HSE Economic Journal

Modeling of Aggregated Indicators of Russia's Foreign Economic Activity Using Block BVARX Models

Ilya Slabolitskiy — 2026-06-23

This paper proposes a new approach for modeling and forecasting key indicators of Russia's foreign economic activity based on a block-based architecture of a Bayesian Vector Autoregression model with exogenous variables (BVARX). In conditions of high external uncertainty and the absence of publicly available data on the physical volumes of foreign trade since 2022, the model addresses two interconnected tasks: scenario forecasting and the reconstruction of statistical time series.

The methodology is based on dividing the system of variables into four substantive blocks (foreign exchange market, oil and gas exports, terms of trade, imports), which allows for the formulation of economically interpretable prior constraints, avoids overfitting, and ensures compu-tational efficiency. The model is estimated using quarterly data from 2000 onwards. The results show that the forecasting quality of the BVARX model on horizons from one to eight quarters significantly exceeds the accuracy of basic autoregressive models (AR/ARX) for most variables. As an applied result, reconstructed values for indicators of oil and gas exports and imports for the period 2022–2025 are presented.

Goodness-of-Fit Assessment of Various Probability Distributions for Interest Rate and Credit Spread Increments in the Russian Market

Evgenii Koltyshev — 2026-06-23

The study is aimed at identifying the theoretical distribution function that best approximates empirical data on interest rate and credit spread increments and provides maximum accuracy in market risk assessment using GARCH-VaR models. The study analyzes the goodness-of-fit of 19 different probability distributions to the empirical distribution of interest rate, bond yield and credit spread increments in the Russian market by using the parametric bootstrap-based Anderson–Darling test and compares VaR models for forecasting quantiles of risk factor increments. To account for the time-varying nature of volatility, the GARCH–X model is applied, which reflects the nonlinear dependence of the volatility of interest rate increments on the level of interest rates. According to the results of the unconditional and conditional coverage tests, the generalized hyperbolic distribution provides the highest accuracy of out-of-sample forecasts among the parametric models. The semi-parametric FHS model, which is simpler in terms of specification and estimation procedures, demonstrates comparable forecast quality. Based on the results obtained, recommendations are formulated for estimating VaR for bonds and fixed income derivatives.

Multimodal Prediction of Crowdfunding Project Success: The Signaling Role of Text and Visual Content

Valeria Fedorova — 2026-06-23

Crowdfunding is a relatively new method of project financing that involves raising funds from a large number of non-professional investors. Despite the rapid growth of this sector, only about 41.98% of crowdfunding campaigns are successful, which highlights the need to identify factors that increase the likelihood of successful fundraising. This study aims to address the question of which textual and visual characteristics of projects most significantly determine their success and to what extent their inclusion improves forecasting accuracy. The study develops a multimodal model for predicting the likelihood of fundraising on the Kickstarter platform. The study hypothesizes that the inclusion of textual (H1) and visual (H2) characteristics significantly improves the predictive power of the models, and their joint integration (H3) ensures the best forecast quality by incorporating additional information. The empirical analysis relies on a dataset of 89,557 projects launched on Kickstarter between 2010 and 2024.

In addition to standard project characteristics commonly used in studies of crowdfunding success (funding goal, campaign duration, category, country, and launch year), the analysis incorporates an extended set of textual and visual factors. Textual features are extracted using natural language processing techniques, including sentiment analysis, readability, and logicality measures applied to project descriptions and risk disclosures. Visual features are obtained using a pretrained ResNet50 neural network, followed by dimensionality reduction via principal component analysis. For predictive purposes, logistic regression, decision trees, ensemble methods (Random Forest, XGBoost, LightGBM), and a multilayer perceptron are employed.

The results demonstrate that the inclusion of textual and visual factors significantly improves predictive performance. The best-performing model (LightGBM) achieves a ROC AUC of approximately 0.82. The findings indicate that funding goals, campaign duration, textual characteristics, and visual content exert the strongest influence on the probability of success. Based on these results, the study concludes that informational signals play a significant role in crowdfunding and offers practical recommendations for improving fundraising strategies on crowdfunding platforms.

Integrating New Standards of Informal Economy Statistics into Rosstat’s Practice: Challenges and Prospects

Anna Lukyanova — 2026-06-23

The paper examines methodological challenges of measuring informality following major revisions of international statistical standards. It analyses the Resolution concerning statistics on the informal economy adopted by the 21st International Conference of Labor Statisti-cians (2023) and offers practical recommendations for implementing this framework in the official statistics of the Russian Federation (Rosstat). Methodologically, the study includes a review of how international guidelines have developed, a comparison of current Rosstat’s practices with the new requirements, and empirical estimations based on microdata from the 2025 Labor Force Survey.

