This article presents a theoretical and practical comparison between classical econometric modeling and modeling based on the fuzzy differential equations approach for forecasting agricultural yields. The study describes the mathematical structures of both models, the interrelationships between variables, and the methodology of modeling while accounting for uncertainty.

This paper comprehensively examines the theoretical and practical aspects of the efficient use of investments in the development of the national economy based on an econometric approach. The study utilizes macroeconomic data for the period 2000–2024 to conduct an in-depth analysis of the impact of investment volume, employment level, and export volume on Gross Domestic Product (GDP). The methodological framework includes correlation analysis, construction of a multiple regression model, evaluation of parameter significance using t-statistics, assessment of overall model adequacy through the F-test, and testing for autocorrelation using the Durbin–Watson statistic. All econometric computations and model estimations were carried out using the modern statistical software environment R Studio. The empirical results demonstrate that investments have a strong positive and statistically significant effect on economic growth, as measured by GDP. Based on the findings, a set of practical recommendations has been developed aimed at improving the efficiency of investment utilization, optimizing their allocation across economic sectors, and enhancing investment policy.

This article examines the theoretical and methodological foundations of applying the fuzzy regression model in assessing production efficiency in agricultural sectors. The advantages of the fuzzy approach in optimizing the utilization of available resources in the agricultural sector are analyzed. Furthermore, the scientific significance of identifying the relationships between factors affecting efficiency under conditions of uncertainty using the fuzzy regression model is theoretically substantiated.

This study analyzes the impact of small business entities’ activities on regional economic indicators using statistical and econometric methods. The Stata software was employed for empirical calculations, providing an opportunity to assess the influence of small business on regional economic growth. The results of this approach have practical significance for improving mechanisms to support small businesses in the regional economy.

It is vital to establish trustworthy forecasting methodologies in order to predict and assess a country's energy consumption ahead of time. This enables economists to better track and analyze consumers' energy needs. To that purpose, this study was done to establish the Republic of Uzbekistan's long-term energy consumption forecast using data on energy consumption volume acquired between 1985 and 2023. The forecasting procedure used the econometric ARIMA model. The Box-Jenkins approach was used to determine the optimal ARIMA order. According to the findings, the ARIMA (0,1,3) model was shown to be the most accurate. Based on this model, the entire predicted results had an average percentage inaccuracy of 7.2 percent. It was discovered that ARIMA is the most effective model for making long-term strategic decisions about energy consumption.

Linear models has been a powerful econometric tool used to show the relationship between two or more variables. Many studies also use linear approximation for nonlinear cases as it still might show valid results. OLS method requires the relationship of dependent and independent variables to be linear, although many studies employ OLS approximation even for nonlinear cases. In this study, we are introducing alternative method of intervals estimation, bootstrap, in linear regressions when the relationship is nonlinear. We compare the traditional and bootstrap confidence intervals when data has nonlinear relationship. As we need to know the true parameters, we carry out a simulation study. Our research findings indicate that when error term has non-normal shape, bootstrap interval will outperform the traditional method due to no distributional assumption and wider interval width

In this article, digitization of the payment sector has become a real need of the hour, because life in the digital world imposes various demands on financial processes, which innovative technologies, including distributed ledgers and smart contracts, are designed to meet.