Alan Kanamatovich Karaev

The research aims to explore the dynamic links between government investment in defense and civilian R&D and labor productivity in Russia, in order to devise optimal public management techniques for fostering economic growth and innovation. The objective of the study is to analyze the potential impact of defense and civilian R&D on labor productivity growth in Russia from 2005 to 2024. The study’s methodology framework is rooted in economic growth theories and missionoriented innovation policy concepts. The core method involves wavelet coherence analysis with continuous wavelet transforms. This method enables the assessment of the nonlinear relationship’s intensity, using the wavelet coherence index, and its direction, by analyzing phase differences between defense and civilian R&D and labor productivity in Russia at various time scales. The study’s information is based on the Russian Ministry of Finance and Rosstat: GDP, number of employees, government expenditure on consumption and investment for the period 2005–2023. The study’s findings suggest that, over the medium term, greater investment in defense R&D boosts labor productivity in Russia and fuels the multiplier effect for the nation’s economic innovation. Investing in R&D enhances aggregate demand and triggers economic shifts, paving the way for novel technical and economic paradigms beyond what spontaneous marketdriven labor productivity growth provides. The work’s endings hold significance for public financial policy development in managing economic growth and the labor market, which will help combat economic stagnation and advance the Russian economy.

Precious metals are key elements of the asset class in the international financial market. They have a number of advantages: they are a valuable tool for preserving wealth, they are not at risk of default, and they provide effective protection against inflation. In addition, they are considered safe-haven assets during periods of unfavorable geopolitical conditions. The purpose of this research is to find methods for improving the accuracy of predicting the price of precious metals through the use of advanced machine learning models. The methodological basis is the concept of predicting the effectiveness of precious metals as a stabilizing asset (and a safe-haven asset) in the context of future financial instability. The research methodology is based on evaluating the effectiveness of machine learning models using information criteria such as AIC and BIC, determination coefficients R2 and Adj-R2, and RMSE and MAPE as measures of the closeness between the actual and predicted values of time series and error measures. The information base for this study was based on daily data on the prices of four precious metals (gold, silver, platinum, and palladium), provided by Investing.com. Given the increasing importance of precious metals as indicators of investor sentiment and the state of the global economy, and in light of the escalating geopolitical tensions from 2020 to 2025, we conducted a comparative analysis using modern machine learning models. The results obtained demonstrate the advantages of the KAN model as a promising tool for improving the accuracy of forecasts and the interpretability of results in various scenarios. This is highly significant for the development of an effective investment strategy in the precious metal markets.

The study aims to examine the dynamic relationships (links) between monthly defense spending (in current prices) and inflation in Russia over the period (2011–2023), based on wavelet coherent analysis, which allows us to identify correlations and the links direction between the analyzed variables that change over time. The research states that over short time scales (1–20) months, throughout the entire observation period, there are frequent (over 10 times) changes in modes, with different cause-and-effect relationships between defense spending and inflation. However, the most frequent mode is the one in which changes in variables are consistent in phase, but defense spending outpaces inflation and the growth of defense spending contributes to the inflation expansion (a positive correlation between defense spending and inflation). Over longer time scales (60–156) months, only two modes have been formed, with the first mode showing variables that are phase-aligned, but inflation outpacing defense spending and inflation growth contributing to defense expenditures growth (the direction of the positive causal links is from inflation to defense spending), is then transitioning to a mode in which the variables change in anti-phase and in which defense expenditures outpaces inflation, and the growth of defense spending suppresses the growth of inflation (a negative correlation between defense spending and inflation). This shows no inflationary consequences for Russia’s defense spending under this regime. As a result , the authors conclude that complex of intricate, multi-scale economic links between Russia’s defense expenditures and inflation exist across different investment timeframes. The study’s practical importance lies in its identification of the temporal and qualitative aspects of the nonlinear interplay between defense spending and inflation in Russia, alongside the conclusion that escalating defense spending doesn’t negatively affect the macroeconomic inflation indicator over extended periods.

