voprstatВопросы статистикиVoprosy Statistiki2313-63832658-549910.34023/2313-6383-2024-31-5-5-22voprstat-1794Research ArticleМАТЕМАТИКО-СТАТИСТИЧЕСКИЕ МЕТОДЫ В АНАЛИЗЕ И ПРОГНОЗИРОВАНИИMATHEMATICAL AND STATISTICAL METHODS IN ANALYSIS AND FORECASTINGИспользование обследований экономических тенденций в наукастинге роста ВВПResults of Economic Tendency Surveys in Nowcasting GDP Growthhttps://orcid.org/0000-0002-6383-9562КитрарЛ. А.KitrarL. A.

Китрар Людмила Анатольевна – канд. экон. наук, доцент, Институт статистических исследований и экономики знаний

101000, г. Москва, Покровский бульвар, д. 11

Liudmila A. Kitrar – Cand. Sci. (Econ.), Assistant Professor, Institute for Statistical Studies and Economics of Knowledge

11, Pokrovsky Blvd., Moscow, 101000

kitrar.liudmila@gmail.comhttps://orcid.org/0009-0003-0590-5271РахмановМ. А.RahmanovM. A.

Рахманов Мурад Абдусаматович – начальник юридического отдела, главное управление по городу Ташкенту

100001, г. Ташкент, ул. Ислама Каримова, д. 6

Murad A. Rahmanov – Head of the Legal Department, Main Administration for Tashkent City

6, Islam Karimov Str., Tashkent, 100001

Murat_raxmanov@mail.ruhttps://orcid.org/0000-0003-2632-9026ЛипкиндТ. М.LipkindT. M.

Липкинд Тамара Михайловна – независимый эксперт

111583, г. Москва, ул. Косинская, д. 18

Tamara M. Lipkind – Independent Expert

18, Kosinskaya Str., Moscow, 111538

liptoma3@gmail.comНациональный исследовательский университет «Высшая школа экономики»РоссияNational Research University Higher School of Economics (HSE University)Russian FederationЦентральный Банк Республики УзбекистанУзбекистанCentral Bank of the Republic of UzbekistanUzbekistan202429102024315522Copyright © Китрар Л.А., Рахманов М.А., Липкинд Т.М., 20242024Китрар Л.А., Рахманов М.А., Липкинд Т.М.Kitrar L.A., Rahmanov M.A., Lipkind T.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://voprstat.elpub.ru/jour/article/view/1794

В статье обосновывается целесообразность интеграции обобщенной информации обследований экономических тенденций в систему наукастинга экономического роста. Проведен сравнительный анализ эффективности моделей векторной авторегрессии, включающих индекс экономических настроений, рассчитанный на основе обследований Росстата деловой активности и потребительских ожиданий, и индекс бизнес-климата, сформированный по данным мониторинга предприятий Банком России.Статистическое тестирование временных рядов композитных индикаторов и индекса физического объема (ИФО) ВВП подтвердило наличие значимых корреляционной и причинной связей между ними, а также сходство их циклических профилей. Оба индекса синхронно или раньше ИФО ВВП проходят поворотные циклические точки, при этом их прогностические способности усиливаются ранней доступностью информации. Возможности наукастинга экономического роста с использованием информации обследований оценены в трех спецификациях модели векторной авторегрессии с дамми-переменными. На внутривыборочном временном интервале наибольшую точность показала модель, включающая оба композитных индикатора и ИФО ВВП.Предложенный подход позволяет получать оперативные оценки перспектив роста ВВП существенно раньше публикации официальной статистической информации

The article proves the usefulness of including aggregate information from economic tendency surveys in the system of nowcasting of economic growth. The authors compare the efficiency of vector autoregressive models that include the economic sentiment indicator based on the results of Rosstat business activity and consumer expectations surveys, and the business climate indicator based on the results of the Bank of Russia enterprise monitoring.Statistical testing of time series of composite indicators and GDP volume index confirmed a significant correlation and causality between them, as well as the similarity of their cyclical profiles. Both indices pass cyclical turning points synchronously or ahead of the GDP volume index, their predictive capabilities are enhanced by the early availability of information. The capabilities of GDP growth nowcasting using survey information are assessed in three specifications of the vector autoregressive model with dummy variables. On the in-sample period, the model including two composite indicators and the GDP volume index demonstrates the highest accuracy.The proposed approach allows us to obtain flash estimates of GDP growth prospects that are significantly ahead of the official statistical information

