Monthly indicator and nowcast of nominal GDP with flow of funds
DOI:
https://doi.org/10.22394/1993-7601-2026-82-105-123Keywords:
номинальный ВВП, темпоральное дезагрегирование, потоки фондов, DFM, наукаст, прогнозированиеAbstract
We construct a monthly indicator and produce a nowcast of nominal GDP in Russia using operational data. The data used are bank statements and the ruble price of Russian oil. This approach allows for the nowcast of nominal GDP ahead of official statistics by 3–5 months. Nominal GDP data is published by Rosstat only on a quarterly basis and with a significant delay after the reporting quarter. Therefore, high-frequency and operational assessment of the indicator is important for assessing macroeconomic dynamics. It is also relevant for analysing sectors such as fiscal, financial, and external. We use statistics of the 0409101 form "Revolving statement of accounting accounts of a credit institution" as bank reporting data. To assess the quality of the models, we consider indicators of the indicator's variability over time and the accuracy of the GDP. Model performance accuracy is assessed using both actual and vintage data. The paper uses models of temporal disaggregation, MIDAS, and the DFM model.Downloads
References
Гареев М. Ю., Полбин А. В. (2022). Наукастинг: оценка изменения ключевых макроэкономических показателей с использованием методов машинного обучения. Вопросы экономики, 8, 133–157. DOI: 10.32609/0042-8736-2022-8-133-157.
Жемков М. (2022). Оценка месячного индикатора ВВП методами темпорального дезагрегирования. Деньги и кредит, 81 (2), 79–104.
Поршаков А. С., Пономаренко А. А., Синяков А. А. (2016). Оценка и прогнозирование ВВП России с помощью динамической факторной модели. Журнал Новой экономической ассоциации, 30 (2), 60–76. DOI: 10.2139/ssrn.5043879.
Полбин А. В., Шумилов А. В. (2023). Прогнозирование инфляции в России с помощью TVP-модели с байесовским сжатием параметров. Вопросы статистики, 30 (4), 22–32. DOI: 10.34023/2313-6383-2023-30-4-22-32.
Полбин А. В., Шумилов А. В. (2025). Наукастинг и прогнозирование ВВП России и его компонентов с помощью квантильных моделей. Прикладная эконометрика, 79, 5–26. DOI: 10.22394/1993-7601-2025-79-5-26.
Станкевич И. П. (2020). Сравнение методов наукастинга макроэкономических индикаторов на примере российского ВВП. Прикладная эконометрика, 59, 113–127. DOI: 10.22394/1993-7601-2020-59-113-127.
Фокин Н. Д. (2023). Наукастинг и прогнозирование основных российских макроэкономических показателей с помощью MFBVAR-модели. Экономическая политика, 18 (3), 110–135. 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.
Galbraith J.W., Tkacz G. (2015). Nowcasting GDP with electronic payments data. ECB Statistics Paper, 10. DOI: 10.2866/444926.
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.
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.
Sax C., Steiner P. (2013). Temporal disaggregation of time series. The R Journal, 5 (2), 80–88. DOI: 10.32614/RJ-2013-028.
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.
Zhemkov M. (2021). Nowcasting Russian GDP using forecast combination approach. International Economics, 168, 10–24. DOI: 10.1016/j.inteco.2021.07.006.
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.
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.
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.
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.
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.
