Using additive regression models for short-term forecasting of financial macro-indicators and assessing the potential for financing megaprojects
// Finance and Management. – 2023. – № 2.
– P. 15-26.
Read the article
Abstract: The subject of this article is the issue of using additive regression models to predict financial indicators at the macro level. At the same time, special attention is paid to the impact of the economy monetization on the possibility of attracting funding for global development projects (megaprojects). It is shown that the main drawback of the most common forecasting models today is their situation-dependent nature. This, in turn, creates difficulties with the initial setup of the models and the subsequent interpretation of the results obtained, limiting the scope of the models, making the use of this toolkit difficult for financial professionals who do not have special mathematical training. With the help of modeling, forecast values of the gross domestic product (GDP) and money supply (M2) for the short-term time obtained, on the basis of which the expected value of the level of the economy monetization was calculated. Based on a predictive assessment of the level of monetization, it is shown that at the moment the country has a limited potential for increasing domestic debt, which, in the conditions of closing access to international capital markets and partial blocking of state reserves, can become a factor in disrupting the financing of megaprojects for the economy structural modernization. Directions for improving the monetary policy aimed at correcting this situation and increasing domestic investment activity are proposed.
Keywords: structural modernization, financial modeling, economy monetization, megaprojects, macro-indicators, short-term forecasting, money-credit policy, money supply M2, GDP, additive regression models
Vertakova, Yu.V. (2016). Review of economic approaches and models for GDP forecasting. Economics and Management, 2(124), 22–29.
Abdikeev, N.M., Pashchenko, F.F., Gusev, V.B., Ivanyuk, V.A., Grineva, N.V., Kuznetsov, N.V., Malikova, O.I., & Kuznetsov, V.I. (2019). Modeling the long-term socio-economic development of Russia. Moscow, Russia: KnoRus.
Liu, H. & Chan, W.S. (2019). Forecasting the GDP Growth Rate Using Mixed-Frequency Data. International Journal of Forecasting, 35(3), 1002–1015.
Matrosov, V.V. & Shalfeev, V.D. (2021). Modeling economic and financial cycles: generation and synchronization. Izvestiya vuzov, Vol. 29, Issue 4, 515–537.
Forecasting and model apparatus. Central Bank of the Russian Federation. Retrieved from: http://www.cbr.ru/dkp/system_p/
Guide to quarterly national accounts. International Monetary Fund. Retrieved from: https://www.imf.org/external/pubs/ft/qna/pdf/2017/QNAManual2017RUS.pdf