обследования экономических тенденцийнаукастингиндекс экономических настроенийиндекс бизнес-климатакомпозитные индикаторы бизнес-цикларост ВВПвекторная авторегрессиядамми-переменныеeconomic tendency surveyseconomic sentiment indicatorbusiness climate indicatorGDP growthvector autoregressivedummy variablesReferencesUN. Handbook on Economic Tendency Survey. New York: UN Publ., 2015. 145 p.UN. Handbook on Economic Tendency Survey. New York: UN; 2015. 145 p.UNECE. Guidelines on Producing Leading, Composite and Sentiment Indicators. Geneva: UN, 2019. 125 p.UNECE. Guidelines on Producing Leading, Composite and Sentiment Indicators. Geneva: UN; 2019. 125 p.Giannone D., Reichlin L., Small D. Nowcasting: The Real-Time Informational Content of Macroeconomic Data // Journal of Monetary Economics. 2008. Vol. 55. Iss. 4. P. 665–676. doi: https://doi.org/10.1016/j.jmoneco.2008.05.010.Giannone D., Reichlin L., Small D. Nowcasting: The Real-Time Informational Content of Macroeconomic Data. Journal of Monetary Economics. 2008;55(4):665– 676. Available from: https://doi.org/10.1016/j.jmoneco.2008.05.010.Theil H. Economic Forecasts and Policy. Amsterdam: North-Holland Pub. Co., 1958.Theil H. Economic Forecasts and Policy. Amsterdam: North-Holland Publ. Co.; 1958.European Commission. The Joint Harmonised EU Programme of Business and Consumer Surveys. User Guide (Updated January 2024). URL: https://economy-finance.ec.europa.eu/document/download/4f162b92-e654-4cefbeed-38960dae1b09_en?filename=bcs_user_guide.pdf.European Commission. The Joint Harmonised EU Programme of Business and Consumer Surveys. User Guide (Updated January 2024). Available from: https://economy-finance.ec.europa.eu/document/download/4f162b92-e654-4cef-beed-38960dae1b09_en?filename=bcs_user_guide.pdf.Claveria O., Pons E., Ramos R. Business and Consumer Expectations and Macroeconomic Forecasts // International Journal of Forecasting. 2007. Vol. 23. Iss. 1. P. 47– 69. doi: https://doi.org/10.1016/j.ijforecast.2006.04.004.Claveria O., Pons E., Ramos R. Business and Consumer Expectations and Macroeconomic Forecasts. International Journal of Forecasting. 2007;23(1):47–69. Available from: https://doi.org/10.1016/j.ijforecast.2006.04.004.Christiansen C., Eriksen J.N., Moller S.V. Forecasting US Recessions: The Role of Sentiment // Journal of Banking and Finance. 2014. Vol. 49. P. 459–468. doi: https://doi.org/10.1016/j.jbankfin.2014.06.017.Christiansen C., Eriksen J.N., Moller S.V. Forecasting US Recessions: The Role of Sentiment. Journal of Banking and Finance. 2014;49:459–468. Available from: https://doi.org/10.1016/j.jbankfin.2014.06.017.Cesaroni T. The Cyclical Behavior of the Italian Business Survey Data // Empirical Economics. 2011. Vol. 41. P. 747–768. doi: https://doi.org/10.1007/s00181-010-0390-7.Cesaroni T. The Cyclical Behavior of the Italian Business Survey Data. Empirical Economics. 2011;41:747–768. Available from: https://doi.org/10.1007/s00181-010-0390-7.van Aarle B., Moons C. Sentiment and Uncertainty Fluctuations and Their Effects on the Euro Area Business Cycle // Journal of Business Cycle Research. 2017. Vol. 13. P. 225–251. doi: https://doi.org/10.1007/s41549-017-0020-y.van Aarle B., Moons C. Sentiment and Uncertainty Fluctuations and Their Effects on the Euro Area Business Cycle. Journal of Business Cycle Research. 