| In the section | Articles |
| Title of the article | Nowcasting GRP in the Russian Economy Using Quantile Econometric Models |
| Pages | 99-119 |
| Author | Alexandra Borisovna Chudaeva Intern-Researcher Center for Mathematical Modeling of Economic Processes, Institute of Applied Economic Research, Russian Presidential Academy of National Economy and Public Administration 82 Vernadsky Av., buil. 1, Moscow, 119571, Russian Federation Institute for Research on Socio-Economic Transformations and Financial Policy, Financial University 49/2 Leningradsky Av., Moscow, 125167, Russian Federation This email address is being protected from spambots. You need JavaScript enabled to view it. ORCID: 0009-0009-7150-9414 |
| Abstract | The paper aims to develop a model for probabilistic nowcasting of the Russian regions’ GRP growth rate, as the official statistiсs on this indicator are published with a long delay. Taking into account uncertainty and risk management problems, probabilistic nowcasting becomes especially relevant. However, this topic is poorly developed in the national researches considering regional forecasting. Linear and quantile regression, as well as quantile neural networks of various specifications are used in this paper as modeling tools. The models are estimated with the help of regional panel data and further compared in terms of interval and point nowcasts’ accuracy. Pooled additive quantile neural network turns out to be a promising model as it provides the most valid picture of the GRP growth slowing risks and is generally more robust in building point nowcasts. But when modeling the right-hand side of the distribution, that is, the scenario of extreme GRP growth, pooled linear regression is preferred. In turn, models with fixed individual effects, on average, give unsatisfactory results, but they are optimal for some regions. The constructed models can be used by policymakers to monitor recession risks and may help to to take prompt anti-crisis economic policy measures |
| Code | 332.14+330.43 |
| JEL | C23, C53, E27, R11 |
| DOI | https://dx.doi.org/10.14530/se.2025.4.099-119 |
| Keywords | gross regional product, nowcasting, panel data, interval forecasting, quantile regression, neural network, regions, Russia |
| Download | |
| For citation | Chudaeva A.B. Nowcasting GRP in the Russian Economy Using Quantile Econometric Models. Prostranstvennaya Ekonomika = Spatial Economics, 2025, vol. 21, no. 4, pp. 99–119. https://dx.doi.org/10.14530/se.2025.4.099-119 (In Russian) |
| References | 1. Adrian T., Boyarchenko N., Giannone D. Vulnerable Growth. American Economic Review, 2019, vol. 109, no. 4, pp. 1263–1289. 2. Adrian T., Grinberg F., Liang N., Malik S. The Term Structure of Growth-at-Risk. IMF. Working Paper No. 180, 2018, 40 p. 3. Borodin S.N. Forecasting the Economic Development of the Region Based on Regression and Neural Network Models. Russian Economic Bulletin, 2024, vol. 7, no. 1, pp. 290–300. (In Russian). 4. Cannon A.J. Quantile Regression Neural Networks: Implementation in R and Application to Precipitation Downscaling. Computers & Geosciences, 2011, vol. 37, issue 9, pp. 1277–1284. 5. Chudaeva A. Analysis of Inflation Risk Factors in Russia. Denigi i Kredit = Russian Journal of Money and Finance, 2025, vol. 84, no. 1, pp. 60–92. (In Russian). 6. Fokin N.D. Nowcasting and Forecasting Key Russian Macroeconomic Variables with the MFBVAR Model. Ekonomicheskaya Politika = Economic Policy, 2023, vol. 18, no. 3, pp. 110–135. (In Russian). 7. Fosten J., Greenaway-McGrevy R. Panel Data Nowcasting. Econometric Reviews, 2022, vol. 41, issue 7, pp. 675–696. 8. Gorshkova T. Forecasting GRP using Spatial Correlation Methods. Nauchny Vestneyk IEP im. Guydara.ru [Gaidar Institute Scientific Bulletin], 2018, vol. 10, no. 129, pp. 37–47. (In Russian). 9. Kazakova M.V., Fokin N.D. Testing Forecasting Properties of Different Approaches to Interval Forecasting (Using the Example of Inflation In Russia). Voprosy Statistiki [Statistical Issues], 2024, vol. 31, no. 5, pp. 23–40. (In Russian). 10. Kipriyanov A. Comparison of Models for Growth-at-Risk Forecasting. Denigi i Kredit = Russian Journal of Money and Finance, 2022, vol. 81, no. 1, pp. 23–45. (In Russian). 11. Koenker R. Quantile Regression for Longitudinal Data. Journal of Multivariate Analysis, 2004, vol. 91, issue 1, pp. 74–89. 12. Koenker R. Quantile Regression. Cambridge: Cambridge University Press, 2005, 368 p. 13. Orlova I.V., Filonova E.S. Analysis and Forecasting the Nominal Wage of the Population of the Russian Federation Taking into Account its Regional Differentiation. Fundamentalnye Issledovaniya [Fundamental Research], 2021, no. 5, pp. 67–74. (In Russian). 14. Polbin A.V., Shumilov A.V. Forecasting Output Growth of Russian Manufacturing Industries using Panel Data Models. Ekonomicheskoe Razvitie Rossii = Russian Economic Developments, 2022, vol. 29, no. 2, pp. 15–19. (In Russian). 15. Vasilyeva R.I. The Impact of Fiscal Decentralization on Economic Growth of Regions of the Russian Federation. Vestnik Universiteta [Bulletin of the University], 2022, vol. 1, no. 12, pp. 145–153. (In Russian). 16. Vavilova D.D., Zerari R. Analysis, Modeling and Forecasting of the Gross Regional Product Dynamics Based on the Production Function. Ekonomika. Informatika = Economics. Information Technologies, 2024, vol. 51, no. 1, pp. 5–17. (In Russian). |
| Financing | The article is based on the results of research carried out at the expense of the Scientific Fund of the Financial University |
| Submitted | 23.06.2025 |
| Approved after reviewing | 13.09.2025 |
| Accepted for publication | 09.10.2025 |
| Available online | 25.12.2025 |