This study used econometric panel models to examine the influence of water shortage on economic growth in Central Asian countries. To evaluate long-term panel data, we used a pooled regression model, a fixed effect model, and a random effect model on annual data from 1990 to 2023 across five nations. After creating the models, we ran diagnostic tests using several statistical techniques. After analyzing the models listed above, we determined that the Pooled regression model is best suited to our panel data. Based on the indicators of this model, it was discovered that increased water shortage in Central Asian countries had a negative impact on economic growth. Each unit increase in water scarcity causes a 0.0166 unit loss in GDP. The findings suggest that countries experiencing severe water scarcity should prioritize water resource management and establish a water conservation program that includes the construction of infrastructure to alleviate the shortage.

This article provides an analysis of the Almon lag model based on statistical data from the Fergana Region. The model facilitates the study of relationships between variables over time by accounting for distributed lag effects. Using the available data, the research examines the dynamics of key factors in the Fergana Region and identifies the optimal lag structure. The results enhance the understanding of the region's economic dynamics and offer valuable insights for policymakers and researchers alike.

This article discusses the development of a linear regression model for assessing business value in the oil and gas sector of Uzbekistan. The model integrates key economic and operational variables, such as global oil and gas prices, political stability, macroeconomic indicators, sales volumes, and others, including EBITDA and debt level. The study focuses on the statistical significance of variables and their impact on market value, thus providing a basis for strategic management and planning in the industry.

Purpose: This study aims to investigate the intricate relationship between the volume of exports (Y) and freight turnover on railways (X) through a paired linear regression model. The purpose is to discern the quantitative impact of railway freight turnover on a nation's export volumes and offer insights for policymakers and stakeholders in the transportation and trade sectors. Design/Methodology/Approach: A quantitative research design is employed, utilizing historical data spanning from 2000 to 2022. The chosen methodology involves the estimation of a paired linear regression model using the method of least squares. Statistical significance is tested through the coefficient of determination, Fisher's F-test, and an examination of heteroskedasticity. Elasticity analysis, rank correlation, and graphical assessments of residuals provide a comprehensive understanding of the relationship. Findings: The research reveals a strong and statistically significant positive correlation (r = 0.92) between export volumes and railway freight turnover. The regression model, validated through multiple tests, explains 84.72% of the variability in export volumes. Economic interpretation indicates that a one-unit increase in railway freight turnover leads to a substantial average increase of 1627720.728 units in export volumes. The absence of heteroskedasticity reinforces the robustness of the model.

The article examines the regional differentiation of factors influencing the development of trade services, and the internal connection of development trends with regional characteristics. Also, when forming a system of trend indicators, regional indicators should have the same sign as general indicators. It is based on the fact that priority factors can be assessed on the basis that signs of trends make it possible to study socio-economic, technical and technological patterns of development. The state of regional development of trade services was analyzed in certain dynamics by comparing indicators of territorial integrity and territory structure. The level of resource efficiency of the trade service network, changes in the foreign trade system, changes in population, socio-economic, technological changes in lifestyle, the level of innovativeness of the trade environment and the distribution of capital of trade enterprises reveal development. trends. were assessed as the main elements of influence.

In this article, the practice of fire risk management as a result of carrying out rain risk assessment in residential houses of the Republic, installation of security equipment at low cost, possibilities of purchasing safe doors and making decisions in emergency situations is studied. In it, 3002 randomly selected residential areas of the republic were taken as objects. Statistical data of the research were obtained on the basis of social survey questions in Google form online. In the review of the literature, the views of foreign scientists on the subject were studied, and in the process of econometric modeling, the Ologit model was used in the Stata 18.0 program. After the analysis, linktest was used to check the quality of the regression model. Purchase of equipment at own expense and purchase of safe doors were found to be statistically significant in managing fire risks in residential areas.

This study examines the relationship between key macroeconomic indicators,
including the Human Development Index, Gender, Business, and Law indices, as well as the ease of doing business index and import level, with the inflow of foreign direct investment into Uzbekistan. Using OLS regression, correlation analysis, cointegration tests, and other econometric techniques, the analysis reveals that changes in the Human Development Index, Gender Index, Business and Legal index, and Import level positively correlate with foreign investment inflows. This suggests that human development, gender equality, business environment, and improved import policies play crucial roles in attracting foreign investments.

OLS regressions have a set of assumption in order to have its point and interval estimates to be unbiased and efficient. Data missing not at random (MNAR) can pose serious estimations issues in the linear regression. In this study we evaluate the performance of OLS confidence interval estimates with MNAR data. We also suggest bootstrapping as a remedy for such data cases and compare the traditional confidence intervals against bootstrap ones. As we need to know the true parameters, we carry out a simulations study. Research results indicate that both approaches show similar results having similar intervals size. Given that bootstrap required a lot of computations, traditional methods is still recommended to be used even in case of MNAR

The article analyzes the dynamics of the main economic indicators of the trade
sector of the Bukhara region in 2012-2023. Based on the trend analysis of socio-economic indicators of the dynamics of trading activity, a generalized complex forecast model has been developed and optimistic and pessimistic forecast scenarios for 2025-2027 have been developed.

These findings underscore the need for context-specific corporate governance models to optimize overall financial performance and mitigate risk. This research contributes to the understanding of how multiple governance models affect bank stability globally and provides information for policy makers and bank executives aiming to improve governance frameworks. These findings underscore the need for context-specific ways of corporate governance to optimize economic performance and mitigate risk. This research contributes to the expert knowledge of the ways in which different governance models affect the global stability of financial institutions and provides information for policymakers and bank executives aiming to improve governance frameworks.