The relevance of this study stems from the need to enhance the accuracy of forecasting tools in determining future budget revenues for the Russian Federation, given the dynamic macroeconomic environment shaped by sanctions. In the current situation, it is essential to respond quickly to the changes taking place. This requires the use of various frequency data in predictive models and the search for new, more accurate forecasting methods. The object of the study is the dynamics of federal budget revenues. The subject of the research is to examine the applicability of reservoir computing in forecasting federal budget revenues in the Russian Federation. The purpose of the study is to identify the feasibility of using reservoir computing models in forecasting federal budget revenues in the Russian Federation. Empirical and theoretical methods were employed in the research process. These methods allowed us to understand the essence of reservoir computing, interpret the predictive results obtained, and select the best hyperparameters. As a result, a model based on reservoir computing was proposed by the author, taking into account the dynamics of monthly and daily factors in the development of the Russian economy. It is concluded that the world’s first experience in using reservoir computing in forecasting federal budget revenues in the Russian Federation has improved the quality of the model. The characteristics of the resulting model are significantly better than analogues calculated using other methods. The high fragmentation of the Russian data and the short length of the time series have also been revealed, which was eliminated by shortening the time period for training models and imputing missing values in the data.

В статье проведен анализ динамической зависимости бюджетных кредитов, выданных бюджетам бюджетной системы РФ и являющихся источниками внутреннего финансирования дефицита бюджета и инфляции, с использованием теста Грэйнджер причинности, на основе непрерывных вейвлет преобразований. Результаты работы свидетельствует о наличии значительной двунаправленной причинно-следственной связи бюджетных кредитов и инфляции в России на всех масштабах, как в краткосрочной и среднесрочной, так и в долгосрочной перспективе, в течение периода наблюдения (01.2011÷12.2025). Важный результат работы, свидетельствует о том, что в взаимоотношениях между бюджетными кредитами и инфляцией был выявлен режим с противо-фазной причинно-следственной связью между бюджетными кредитами и инфляцией. Основные выводы и рекомендации исследования заключаются в том, что для проведения эффективной монетарной политики таргетирования инфляции, и, соответственно, контроля инфляции, критически важным является понимание механизма взаимного влияния бюджетного кредитования и инфляции. Так как бюджетный дефицит и источники его финансирования могут быть источником инфляции, то для контроля над инфляцией, монетарным властям необходимо уделять первостепенное внимание источникам финансирования дефицита бюджета.

The subject of the study is the choice of a model for financial forecasting of budget revenues, which allows the most correct assessment and obtaining a forecast value for the next period. The purpose of the study is to identify promising models of financial forecasting of budget revenues of the Russian Federation. DSGE models used since the 60s of the twentieth century have failed to identify a number of crises and timely predict the level of changes in government revenues in the United States, the Eurozone, and Russia, which did not allow for prompt adjustment of the policy pursued in the field of public revenue management. The novelty of the study consists in identifying the shortcomings of the modern methodology of financial forecasting associated with the obsolescence of the approaches used and the need to search for new models that allow you to quickly refine the prognostic results. The study used such methods as measuring predictive values and the size of their errors, analyzing and comparing the results obtained using methods and models of machine and deep learning. As a result of the study of predictive methods and models of machine and deep learning used in real business, the stock market and public finance, the most promising of them were selected. The main selection criteria were the possibility of modeling nonlinear relationships of parameters, the efficiency of calculation, the minimality of error, and the absence of a problem with retraining. In the course of the study, the expediency of time series decomposition was revealed, which made it possible to minimize predictive errors and choose the most accurate model for forecasting budget revenues of the Russian Federation. The results of the study can be used to form a systemof predictive indicators used to develop a dashboard system for civil servants in order to improve the accuracy and efficiency of their decisions.

