The rapid development of digital technologies has changed the way data is collected, stored, and analyzed. Big Data has created new opportunities and challenges for econometric research. This article discusses the integration of econometric methods with big data analysis, the methodological innovations needed, and the results of empirical economic research. The study also highlights modern tools and ideas that help econometricians manage the complexity and scale of large data sets while maintaining model accuracy and interpretability. It also highlights how the combination of traditional econometric thinking and computational methods can improve the quality and scope of economic analysis in the modern digital economy

This study investigates the relationship between ESG disclosure quality and international competitiveness among commercial banks in Central Asia, benchmarked against a matched sample of European Union (EU) institutions. Using a mixed-methods approach –including content analysis of sustainability reports (2018–2024), a novel ESG Disclosure Quality Index (EDQI), and panel regressions on 495 bank-year observations – we find that Central Asian banks exhibit significantly lower ESG transparency across environmental, social, and governance dimensions compared to EU peers. Crucially, higher-quality ESG disclosure is robustly associated with greater foreign investment inflows, increased likelihood of Eurobond issuance, higher foreign ownership, and improved credit ratings – even after controlling for bank fundamentals and institutional quality. Notably, the marginal benefit of ESG transparency is significantly stronger in Central Asia than in the EU, suggesting that credible disclosure serves as a critical signaling mechanism in emerging markets where such information is scarce. These findings support the strategic adoption of global ESG reporting standards (e.g., ISSB, TCFD) by Central Asian regulators and banks to enhance financial integration and investor confidence.

This article analyzes the application of dynamic econometric models in economics, particularly the contemporary trends observed in recent years. These models (for example, DSGE, VAR, and dynamic panel models) serve as primary tools for evaluating economic dynamics, shock impacts, and policy assessment, with their precision being enhanced through the integration of machine learning (ML) and big data. The methodology employs a systematic literature review, involving the analysis of several research studies from prestigious journals. The results demonstrate that DSGE models provide macroeconomic forecasts with RMSE values ranging from 0.15 to 0.25, VAR models assess shock impacts, and ML integration improves accuracy by 20-25%, although limitations exist due to computational complexity and data uncertainty. In conclusion, the role of these models in shaping economic policy is emphasized, with recommendations provided for deepening ML integration, expanding the utilization of big data, and addressing the aforementioned constraints.

This article critically reviews the main methods used to analyze the determinants of foreign direct investment (FDI), focusing on panel data regression, gravity models, generalized method of moments (GMM), and qualitative-comparative analysis (QCA). For each method, we synthesize findings from major empirical studies to understand how specific economic, institutional, and policy variables influence FDI inflows. The study reveals that methodological choice significantly affects the magnitude and even direction of FDI determinants, underscoring the importance of mixed approaches in policy research.

This article analyzes the impact of transport infrastructure on destination development and demonstrates how it influences the traditional demand for tourism at the international trade level. An efficient transportation system and adequate infrastructure are essential for the growth of the tourism sector. Having well-developed transport infrastructure leads to lower transportation and trade costs and reduces travel distance, which contributes to the development of international tourism. Based on panel data from 22 countries that traded with Uzbekistan during the period 2003–2023, the study examines bilateral tourism flows over that time. We applied the gravity model to international tourism flows, controlling for the role of infrastructure in the dataset and disaggregating the data by country and time. The results show that inbound tourism to Uzbekistan is strongly influenced by both the direct and indirect development of its transport infrastructure. According to our findings, Uzbekistan can strategically develop its tourism sector by improving its transportation infrastructure – specifically railways, roads, and air transport.

Ensuring sustainable and inclusive development of regional economies stands as a central challenge for contemporary economic policy and regional planning. This paper develops the theoretical foundations for econometric modeling of regional development, aiming to provide a rigorous methodological framework for quantifying growth drivers and producing reliable forecasts of regional economic performance. The primary objective is to specify and justify econometric models that capture both the long-run relationships and short-run dynamics among core regional indicators (GRDP, industrial output, retail turnover, investment inflows, employment and income levels, and institutional variables). The theoretical discussion integrates perspectives from neoclassical growth theory, endogenous growth models, institutional economics and evolutionary economics to frame the empirical strategy

This paper aims to study the relationship between ICT development, export diversification, and income inequality. The study is based on the analysis of data for 83 countries from 2002 to 2019 using the panel quantile regression method. The results of the analysis indicate that ICT has a negative impact on the level of income inequality, and this effect is more pronounced in countries with a high Gini coefficient. Export diversification also contributes to a more equal income distribution, and this effect is more pronounced in countries with low and medium levels of inequality. Moreover, the interaction of the ICT index with the export concentration index has a negative effect on income inequality, and the effect is stronger at the upper quantiles of the distribution. Accordingly, countries with high income inequality should pay special attention to the digitalization of the economy and strengthening export diversification.

This paper investigates the determinants of Return on Equity (ROE) in joint-stock companies in Uzbekistan, using the DuPont Model to analyze the impact of profitability, efficiency, and leverage. Panel data from 25 Uzbek non-financial firms over a 10-year period (2014–2023) were examined using fixed and random effects models. While profitability and efficiency show a significant positive effect aligned with the DuPont assumptions, the leverage demonstrates a negative effect. In the lagged model, all three factors have a considerable positive impact. Being the first paper to analyze non-financial companies in Uzbekistan, it provides useful insights into companies’ performance drivers for both researchers and managers.