Subject. The article discusses possible lines for improving the methodological framework of the long-term economic forecast of gold production in the Russian Federation, relying upon the mathematical apparatus of econometric models. Objectives. We devise an economic-mathematical model for predicting the gold production with respect to the specifics of the economic development in Russia’s gold mining industry. Methods. The study is based in the correlation and regression methods for analyzing publicly available statistical data on the gold market. The least square method is taken as the methodological approach to designing the economic-mathematical model for forecasting. Results. Each gold deposit is found to be distinctive, having its own qualities, which seriously differ from those of other gold deposit. As a result of the analysis, we discovered key factors, which significantly influence the gold production, such as demand and price for gold, the amount and quality of geological reserves of gold, the amount of investment to be made in geological prospecting, national exchange rate and key rate of the Bank of Russia. We substantiated the use of the linear three-factor model, which involves gold production volumes in Russia, demand for gold, national exchange rate and price for gold as regressors. According to our estimates, in the Russian Federation, gold production will have reached 370–380 tons by 2025. Conclusions. Based on the least square method, the structural forecast apparatus does not account for geological and geographic-economic distinctions of gold deposits due to their unique nature. Therefore, determining regressors for the model, we predominantly focuses on open access data.
Keywords: long-term forecasting, econometric models, demand and production of gold, correlation and regression analysis
Almon Cl. Iskusstvo ekonomicheskogo modelirovaniya [The Craft of Economic Modeling]. Moscow, MAKS Press Publ., 2012, 646 p.
Baranov E.F., Elsakova A.V., Korneva E.S., Staritsyna E.A. [Decomposition analysis based on input-output tables]. Voprosy statistiki, 2016, no. 10, pp. 44–56. (In Russ.) URL: Link
Suvorov N.V., Treshchina S.V., Beletskii Yu.V. [Design of methods for long-term forecasting of development trends in the Russian economy (Methodology and model toolkit)]. Problemy prognozirovaniya = Problems of Forecasting, 2020, no. 6, pp. 66–80. URL: Link (In Russ.)
Kuleshov V.V., Alekseev A.V., Yagol'nitser M.A. [Methods of cognitive analysis in devising and substantiating strategies of economic development]. Problemy prognozirovaniya = Problems of Forecasting, 2019, no. 2, pp. 104–112. URL: Link (In Russ.)
Shashank G., Shalini G. Modeling Economic System Using Fuzzy Cognitive Maps. International Journal of System Assurance Engineering and Management, 2017, vol. 8, no. 2, pp. 1472–1486. URL: Link
Suvorov N.V., Treshchina S.V., Beletskii Yu.V., Balashova E.E. [Balance and factor models as a tool for analyzing and forecasting the structure of the economy]. Nauchnye trudy: Institut narodnohozyajstvennogo prognozirovaniya RAN = Scientific Works: Institute for Economic Forecasting of the Russian Academy of Sciences, 2017, vol. 5, no. 5, pp. 50–75. URL: Link (In Russ.)
Shirov A.A., Sayapova A.R., Yantovskii A.A. [Integrated input-output balance as an element of analysis and forecasting in the post-Soviet space]. Problemy prognozirovaniya = Problems of Forecasting, 2015, no. 1, pp. 11–22. URL: Link (In Russ.)
Ivanter V.V., Ksenofontov M.Yu. [A concept of the constructive forecast of the long-term economic growth in Russia]. Problemy prognozirovaniya = Problems of Forecasting, 2012, no. 6, pp. 4–13. URL: Link (In Russ.)
Auzina L.I. [Predicting groundwater rise in historical centres of Eastern Siberian cities]. Nauki o Zemle i nedropol'zovanie = Earth Sciences and Subsoil Use, 2021, vol. 44, no. 1, pp. 73–84. (In Russ.) URL: Link
Vasil'ev A.A. [Genesis of Hybrid Forecasting Models Based on Forecast Combination]. Vestnik Tverskogo gosudarstvennogo universiteta. Ser. Ekonomika i upravlenie = Bulletin of Tver State University. Ser. Economics and Management, 2014, no. 1, pp. 316–331. (In Russ.)
Bozkurt O.O., Biricik G., Taysi Z.C. Artificial Neural Network and Sarima Based Models for Power Load Forecasting in Turkish Electricity Market. PLOS ONE, 2017, no. 12. URL: Link
Wang B., Hao W.N., Chen G. et al. Wavelet Neural Network Forecasting Model Based on ARIMA. Applied Mechanics and Materials, 2013, vol. 347-350, pp. 3013–3018. URL: Link
Myasoedov S.A. [The Analysis of trends in the development of the gold mining industry in Russia]. Mikroekonomika = Microeconomics, 2009, no. 8, pp. 193–200. (In Russ.)
Zaernyuk V.M., Chernikova L.I., Zabaikin Yu.V. [The gold industry of Russia: Trends, problems and prospects for the development]. Finansovaya analitika: problemy i resheniya = Financial Analytics: Science and Experience, 2017, vol. 10, iss. 9, pp. 972–986. (In Russ.) URL: Link