The article identifies relevant methods of short-term forecasting of regional and local budget revenues to improve the accuracy of forecasts. The methodological basis of the study is the analytical review of scientific publications on the methods of forecasting budget revenues, identification of their distinctive features, limitations of their application, as well as justification of the choice of the method that provides high accuracy of forecasting tax revenues of subnational budgets. The authors argue that the accuracy of revenue forecasts is the most important component of economic government planning as it enables the development of effective fiscal policy, resource allocation and strategic financial management. Given that achieving an accurate forecast is a complex task, the paper shows that one of the main sources of error in revenue forecasting is the choice of revenue forecasting method, in addition to a variety of political and institutional factors affecting the accuracy of forecasts. Drawing on comparative analysis of existing forecasting methods, the authors select two methods: time series forecasting method (traditional SARMA/SARIMA method) and forecasting method based on discrete wavelet transform. They are tested on the data of aggregated monthly tax revenues of consolidated budgets of the subjects of the Russian Federation for the period from January 2011 to March 2023. It has been revealed that the forecasting method based on wavelet transformations is superior to the traditional SARMA/SARIMA method by all indicators and allows to achieve a higher level of accuracy of forecasts of monthly aggregated tax revenues of consolidated budgets of Russian regions. The findings demonstrate a high forecasting potential of shortterm forecasting methods with a preliminary decomposition of time series based on wavelet transformations. The obtained results make it possible to improve the accuracy of classical forecasting methods and thus contribute to the growth of efficiency and effectiveness of budget planning and forecasting.

The main source of formation of national budgets and financial resources that ensure the macroeconomic stability of economic unions are their own revenue sources: tax, non-tax payments, other potential reserves. The purpose of the study is to assess the fiscal convergence of the countries of the Eurasian Economic Union (EAEU) based on the indicators of internal revenue sources of national budgets. The hypothesis of the study suggests that the degree of fiscal convergence among the countries of the Eurasian Economic Union is directly affected by the states' national revenues as the main sources of the financial stability of national economies and the united economy. The article analyzes the indicators that determine the formation of internal revenue sources of the state budget (budget revenues, budget deficit, tax revenues). The multidimensional geometric approach is used - the method of the multidimensional minimum volume ellipsoid, which assesses dynamic changes in the volume of space between the countries of the EAEU. This method allows us not only to visualize the ongoing convergence processes in the territory of the analyzed countries, but also to assess the overall impact of the studied indicators on fiscal convergence. The results of the study demonstrate unstable convergence/divergence trends within the integration entity over the past 11 years, which indicates insufficient use of joint fiscal levers to stabilize the overall macroeconomic situation. The proposed indicators of own revenue sources allow us to clearly establish the degree of convergence of fiscal systems in multidimensional space. It has been revealed that the countries studied did not achieve significant fiscal convergence during the analyzed period despite their participation in economic unions, specifically the EAEU. During shock situations, countries pursue fiscal policies that cater for their domestic interests, without coordinating its consequences with one another. The theoretical significance of the obtained results lies in the expansion of the assessment pool of fiscal convergence indicators; the practical significance lies in the possible harmonization of national fiscal policies to ensure macroeconomic stabilization in the economic union during periods of crisis.

The purpose of the study is to establish a dynamic relationship between financial development, measured by the share of liquid liabilities to GDP, and the economic growth of the Russian Federation, measured as GDP per capita, for the period 2000-2022. Based on correlation analysis using the method of wavelet analysis, which allows us to identify empirical patterns in the relationship between variables on different time scales that cannot be detected traditional methods of time series analysis. The results of the study indicate that financial development in general led to economic growth in Russia in 2000-2022 and demonstrated the positive impact of financial development on economic growth, which is observed both in the short, medium and long term, alternating with the negative impact of financial development on economic activity in the short and medium term. This negative effect of financial development on the real sector of the country's economy is typical not only for Russia, but is especially relevant for low- and lower-middle-income countries. It was revealed that short- and medium-term negative effects later turn into positive effects in the long term, which is consistent, in particular, with the endogenous theory of growth. It is concluded that in the practical implementation of the liberalization policy aimed at financial development through financial deepening, it is necessary and important to distinguish between short-term, medium-term and long-term effects of financial development on economic development, given that the positive benefits obtained through financial deepening and liberalization are expected only if the economy is at a certain level the level of economic development, due, for example, to the formation of mature institutions.