UDC 336.74
DOI: 10.36871/ek.up.p.r.2024.04.06.008
Authors
Stepan V. Phinochko,
Olga V. Krioni,
Ufa University of Science and Technology, Ufa, Russia
Abstract
This article is devoted to the development of a mathematical model for forecasting the dynamics of
monetary aggregates M0–M3. The relevance of this topic is due to the demand for monetary aggregates as one
of the key indicators for the purposes of substantiating the monetary policy of the Bank of Russia. Based on the
forecasts of these macroeconomic indicators, the issues of interest rates, inflation and lending volume regulation
are solved.
The research use such methods as the method of comparison, relative and average values, graphical and
tabular presentation of data, grouping, method of correlation and regression analysis, etc. Modeling based on the
apparatus of neural networks was also used to build a forecast of the dynamics of monetary aggregates.
Scientific novelty of the work lies in the fact that the unique models of forecasting the dynamics of monetary
aggregates were built, as well as the forecast of key indicators for the last quarter of 2023.
The practical significance of this study lies in the possibility of applying the best of the developed multifactor
models, as well as models using ANN, for further forecasting of liquidity indicators.
Keywords
monetary aggregates, money supply, banking system, central bank, liquidity indicators, correlation and regression analysis, multifactor models, forecasting.
