Построение месячного индикатора и наукастинг номинального ВВП с использованием данных банковской статистики
DOI:
https://doi.org/10.22394/1993-7601-2026-82-105-123Ключевые слова:
номинальный ВВП, темпоральное дезагрегирование, потоки фондов, DFM, наукаст, прогнозированиеАннотация
В работе производится построение месячного индикатора и наукастинг квартального показателя номинального ВВП в России на основе оперативных данных, в качестве которых используются данные банковской отчетности и рублевая цена на российскую нефть, что позволяет осуществлять наукастинг номинального ВВП с опережением официальной статистики на 3–5 месяцев. Поскольку данные по номинальному ВВП публикуются Росстатом только на квартальной частотности и со значительной задержкой после конца отчетного квартала, высокочастотная и оперативная оценка показателя представляет значительную актуальность для общей оценки макроэкономической динамики, а также анализа таких секторов экономики, как бюджетный, финансовый и внешний. В качестве данных банковской отчетности используется статистика формы 0409101 «Оборотная ведомость по счетам бухгалтерского учета кредитной организации». Качество моделей оценивается показателями изменчивости индикатора во времени и точности наукаста ВВП. При этом точность оценивается как на фактических, так и на винтажных данных. В работе используются модели темпорального дезагрегирования, MIDAS и DFM.Скачивания
Библиографические ссылки
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Porshakov A. S., Ponomarenko A. A., Sinyakov A. A. (2016). Nowcasting and short-term forecasting of Russian GDP with a dynamic factor model. Journal of the New Economic Association, 30 (2), 60–76 (in Russian). DOI: 10.2139/ssrn.5043879.
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Stankevich I. (2020). Comparison of macroeconomic indicators nowcasting methods: Russian GDP case. Applied Econometrics, 59, 113–127 (in Russian).
Fokin N. D. (2023). Nowcasting and forecasting of key Russian macroeconomic indicators using an MFBVAR model. Economic Policy, 18 (3), 110–135 (in Russian). DOI: 10.18288/1994‑5124‑2023‑3‑110‑135.
Barbone L., Bodo G., Visco I. (1981). Costi e profitti nell'industria in senso stretto: Un'analisi su serie trimestrali, 1970–1980. Bollettino della Banca d'Italia, 36, 465–510.
Barcellan R., Di Fonzo T., Raffaele D., Staplehurst V., Buono D. (2003). Ecotrim: A program for temporal disaggregation of time series. Workshop on Quarterly National Accounts, Eurostat, Theme, 2, 79–95.
Barlas A.B., Mert S.G., Isa B.O., Ortiz A., Rodrigo T., Soybilgen B., Yazgan E. (2024). Big data financial transactions and GDP nowcasting: The case of Turkey. Journal of Forecasting, 43 (2), 227–248. DOI: 10.1002/for.3032.
Bezemer D.J. (2010). Understanding financial crisis through accounting models. Accounting, Organizations and Society, 35 (7), 676–688. DOI: 10.1016/j.aos.2010.07.002.
Bournay J., Laroque G. (1979). Réflexions sur la méthode d'elaboration des comptes trimestriels. Annales de l'inséé, 3–30. DOI: 10.2307/20075332.
Boyko V., Kislyak N., Nikitin M., Oborin O. (2020). Methods for estimating the gross regional product leading indicator. Russian Journal of Money and Finance, 79 (3), 3–29. DOI: 10.31477/rjmf.202003.03.
Burca V., Mates D., Bogdan O. (2020). Analysis on the effects of quality of financial statements over GDP forecasting models: An empirical cross-country approach. International Journal of Economics and Business Administration, 8 (4), 683–698. DOI: 10.2478/sbe-2020-0056.
Chapman J., Desai A. (2021). Using payments data to nowcast macroeconomic variables during the onset of COVID‑19. Staff Working Papers 21–2. Bank of Canada. DOI: 10.34989/swp-2021-2.
Chow G.C., Lin A. (1971). Best linear unbiased interpolation, distribution, and extrapolation of time series by related series. The Review of Economics and Statistics, 372–375. DOI: 10.2307/1928739.
Dafermos Y., Nikolaidi M., Galanis G. (2017). A stock-flow-fund ecological macroeconomic model. Ecological Economics, 131, 191–207. DOI: 10.1016/j.ecolecon.2016.08.013.
Felber L., Beyeler S. (2023). Nowcasting economic activity using transaction payments data. SNB Working Paper, 01/2023. DOI: 10.2139/ssrn.4422430.
