vol. 13, No. 12, article e0207359. https://doi.org/10.1371/journal.pone.0207359Patel J. C., Khurana P., Sharma Y. K., Kumar B., Ragumani S. (2018). Chronic lifestyle diseases display seasonal sensitive comorbid trend in human population evidence from Google Trends. PLoS ONE, Vol. 13, No. 12, article e0207359. https://doi.org/10.1371/journal.pone.0207359Petrova D., Trunin P. (2020). Population forecasting and analysis of demographic heterogeneity of Russia. Available at SSRN: https://doi.org/10.2139/ssrn.3594521Petrova D., Trunin P. (2020). Population forecasting and analysis of demographic heterogeneity of Russia. Available at SSRN: https://doi.org/10.2139/ssrn.3594521Pramana S., Paramartha D. Y., Ermawan G. Y., Deli N. F., Srimulyani W. (2022). Impact of COVID­19 pandemic on tourism in Indonesia. Current Issues in Tourism, Vol. 25, No. 15, pp. 2422—2442. https://doi.org/10.1080/13683500.2021.1968803Pramana S., Paramartha D. Y., Ermawan G. Y., Deli N. F., Srimulyani W. (2022). Impact of COVID­19 pandemic on tourism in Indonesia. Current Issues in Tourism, Vol. 25, No. 15, pp. 2422—2442. https://doi.org/10.1080/13683500.2021.1968803Preis T., Moat H. S., Stanley H. E., Bishop S. R. (2012). Quantifying the advantage of looking forward. Scientific Reports, Vol. 2, No. 1, pp. 1—2. https://doi.org/10.1038/srep00350Preis T., Moat H. S., Stanley H. E., Bishop S. R. (2012). Quantifying the advantage of looking forward. Scientific Reports, Vol. 2, No. 1, pp. 1—2. https://doi.org/10.1038/srep00350Preis T., Moat H. S., Stanley H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, Vol. 3, No. 1, pp. 1—6. https://doi.org/10.1038/srep01684Preis T., Moat H. S., Stanley H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, Vol. 3, No. 1, pp. 1—6. https://doi.org/10.1038/srep01684Raftery A. E. (1995). Bayesian model selection in social research. Sociological Methodology, Vol. 25, pp. 111—163. https://doi.org/10.2307/271063Raftery A. E. (1995). Bayesian model selection in social research. Sociological Methodology, Vol. 25, pp. 111—163. https://doi.org/10.2307/271063Salisu A. A., Ogbonna A. E., Oloko T. F., Adediran I. A. (2021). A new index for measuring uncertainty due to the COVID­19 pandemic. Sustainability, Vol. 13, No. 6, article 3212. https://doi.org/10.3390/su13063212Salisu A. A., Ogbonna A. E., Oloko T. F., Adediran I. A. (2021). A new index for measuring uncertainty due to the COVID­19 pandemic. Sustainability, Vol. 13, No. 6, article 3212. https://doi.org/10.3390/su13063212Sax C., Eddelbuettel D. (2018). Seasonal adjustment by X­13ARIMA­SEATS in R. Journal of Statistical Software, Vol. 87, No. 11, pp. 1—17. https://doi.org/10.18637/jss.v087.i11Sax C., Eddelbuettel D. (2018). Seasonal adjustment by X­13ARIMA­SEATS in R. Journal of Statistical Software, Vol. 87, No. 11, pp. 1—17. https://doi.org/10.18637/jss.v087.i11Shayaa S., Al­Garadi M. A., Piprani A. Z., Ashraf M., Sulaiman A. (2017). Social media sentiment analysis of consumer purchasing behavior vs consumer confidence index. In: Proceedings of the International Conference on Big Data and Internet of Things (BDIOT’17). New York: Association for Computing Machinery, pp. 32—35. https://doi.org/10.1145/3175684.3175712Shayaa S., Al­Garadi M. A., Piprani A. Z., Ashraf M., Sulaiman A. (2017). Social media sentiment analysis of consumer purchasing behavior vs consumer confidence index. In: Proceedings of the International Conference on Big Data and Internet of Things (BDIOT’17). New York: Association for Computing Machinery, pp. 32—35. https://doi.org/10.1145/3175684.3175712Sun S., Wei Y., Tsui K. L., Wang S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, Vol. 70, pp. 1—10. https://doi.org/10.1016/j.tourman.2018.07.010Sun S., Wei Y., Tsui K. L., Wang S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, Vol. 70, pp. 1—10. https://doi.org/10.1016/j.tourman.2018.07.010Szczygielski J. J., Bwanya P. R., Charteris A., Brzeszczyński J. (2021). The only certainty is uncertainty: An analysis of the impact of COVID­19 uncertainty on regional stock markets. Finance Research