2017;13:225–251. Available from: https://doi.org/10.1007/s41549-017-0020-y.Astolfi R. et al. The Use of Short-Term Indicators and Survey Data for Predicting Turning Points in Economic Activity: A Performance Analysis of the OECD System of CLIs During the Great Recession // OECD Statistics Working Papers. 2016. No. 2016/08. doi: https://doi.org/10.1787/5jlz4gs2pkhf-en.Astolfi R. et al. The Use of Short-Term Indicators and Survey Data for Predicting Turning Points in Economic Activity: A Performance Analysis of the OECD System of CLIs During the Great Recession. OECD Statistics Working Papers. 2016; No. 2016/08. Available from: https://doi.org/10.1787/5jlz4gs2pkhfen.Cesaroni T., Iezzi S. The Predictive Content of Business Survey Indicators: Evidence from SIGE // Journal of Business Cycle Research. 2017. Vol. 13. P. 75–104. doi: https://doi.org/10.1007/s41549-017-0015-8.Cesaroni T., Iezzi S. The Predictive Content of Business Survey Indicators: Evidence from SIGE. Journal of Business Cycle Research. 2017;13:75–104. Available from: https://doi.org/10.1007/s41549-017-0015-8.Basselier R., Liedo D. de A., Langenus G. Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys // Working Paper Research. 2017. No. 331. Brussels: National Bank of Belgium, 2017.Basselier R., de Antonio Liedo D., Langenus G. Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys. Working Paper Research. 2017. No. 331. Brussels: National Bank of Belgium; 2017.Lehmann R. The Forecasting Power of the ifo Business Survey // CESifo Working Paper No. 8291. Munich: CESifo, 2020. URL: https://www.cesifo.org/en/publications/2020/working-paper/forecasting-power-ifo-business-survey.Lehmann R. The Forecasting Power of the info Business Survey. CESifo Working Paper. 2020. No. 8291. Available from: https://www.cesifo.org/en/publications/2020/working-paper/forecasting-power-ifo-business-survey.Boudt K., Todorov V., Upadhyaya Sh. Nowcasting Manufacturing Value Added for Cross-Country Comparison // Statistical Journal of the IAOS. 2009. Vol. 26. Iss. 1. P. 15–20. doi: https://doi.org/10.3233/SJI-2009-0694.Boudt K., Todorov V., Upadhyaya Sh. Nowcasting Manufacturing Value Added for Cross-Country Comparison. Statistical Journal of the IAOS. 2009;26(1):15–2-0. Available from: https://doi.org/10.3233/SJI-2009-0694.Banbura M., Runstler G. A Look into the Factor Model Black Box – Publication Lags and the Role of Hard and Soft Data in Forecasting GDP // ECB Working Paper No. 751. Frankfurt am Main: ECB, 2007. URL: https://ssrn.com/abstract=984265.Banbura M., Rünstler G. A Look into the Factor Model Black Box – Publication Lags and the Role of Hard and Soft Data in Forecasting GDP. ECB Working Paper No. 751. Frankfurt am Main: ECB; 2007. Available from: https://ssrn.com/abstract=984265.Drechsel K., Maurin L. Flow of Conjunctural Information and Forecast of Euro Area Economic Activity // Journal of Forecasting. 2011. Vol. 30. Iss. 3. P. 336–354. doi: https://doi.org/10.1002/for.1177.Drechsel K., Maurin L. Flow of Conjunctural Information and Forecast of Euro Area Economic Activity. Journal of Forecasting. 2011;30(3):336–354. Available from: https://doi.org/10.1002/for.1177.Gayer C., Girardi A., Reuter A. The Role of Survey Data in Nowcasting Euro Area GDP Growth // European Commission Economic Papers No 538. Brussels: 2014. URL: https://ec.europa.eu/economy_finance/publications/economic_paper/2014/pdf/ecp538_en.pdf.Gayer C., Girardi A., Reuter A. The Role of Survey Data in Nowcasting Euro Area GDP Growth. European Commission Economic Papers. 