This paper applies multivariate econometric modeling methods to evaluate and forecast enterprise asset indicators in the Republic of Uzbekistan. The case study focuses on “Grant Thornton Valuation” LLC, where revenues from privatization serve as the dependent variable. Explanatory variables include the number of contracts, the number of valuation reports, the number of privatized objects, as well as the number of issued certificates and licenses. The model’s reliability was tested using descriptive statistics, correlation analysis, VIF, and ADF tests, followed by forecasting. The results provide practical insights for analyzing enterprise performance and projecting future trends.

This article examines the modeling and forecasting of urban population spatial distribution based on Geographic Information Systems (GIS) technologies. The study conducted geospatial analysis of population density, urbanization processes, and demographic changes using Tashkent city as a case study. Population spatial distribution forecasting models were developed using modern GIS software - ArcGIS, QGIS, and Python libraries. As a result, a forecast of geographic distribution of urban population for 2025-2035 has been prepared and practical recommendations for urban planning have been developed.

This study intends to forecast the agricultural commodities in Uzbekistan. The data were obtained from National Statistical Committee of the Republic of Uzbekistan. The study’s 15-year duration spans from 2010 until 2024. Box-Jenkin’s methodology, known as Auto Regressive Integrated Moving Average (ARIMA) approach was used in this study on a number of specific Uzbek agricultural commodities. According to the methodology requirement for large amount of data points, 10 species of agricultural products were chosen. All the essential steps of the model were utilized methodically for dynamic forecasting 5 periods ahead from 2025onwards. Using various model selection criteria, including Adj R^ 2, the minimum AIC value, and the lowest MAPE values, the study confirmed the accuracy of the model. Barley had the lowest Akaike Information Criterion (AIC) value among these products, while cabbage had the lowest Mean Absolute Percentage Error (MAPE) value.

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.

The article considers an important issue of training personnel with a scientific degree and the possibilities in the field of using statistical analysis to forecast this process. The use of statistical analysis to study and forecast the processes of training personnel with a scientific degree allows to significantly improve the efficiency of managing this process, optimize training costs, and ensure more accurate planning of the need for scientific personnel. Using the information from this research, it is possible to develop a system for training personnel with a scientific degree and to increase their competitiveness in the labor market.

The dynamics of production of products based on panel data for 2010–2024 for 3 regions of the Republic of Uzbekistan are econometrically modeled. The study analyzes time-specific structural changes by region using Two-Way Fixed Effects (FE) and Log Trend Interaction models. According to the results of empirical analysis, it is possible to increase the rate of variation in fishery production by 78.8%. The increased econometric forecasts for 2025–2027 indicate that the sector's production will continue to grow and regional growth dynamics will develop.

This article analyzes the significance of investment flow in managing the equilibrium state within a self-organizing trading system. The research examines the impact of investment flow on economic systems, its various forms, and its influence on the dynamics of the trading system. Additionally, continuous and discrete factors affecting the system are classified, and a model is developed using a stochastic differential equation. A methodology for determining the optimal value of investment flow is proposed based on the Hamilton–Jacobi–Bellman equation. The results obtained are of great importance for forecasting economic systems, optimal resource allocation, and the development of market strategies.

In this article, mathematical models are compiled based on the dynamics of electricity supply volumes in the Republic of Uzbekistan for 2010-2023, as well as for the period from 2010 to 2017 and beyond. The forecast indicators for the period from 2024 to 2030 have been determined. The dynamics of the growth of electricity supply volumes during the study period is also analyzed.

In this article, using the example of the Surkhandarya region, the dynamics of production of fruit and vegetable products is analyzed and forecasted using economic and statistical methods. Scientific proposals and practical recommendations aimed at further development of the fruit and vegetable industry in the regions are also developed.

The study paper provides a detailed examination of how digital marketing methods affect the economic efficiency of commercial banks, specifically within the changing environment of the banking sector. The study examines the relationship between higher digital marketing spending, increased investment in staff development, and their impact on the net income of banks using 18 years of financial data from ATIB "Mortgage Bank" in Uzbekistan. The study uses advanced statistical analysis to create Vector Autoregressive (VAR) and Autoregressive Distributed Lag (ARDL) models to predict and assess the impact of marketing initiatives in the banking industry. The results highlight a strong correlation between increased digital marketing initiatives, greater staff commitment, and higher financial success of commercial banks. The VAR and ARDL econometric equations, generated from time series data, offer a strong foundation for comprehending the dynamic relationship between marketing tactics and economic results in the banking sector. This research adds to the overall discussion on the effectiveness of digital marketing, providing significant data for financial institutions looking to improve their economic efficiency by strategically investing in marketing and human resources.

This article talks about the calculation of financial results in wine industry enterprises, the regulation of financial results and increasing profit in it, the essence, theoretical foundations of the accounting of financial results in wine industry enterprises, and the possibilities of increasing profit in wine industry enterprises.