Modeling of Grain Exports Forecast Based on Correlation-Regressive Analysis

Purpose of the research. The growth in domestic grain exports is one of the key factors in the positive development of the country’s economy, which is due, among other things, to the growing demand for this product in various regions of the world. Therefore, Russian exporters are focused on implementing a strategy aimed at strengthening their position in the international market. However, there are a number of factors that can influence grain export volumes, either upward or downward, which negatively affects both revenues andthe willingness of exporters to ensure conditions for export cargo flows in line with market needs. Therefore, the purpose of this paper is to construct and solve a forecast model, the application of which will enable the most accurate planning of exporters’ resource potential.

Materials and methods. To achieve this purpose, statistical data over a relatively long period (27 years) were used. Statistical and correlation-regression analysis methods were used in developing the dynamic econometric model. This allowed us to explore the relationships between variables, as well as establish the structure and test the time series for cointegration. The model solution uses the least squares method, which involves checking it for compliance with the relevant requirements.

Results. Using a matrix of paired correlation coefficients, intercorrelated variables were excluded, and the most significant factors for forecasting were identified. It has been established that the closest possible connection exists between the level of Russian grain exports, gross volumes of grain production, global grain export volumes, and global grain shipments. Given the relatively strong intercorrelation between global grain exports and global grain shipments, the authors decided to include the global grain export index in the model. To eliminate “spurious” correlation, which could be caused by trends and periodic fluctuations, a study of the time series structure of the indexes was conducted, not only visually but also using autocorrelation functions. As a result, a model was constructed for the dependence of Russian grain exports on gross production volumes in Russia and global grain exports. Tests confirmed the adequacy of the relationships between the indexes. The construction of the model made it possible to quantify the effect of factor features on the change in the result: an increase in grain production by 1 million tons leads to an increase in grain exports by an average of 313 thousand tons, while an increase in global exports causes an increase in Russian exports by 109 thousand tons.

Conclusion. To validate the econometric model, actual and forecasted factor values were substituted into it. The forecast values obtained as a result of applying the author’s model almost completely coincided with the forecast made by the Food and Agriculture Organization of the United Nations for 2025, demonstrating its adequacy.

1. Yarkova T. M. Problems and Prospects for the Development of Grain Economy in Russia in the Context of World Trade. Ekonomika, predprinimatel’stvo i pravo = Economy, Entrepreneurship and Law. 2024; 14; 7: 3861-3872. DOI: 10.18334/epp.14.7.121388. (In Russ.)

2. Shalayeva L. V. Grain Resources of the Russian Federation in the Context of Macroeconomic Shocks: Assessment of Trends in the Context of Main Factors. Prodovol’stvennaya politika i bezopasnost’ = Food Policy and Security. 2023; 10; 1: 157-174. DOI: 10.18334/ppib.10.1.116924. (In Russ.)

3. Shalayeva L. V. World and Russian Grain Market: Assessment of Trends and Prospects. Prodovol’stvennaya politika i bezopasnost’ = Food Policy and Security. 2023; 10; 2: 287-302. DOI: 10.18334/ppib.10.2.117014. (In Russ.)

4. Ryazanov V.A. The Impact of the World Market on Domestic Grain Prices in Russia. Vestnik Instituta ekonomiki Rossiyskoy akademii nauk = Bulletin of the Institute of Economics of the Russian Academy of Sciences. 2022; 4: 50-59. DOI: 10.52180/2073-6487_2022_4_50_59. (In Russ.)

5. Savenkova I.V., Matveyeva O.P. Realization of the Export Potential of the Russian Federation in the World Wheat Market. Vestnik Belgorodskogo universiteta kooperatsii, ekonomiki i prava = Bulletin of the Belgorod University of Cooperation, Economics and Law. 2021; 5(90): 47-57. DOI: 10.21295/2223-5639-2021-5-47-57. (In Russ.)

6. Davydovskiy A. G. Modeling and forecasting wheat exports and imports under the influence of cosmogeophysical fluctuations based on Big Data technologies [Internet]. AgroEkoInzheneriya = AgroEcoEngineering. 2020; 2(103). Available from: https://cyberleninka.ru/article/n/modelirovaniei-prognozirovanie-eksporta-i-importa-pshenitsypod-vliyaniem-kosmogeofizicheskih-fluktuatsiy-naosnove-tehnologiy-big. (In Russ.)

7. Statistika stran i regionov = Statistics of countries and regions [Internet]. Available from: https://statbase.ru/. (In Russ.)

8. Eksportnyye gorizonty = Export horizons [Internet]. Available from: https://www.kommersant.ru/doc/7284737. (In Russ.)

9. Shirnayeva S.Yu. Econometric modeling and forecasting of indicators of export of goods of the Russian Federation. Fundamental’nyye issledovaniya = Fundamental research. 2020; 6: 172-177. DOI: 10.17513/fr.42796. (In Russ.)

10. Pererva O.L., Stepanov S.Ye., Nezimova S.S. Comparison of econometric models and methods of business analytics for predicting enterprise bankruptcy [Internet]. Naukovedeniye = Naukovedenie. 2017; 9: 6. Available from: https://naukovedenie.ru/PDF/82EVN617.pdf. (In Russ.)

11. Babeshko L.O., Bich M.G., Orlova I.V. Ekonometrika i ekonometricheskoye modelirovaniye = Econometrics and econometric modeling. Moscow: INFRA-M; 2023. 387 p. (In Russ.)

12. Bobrovskaya Ye.D., Polbin A.V. Econometric modeling of the demand function for shortterm housing rentals (using Airbnb in Moscow as an example). Zhurnal Novoy ekonomicheskoy assotsiatsii = Journal of the New Economic Association. 2023; 2(59): 64–84. (In Russ.)

13. Ayyubova N.S. Analysis of the impact of world oil prices on GDP (using the Republic of Azerbaijan as an example). Statistika i Ekonomika = Statistics and Economics. 2023; 20(2): 22-41. DOI: 10.21686/2500-3925-2023-2-21-40. (In Russ.)

14. Gayduk V. I., Mikitayev I. R. Forecasting the development of the regional grain market. Politematicheskiy setevoy elektronnyy nauchnyy zhurnal Kubanskogo gosudarstvennogo agrarnogo universiteta = Polythematic online electronic scientific journal of the Kuban State Agrarian University. 2016; 120: 1047-1066. (In Russ.)

15. Eksport zerna iz Rossii dostig rekordnykh 72 mln tonn v 2024 godu = Russia’s grain exports reached a record 72 million tons in 2024 [Internet]. Available from: https://www.vedomosti.ru/business/news/2025/01/28/1088824-eksport-zerna. (In Russ.)

16. IGC prognoziruyet rekordnoye proizvodstvo zerna v sezone 2025/26 = IGC forecasts record grain production in the 2025/26 season [Internet]. Available from: https://oleoscope.com/news/igc-prognoziruetrekordnoe-proizvodstvo-sezona/. (In Russ.)

17. V 2025 godu eksport zerna mozhet sostavit’ 45 mln ton = Grain exports may reach 45 million tons in 2025 [Internet]. Available from: https://www.agroinvestor.ru/markets/news/43532-v-2025-godu-eksport-zerna-mozhet-sostavit-45-mln-tonn/. (In Russ.)