2014. No. 538. Available from: https://ec.europa.eu/economy_finance/publications/economic_paper/2014/pdf/ecp538_en.pdf.Lehmann R., Wohlrabe K. Forecasting GDP at the Regional Level with Many Predictors // CESifo Working Paper No 3956. Munich: CESifo, 2012. URL: https://www.cesifo.org/en/publications/2012/working-paper/forecasting-gdp-regional-level-many-predictors.Lehmann R., Wohlrabe K. Forecasting GDP at the Regional Level with Many Predictors. CESifo Working Paper. 2013. No. 3956. Available from: https://www.cesifo.org/en/publications/2012/working-paper/forecasting-gdp-regional-level-many-predictors.D´Amato L., Garegnani L., Blanco E. GDP Nowcasting: Assessing Business Cycle Conditions in Argentina // ESTUDIOS BCRA Working Paper 2015. No. 69. C.A. de Buenos Aires: Central Bank of Argentina, 2015. URL: https://www.bcra.gob.ar/Pdfs/Investigaciones/WP_69_2015%20i.pdf.D´Amato L., Garegnani L., Blanco E. GDP Nowcasting: Assessing Business Cycle Conditions in Argentina. BCRA Working Paper Series. 2015. No. 69. Available from: https://www.bcra.gob.ar/Pdfs/Investigaciones/WP_69_2015%20i.pdf.Galli A., Hepenstrick C., Scheufele R. Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland // International Journal of Central Banking. 2019. Vol. 15. No. 2. P. 151–178. URL: https://www.ijcb.org/journal/ijcb19q2a5.pdf.Galli A., Hepenstrick C., Scheufele R. Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland. International Journal of Central Banking. 2019;15(2):151–178. Available from: https://www.ijcb.org/journal/ijcb19q2a5.pdf.Bruno G., Lupi C. Forecasting Industrial Production and the Early Detection of Turning Points // Empirical Economics. 2004. Vol. 29. P. 647–671. doi: https://doi.org/10.1007/s00181-004-0203-y.Bruno, G., Lupi, C. Forecasting Industrial Production and the Early Detection of Turning Points. Empirical Economics. 2004;29:647–671. Available from: https://doi.org/10.1007/s00181-004-0203-y.Mattos D. et al. Forecasting Brazilian Industrial Production with the VAR Model and SARIMA with Smart Dummy. Present at the 33rd CIRET Conference «Economic Tendency Surveys and Economic Policy». Copenhagen, 2016.Mattos D. et al. Forecasting Brazilian Industrial Production with the VAR Model and SARIMA with Smart Dummy [PowerPoint Presentation]. 33rd CIRET Conference «Economic Tendency Surveys and Economic Policy». Copenhagen: 2016.Дубовский Д., Кофанов Д., Сосунов К. Датировка российского бизнес-цикла // Экономический журнал ВШЭ. 2015. Т. 19. № 4. С. 554–575.Dubovskiy D., Kofanov D., Sosunov K. Dating of the Russian Business Cycle. HSE Economic Journal. 2015;19(4):554–575. (In Russ.)Смирнов С.В. Предсказание поворотных точек российского экономического цикла с помощью сводных опережающих индексов // Вопросы статистики. 2020. Т. 27. № 4. С. 53–65. doi: https://doi.org/10.34023/2313-6383-2020-27-4-53-65.Smirnov S. Predicting Turning Points of the Russian Economic Cycle Using Composite Leading Indicators. Voprosy Statistiki. 2020;27(4):53–65. (In Russ.) Available from: https://doi.org/10.34023/2313-6383-2020-27-4-53-65.Смирнов С., Кондрашов Н., Петроневич А. Поворотные точки российского экономического цикла, 1981–2015 гг. // Экономический журнал ВШЭ. 2015. Т. 19. № 4. С. 534–553.Smirnov S., Kondrashov N., Petronevich A. Dating Turning Points of the Russian Economic Cycle, 1981–2015. HSE Economic Journal. 