Fernandez R.B. (1981). A methodological note on the estimation of time series. The Review of Economics and Statistics, 63 (3), 471–476. DOI: 10.2307/1924371.
Fokin N.D. (2023). Nowcasting and forecasting of key Russian macroeconomic indicators using an MFBVAR model. Economic Policy, 18 (3), 110–135 (in Russian). DOI: 10.18288/1994-5124-2023-3-110-135.
Galbraith J.W., Tkacz G. (2015). Nowcasting GDP with electronic payments data. ECB Statistics Paper, 10. DOI: 10.2866/444926.
Gareev M.Y., Polbin A.V. (2022). Nowcasting Russia's key macroeconomic variables using machine learning. Voprosy Ekonomiki, 8, 133–157 (in Russian). DOI: 10.32609/0042-8736-2022-8-133-157.
Gonçalves R.N.C. (2022). Nowcasting Brazilian GDP with electronic payments data. Working Paper Series, 564. Banco Central do Brasil.
Gornostaev D., Ponomarenko A., Seleznev S., Sterkhova A. (2022). A real-time historical database of macroeconomic indicators for Russia. Russian Journal of Money and Finance, 81 (1), 88–103. DOI: 10.31477/rjmf.202201.88.
Green C.J., Murinde V., Moore T. (2002). The flow of funds. Finance and Development, June.
Konchitchki Y., Patatoukas P.N. (2014). Accounting earnings and gross domestic product. Journal of Accounting and Economics, 57 (1), 76–88. DOI: 10.1016/j.jacceco.2013.10.001.
Koop G., McIntyre S., Mitchell J., Poon A. (2020). Reconciled estimates of monthly GDP in the US. Discussion Paper DP‑2020-16. Economic Statistics Centre of Excellence, London. DOI: 10.2139/ssrn.4006477.
Krantz S. (2023). Dynamic factor models a very short introduction. https://cran.r-project.org/web/packages/dfms/vignettes/dynamic_factor_models.pdf.
Litterman R.B. (1983). A random walk, Markov model for the distribution of time series. Journal of Business & Economic Statistics, 1 (2), 169–173. DOI: 10.1080/07350015.1983.10509336.
Mikosch H., Solanko L. (2019). Forecasting quarterly Russian GDP growth with mixed-frequency data. Russian Journal of Money and Finance, 78 (1), 19–35. DOI: 10.31477/rjmf.201901.19.
Polbin A.V., Shumilov A.V. (2023). Forecasting inflation in Russia using a TVP model with Bayesian shrinkage. Voprosy statistiki, 30 (4), 22–32 (in Russian). DOI: 10.34023/2313-6383-2023-30-4-22-32.
Polbin A., Shumilov A. (2025). Nowcasting and forecasting Russian GDP and its components using quantile models. Applied Econometrics, 79, 5–26 (in Russian). DOI: 10.22394/1993-7601-2025-79-5-26.
Porshakov A.S., Ponomarenko A.A., Sinyakov A.A. (2016). Nowcasting and short-term forecasting of Russian GDP with a dynamic factor model. Journal of the New Economic Association, 30 (2), 60–76 (in Russian). DOI: 10.2139/ssrn.5043879.
Quilis E.M. (2013). A Matlab library of temporal disaggregation and interpolation methods. Madrid: Ministry of Economy and Competitiveness. https://back.nber.org/appendix/w22279/analysis/code/TD/TD.pdf.
Santos Silva J.M.C., Cardoso F.N. (2001). The Chow–Lin method using dynamic models. Economic Modelling, 18 (2), 269–280. DOI: 10.1016/S0264-9993(00)00039-0.
Sax C., Steiner P. (2013). Temporal disaggregation of time series. The R Journal, 5 (2), 80–88. DOI: 10.32614/RJ-2013-028.
Stankevich I. (2020). Comparison of macroeconomic indicators nowcasting methods: Russian GDP case. Applied Econometrics, 59, 113–127 (in Russian).
Zhemkov M. (2021). Nowcasting Russian GDP using forecast combination approach. International Economics, 168, 10–24. DOI: 10.1016/j.inteco.2021.07.006.
Zhemkov M. (2022). Assessment of monthly GDP growth using temporal disaggregation methods. Russian Journal of Money and Finance, 81 (2), 79–104 (in Russian). DOI: 10.22394/1993-7601-2020-59-113-127.