Letters, Vol. 43, article 101945. https://doi.org/10.1016/j.frl.2021.101945Szczygielski J. J., Bwanya P. R., Charteris A., Brzeszczyński J. (2021). The only certainty is uncertainty: An analysis of the impact of COVID­19 uncertainty on regional stock markets. Finance Research Letters, Vol. 43, article 101945. https://doi.org/10.1016/j.frl.2021.101945Szczygielski J. J., Charteris A., Obojska L. (2023). Do commodity markets catch a cold from stock markets? Modelling uncertainty spillovers using Google search trends and wavelet coherence. International Review of Financial Analysis, Vol. 87, article 102304. https://doi.org/10.1016/j.irfa.2022.102304Szczygielski J. J., Charteris A., Obojska L. (2023). Do commodity markets catch a cold from stock markets? Modelling uncertainty spillovers using Google search trends and wavelet coherence. International Review of Financial Analysis, Vol. 87, article 102304. https://doi.org/10.1016/j.irfa.2022.102304Taylor K., McNabb R. (2007). Business cycles and the role of confidence: Еvidence for Europe. Oxford Bulletin of Economics and Statistics, Vol. 69, No. 2, pp. 185—208. https://doi.org/10.1111/j.1468­0084.2007.00472.xTaylor K., McNabb R. (2007). Business cycles and the role of confidence: Еvidence for Europe. Oxford Bulletin of Economics and Statistics, Vol. 69, No. 2, pp. 185—208. https://doi.org/10.1111/j.1468­0084.2007.00472.xTran T. D., Vehbi T., Wong B. (2019). Measuring uncertainty for New Zealand using data­rich approach. Australian Economic Review, Vol. 52, No. 3, pp. 344—352. https://doi.org/10.1111/1467­8462.12339Tran T. D., Vehbi T., Wong B. (2019). Measuring uncertainty for New Zealand using data­rich approach. Australian Economic Review, Vol. 52, No. 3, pp. 344—352. https://doi.org/10.1111/1467­8462.12339Vosen S., Schmidt T. (2011). Forecasting private consumption: Survey­based indicators vs. Google Trends. Journal of Forecasting, Vol. 30, No. 6, pp. 565—578. https://doi.org/10.1002/for.1213Vosen S., Schmidt T. (2011). Forecasting private consumption: Survey­based indicators vs. Google Trends. Journal of Forecasting, Vol. 30, No. 6, pp. 565—578. https://doi.org/10.1002/for.1213Wang L., Wang S., Yuan Z., Peng L. (2021). Analyzing potential tourist behavior using PCA and modified affinity propagation clustering based on Baidu index: Taking Beijing city as an example. Data Science and Management, Vol. 2, pp. 12—19. https://doi.org/10.1016/j.dsm.2021.05.001Wang L., Wang S., Yuan Z., Peng L. (2021). Analyzing potential tourist behavior using PCA and modified affinity propagation clustering based on Baidu index: Taking Beijing city as an example. Data Science and Management, Vol. 2, pp. 12—19. https://doi.org/10.1016/j.dsm.2021.05.001Woloszko N. (2021). Tracking GDP using Google Trends and machine learning: A new OECD model. VoxEU, December 19. https://cepr.org/voxeu/columns/tracking­gdp­using­google­trends­and­machine­learning­new­oecd­modelWoloszko N. (2021). Tracking GDP using Google Trends and machine learning: A new OECD model. VoxEU, December 19. https://cepr.org/voxeu/columns/tracking­gdp­using­google­trends­and­machine­learning­new­oecd­modelZhang C., Tian Y. X., Fan Z. P. (2022). Forecasting sales using online review and search engine data: A method based on PCA—DSFOA—BPN N. International Journal of Forecasting, Vol. 38, No. 3, pp. 1005—1024. https://doi.org/10.1016/j.ijforecast.2021.07.010Zhang C., Tian Y. X., Fan Z. P. (2022). Forecasting sales using online review and search engine data: A method based on PCA—DSFOA—BPN N. International Journal of Forecasting, Vol. 38, No. 3, pp. 1005—1024. https://doi.org/10.1016/j.ijforecast.2021.07.010Zou H., Xue L. (2018). A selective overview of sparse principal component analysis. Proceedings of the IEEE, Vol. 106, No. 8, pp. 1311—1320. https://doi.org/10.1109/jPROC.2018.2846588Zou H., Xue L. (2018). A selective overview of sparse principal component analysis. Proceedings of the IEEE, Vol. 106, No. 8, pp. 1311—1320. https://doi.org/10.1109/jPROC.2018.2846588

The authors declare that there are no conflicts of interest present.