2015;19(4):534–553. (In Russ.)Китрар Л., Липкинд Т. Анализ взаимосвязи индикатора экономических настроений и роста ВВП // Экономическая политика. 2020. T. 16. № 6. С. 8–41. doi: https://doi.org/10.18288/1994-5124-2020-6-8-41.Kitrar L., Lipkind T. Analysis of the Relationship Between the Economic Sentiment Indicator and GDP Growth. Ekonomicheskaya Politika. 2020;16(6):8–41. (In Russ.) Available from: https://doi.org/10.18288/1994-5124-2020-6-8-41.Kitrar L., Lipkind T. The Relationship of Economic Sentiment and GDP Growth in Russia in Light of The Covid-19 Crisis // Entrepreneurial Business and Economics Review (EBER). 2021. Vol. 9. No. 1. P. 7–29. doi: https://doi.org/10.15678/EBER.2021.090101.Kitrar L., Lipkind T. The Relationship of Economic Sentiment and GDP Growth in Russia in Light of the Covid-19 Crisis. Entrepreneurial Business and Economics Review. 2021:9(1):7–29. Available from: https://doi.org/10.15678/EBER.2021.090101.Китрар Л., Липкинд Т., Усов Н. Прогнозирование роста ВВП с учетом кризисных шоков на основе результатов обследований деловой активности // Вопросы статистики. 2021. Т. 28. № 4. С. 80–95. doi: https://doi.org/10.34023/2313-6383-2021-28-4-80-95.Kitrar L., Lipkind T., Usov N. Forecasting GDP Growth Considering Crisis Shocks Based on Business Survey Results. Voprosy Statistiki. 2021;28(4):80–95. (In Russ.) Available from: https://doi.org/10.34023/2313-6383-2021-28-4-80-95.Кобзев А., Андреев А. Индикаторы деловой активности и инфляции на основе мониторинга предприятий. Аналитическая записка. Март 2021. М.: ЦБ РФ, 2021. URL: https://cbr.ru/Content/Document/File/119543/analytic_note_20210322.pdf.Kobzev A., Andreev A. Indicators of Business Activity and Inflation Based on Enterprise Monitoring. Analytical Note of the Central Bank of the Russian Federation. 2021. (In Russ.) Available from: https://cbr.ru/Content/Document/File/119543/analytic_note_20210322.pdf.Ляхнова М., Коленко Ю. Оценка разрыва выпуска России по данным мониторинга предприятий // Деньги и кредит. 2024. Т. 83. № 2. С. 26–53. URL: https://rjmf.econs.online/2024/2/nowcasting-the-outputgap-in-russia-using-enterprise-monitoring-data/.Lyakhnova M., Kolenko Y. Nowcasting the Output Gap in Russia Using Enterprise Monitoring Data. Russian Journal of Money and Finance. 2024;83(2):26–53. (In Russ.) Available from: https://rjmf.econs.online/2024/2/nowcasting-the-output-gap-in-russia-using-enterprise-monitoring-data/.Банк России. Мониторинг нефинансовых предприятий: методология Банка России. М.: ЦБ РФ, 2022. URL: https://cbr.ru/Content/Document/File/130872/mm_br.pdf.Bank of Russia. Monitoring of Non-Financial Enterprises: Methodology of the Bank of Russia. 2022. (In Russ.) Available from: https://cbr.ru/Content/Document/File/130872/mm_br.pdf.Granger C.W.J. Investigating Causal Relations by Econometric Models and Cross-spectral Methods // Econometrica. 1969. Vol. 37. Iss. 3. P. 424–438.Granger C.W.J. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica. 1969;37 (3):424–438.Hodrick R.J., Prescott E.C. Postwar U.S. Business Cycles: An Empirical Investigation // Journal of Money, Credit and Banking. 1997. Vol. 29. Iss. 1. P. 1–16.Hodrick R.J., Prescott E.C. Postwar U.S. Business Cycles: An Empirical Investigation. Journal of Money, Credit and Banking. 1997;29(1):1–16.OECD. OECD System of Composite Leading Indicators. 