DOI: 10.32609/j.ruje.11.145962

Authors: Luka Bašić

Abstract: This paper applies a macroeconometric approach to analyze structural shocks and their impact on the key transmission channels of monetary policy in Russia, focusing on the period since the imposition of sanctions and the monetary regime shift beginning in 2014. The approach combines the Lee–Strazicich LM test, used to identify structural breaks, with a Bayesian VAR model that estimates the posterior distribution of shock effects and responses. The model is further extended by dummy variables indicating the periods of imposed sanctions in 2014, the change in the Bank of Russia’s monetary policy in 2015, and the Russia–Ukraine conflict in 2022. The findings indicate that the interest rate transmission channel operates in a fully asymmetric manner: the short run is characterized by the monetary policy shift, and the long run — by the conflict in Ukraine, suggesting the implementation of an expansionary and later highly restrictive policy with a temporary dual approach. The credit and exchange rate transmission channels are identified as the two most important stabilization mechanisms for macroeconomic shocks, with the 2015 regime change significantly enhancing the absorption of exogenous shocks. In contrast, the extended price transmission channel exhibits a more moderate capacity for shock absorption, underscoring the Bank of Russia’s success in anchoring inflation expectations. Overall, the findings confirm that proactive monetary policy shapes inflation expectations effectively by employing two main tools: managing the key interest rate to control inflationary pressures and managing the ruble exchange rate to stabilize the economy.

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DOI: 10.32609/j.ruje.11.126000

Authors: Nwabisa Florence Ndzama

Abstract: We find that, while different models used to estimate the output gap in five major emerging economies show similar trends over time, they lead to different conclusions about how well the output gap can predict inflation. This suggests that the choice of model can significantly impact the conclusions drawn about the relationship between the output gap and inflation. The multivariate Hodrick–Prescott filter and the structural vector autoregressive model produce the smallest forecast errors in most cases among the four output gap models considered. We further find some indications of a better inflation forecasting ability of the output gap in countries with inflation targeting, suggesting that the improved transparency related to inflation targeting might support the inflation forecasting process.

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DOI: 10.32609/j.ruje.10.113578

Authors: Inna S. Lola, Dmitry G. Asoskov

Abstract: This study investigates the utility of business uncertainty indicators as predictive tools for forecasting economic activity in the context of Russia. In an era characterized by global economic volatility and geopolitical shifts, understanding the dynamics of economic uncertainty and its impact on overall economic performance is of paramount importance. The study utilizes a comprehensive dataset based on the results of business tendency surveys in Russia, spanning the period from 2009 to the first half of 2024. Given the importance of uncertainty in shaping economic outcomes, the central research question of this study is: can uncertainty indicators predict business activity in Russia or not? To address this question, we compared two alternative approaches to calculating business uncertainty: the ex‑ante approach, which uses the business community’s assessments of future business trends to measure uncertainty as the dispersion of opinions expressed, and the ex‑post approach, which applies entrepreneurial assessments of both future and current trends to determine business uncertainty as the degree of deviation of entrepreneurial expectations from the real picture. National indicators and sectoral indicators were calculated for the mining and quarrying industry, manufacturing industry, construction, retail trade, wholesale trade and services. For most of the industries under consideration (except for the construction and service sector) and at the national level, the specifications of vector autoregression models that were effective for forecasting real indicators of economic activity, characterized by lower forecast errors compared to standard autoregressive models, were built. According to the results obtained, at the national level, when forecasting GDP, clear preference should be given to the ex‑post indicator.

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DOI: 10.32609/j.ruje.9.97733

Authors: Osama D. Sweidan

Abstract: Exploring the nexus between geopolitical risk (GPR) and military expenditures (ME) has been limited during the past period. It is justified by the absence of a well-published proxy for GPR. Recently, the work of Caldara and Iacoviello (2022) stimulated scholars to examine the consequences of GPR. Our paper seeks to understand the relationship between GPR and ME in the United States (US). It designs a theoretical framework and computes an econometric model using the Autoregressive Distributed Lag methodology based on annual data (1960–2021). In addition, it uses the pairwise Toda–Yamamoto causality test. The results show that the relationship between GPR and ME is one of unidirectional causality and runs from ME to GPR in the US. Further, this relationship is statistically significant and positive in the short and long run. This finding supports our hypothesis that the US GPR is a consequence of resource allocation, i.e., ME, and can be controlled, directed, and mitigated. Thus, ME is a tool to achieve the US international hegemony’s strategic goals. From a policy implication perspective, it has been proved that GPR has broad negative consequences for various economies. Thus, moving toward cooperation and coordination with other nations instead of accumulating ME tends to support the international economy.

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DOI: 10.32609/j.ruje.5.49345

Authors: Arne Melchior

Abstract: The article examines Russia’s participation in world trade and trade policy, using trade data for 1996–2017 and simulations of a numerical world trade model where Russia is divided into domestic regions. Since the mid-1990s, Russia’s foreign trade has grown much faster than the world average. This was accompanied by rapid deterioration in the trade balance for manufacturing, and fast redirection of imports, with more from China and relatively less from others, especially Eastern Europe. Only 1/8 of Russia’s foreign trade in 2017 was with Eastern Europe. This is why Russia can gain more from trade integration with the world beyond Eastern Europe, according to the model simulation analysis. For Russian domestic regions, multilateral liberalization among all countries has a similar effect across all of them, with a welfare gain due to lower import prices. For the commodity-exporting regions of Russia, preferential free trade agreements (FTAs) have a similar impact. For the more industrialized Russian regions, on the other hand, FTAs lead to manufacturing growth, rising wages and higher prices, and a larger welfare gain. According to the model simulations, trade integration promotes industrial diversification, with manufacturing growth also in some commodity regions. The results indicate that external liberalization is particularly important for the central parts of Russia; with Volga and West Siberia generally obtaining the strongest manufacturing boost from trade integration.

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