The analysis highlights four key changes in the measurement standards: (1) a clear procedure for classification of economic units into the formal sector, the informal sector, and the household own-use and community sector; (2) the integration of employment status categories from the International Classification of Status in Employment (ICSE-18) into the measurement of informality; (3) revised operational criteria for sector identification, notably removing enterprise size as a main criterion for defining the formal status of an economic unit and classifying an economic unit as formal if it employs at least one worker in a formal job; (4) the use of employer social insurance contributions as the main criterion for identifying formal employment, with access to paid annual and sick leave serving as supplementary criteria.

The authors demonstrate that Rosstat's current definition of the informal sector is too broad relative to modern international standards, leading to an overestimation of employment in this sector. The main issue is automatic classification of all engaged in entrepreneurial activities into the informal sector. In our opinion, registered individual entrepreneurs and their employees should be assigned to the formal sector. Preliminary estimates suggest that adopting the new standards could reduce the measured share of employment in the informal sector by approximately one-third. The paper also emphasizes the importance of shifting focus from enterprise-based measures (employment in the informal sector) to worker-centered measures (informal employment), which better reflect job quality and economic risks held by workers.

Analysis of the Transformation of Chinese Economic Growth in the «Xi Era»

Inna Lola — 2026-06-23

The article analyzes the transformation of China's economic growth mechanisms in the period after Xi Jinping came to power, with a focus on interprovincial heterogeneity. Based on panel data for 29 provinces of China for 2001-2024, the Bayesian hierarchical regression model is estimated, which makes it possible to identify both average (fixed) and provincial-specific (random and total) effects of key intra-system macroeconomic factors, including export growth rates, R&D expenditures, retail sales and budget deficits. It is emphasized that the observed slowdown in the Chinese economy is not temporary, but structural, determined by the existing imbalances in the economy, including at the regional level.

The empirical strategy assumes an explicit division of the sample into the periods before and after 2012 (the year Xi Jinping came to power), which makes it possible to identify changes in the role of these factors in the context of the discussed transition from an export-oriented growth model to a reliance on domestic consumption and innovation. However, the results show that in 2013–2024 exports remain the main driving force, while role of R&D activity has strengthened extremely, domestic consumption loses its leading position, and government spending ceases to influence the dynamics of economic development in any way, which casts doubt on the success of the “dual circulation” policy. It is also worth noting that after 2012, the interprovincial variability in the contributions of growth factors is decreasing, and 3 types of growth mechanism identified as a result of cluster analysis of the combined effects which existed in 2001–2012 are subsequently reduced to 2.

The findings complement the literature on the “new normal” of the Chinese economy and contribute to research on regional inequality, demonstrating that the transformation of the growthmodel is accompanied not only by changes in average effects, but also by a contraction of the space of regional economic divergence.

The Evolution of Islamic Finance Research: A Bibliometric Analysis of Instruments, Infrastructure, and Global Collaboration

Maneesh Kumar Pandey — 2026-06-23

Islamic finance has expanded rapidly beyond its origins in Muslim-majority countries and has become an important component of the global financial system. This study aims to examine the evolution and intellectual structure of research on Islamic financial instruments through a comprehensive bibliometric analysis. The study employs a bibliometric approach using secondary data collected from the Scopus database. A total of publications from 1997 to 2026 were analyzed to identify research trends and scholarly contributions in Islamic finance. Bibliometric techniques such as citation analysis, co-citation analysis, bibliographic coupling, and co-authorship analysis were applied. Descriptive analysis and visualization tools were used to map the research landscape, including leading journals, influential authors, institutions, and collaboration networks. The findings reveal a significant increase in publications on Islamic financial instruments over the past decades, indicating growing global academic interest. The results highlight key contributing journals, influential scholars, and major institutions driving the research domain. The study also identifies emerging research themes and collaboration patterns that shape the development of Islamic finance scholarship. The study is limited to publications indexed in the Scopus database, excluding other databases and grey literature, which may influence the comprehensiveness of the findings. Future studies can integrate multiple databases to provide a broader understanding of the field. The findings provide insights for researchers, policymakers, and financial institutions by identifying key research trends, influential contributors, and collaboration opportunities in Islamic finance. This study contributes to the literature by offering a systematic bibliometric overview of Islamic financial instruments research and mapping its intellectual structure and global development.