2012. URL: https://www.oecd.org/sdd/41629509.pdf.OECD. OECD System of Composite Leading Indicators. 2012. Available from: https://www.oecd.org/sdd/41629509.pdf.Nilsson R., Gyomai G. Cycle Extraction: A Comparison of the Phase-Average Trend Method, the Hodrick-Prescott and Christiano-Fitzgerald Filters // OECD Statistics Working Papers. No. 2011/04. Paris: OECD Publishing, 2011. doi: https://doi.org/10.1787/5kg9srt7f8g0-en.Nilsson R., Gyomai G. Cycle Extraction: A Comparison of the Phase-Average Trend Method, the Hodrick – Prescott and Christiano – Fitzgerald Filters. OECD Statistics Working Papers. 2011. No. 2011/04. Paris: OECD Publishing; 2011. Available from: https://doi.org/10.1787/5kg9srt7f8g0-en.Gayer Сh. Report: The Economic Climate Tracer. A Tool to Visualise the Cyclical Stance of the Economy Using Survey Data. 2008. URL: http://ec.europa.eu/economy_finance/db_indicators/surveys/documents/studies/ economic_climate_tracer_en.pdf.Gayer Сh. Report: The Economic Climate Tracer. A Tool to Visualise the Cyclical Stance of the Economy Using Survey Data. 2008. Available from: http://ec.europa.eu/economy_finance/db_indicators/surveys/documents/studies/economic_climate_tracer_en.pdf.European Commission. European Business Cycle Indicators. Technical Paper 069. January 2024. doi: https://doi.org/10.2765/925336.European Commission. European Business Cycle Indicators. Technical Paper 069. January 2024. Available from: https://doi.org/10.2765/925336.Безбородова А. SVAR: анализ и прогнозирование основных макроэкономических показателей // Банкаўскі веснік. Исследования Банка № 11. 2017. URL: https://www.nbrb.by/bv/pdf/supplements/58.pdf.Bezborodova A. SVAR: Analysis and Forecasting of the Main Macroeconomic Indicators. Bankovsky Vestnik: Issledovania Banka. 2017;(11):1–30. (In Russ.)Орлов К. Построение большой байесовской векторной авторегрессионной модели для Казахстана // Департамент денежно-кредитной политики Банка Казахстана. Экономическое исследование № 2021-1. URL: https://www.nationalbank.kz/file/download/65031.Orlov K. Construction of a Large Bayesian Vector Autoregressive Model for Kazakhstan. Department of Monetary Policy of the Bank of Kazakhstan. Economic Research No. 2021-1. (In Russ.) Available from: https://www.nationalbank.kz/file/download/65031.Sims C.A. Macroeconomics and Reality // Econometrica. 1980. Vol. 48. P. 1–48.Sims C.A. Macroeconomics and Reality. Econometrica. 1980;48:1–48.Litterman R. Forecasting with Bayesian Vector Autoregressive Model – Five Years of Experience // Journal of Business & Economic Statistics. 1986. Vol. 4. Iss. 1. P. 25–38. doi: https://doi.org/10.2307/1391384.Litterman R. Forecasting with Bayesian Vector Autoregressive Model – Five Years of Experience. Journal of Business & Economic Statistics. 1986;4(1):21–36. Available from: https://doi.org/10.2307/1391384.Gupta R., Jurgilas M., Kabundi A. The Effect of Monetary Policy on Real House Price Growth in South Africa: A Factor-Augmented Vector Autoregression (FAVAR) Approach // Economic Modelling. 2010. Vol. 27. Iss. 1. P. 315–323. doi: https://doi.org/10.1016/j.econmod.2009.09.011.Gupta R., Jurgilas M., Kabundi A. The Effect of Monetary Policy on Real House Price Growth in South Africa: A Factor-Augmented Vector Autoregression (FAVAR) Approach. Economic Modelling. 2010;27(1):315–323. Available from: https://doi.org/10.1016/j.econmod.2009.09.011.

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