FORECASTING GDP GROWTH AND GDP PER CAPITA IN UZBEKISTAN BY THE ORDINARY LEAST SQUARES (OLS) REGRESSION ANALYSIS - Azibaev, Akhmadkhon (Scientific and Technical Journal of Namangan Institute of Engineering and Technology, 2024)
Abstract. Objective. This study employs the Ordinary Least Squares (OLS) regression method to forecast Uzbekistan's GDP growth and GDP per capita from 2023 to 2030. Using historical data from 1991 to 2022, the research aims to provide a predictive model that can inform economic policies and investment decisions. The findings demonstrate a positive trajectory for GDP growth and per capita income, reflecting the potential for sustained economic development. Methods. This study uses the Ordinary Least Squares (OLS) regression method to forecast Uzbekistan’s GDP per capita from 2023 to 2030. Historical GDP per capita data from 1991 to 2022 serves as the basis for this analysis. The OLS method estimates the relationship between GDP per capita and time, aiming to minimize the sum of the squared differences between observed and predicted values. Python and the Statsmodels library were utilized to build and fit the regression model. Results. The OLS regression model provides the following predictions for Uzbekistan's GDP per capita from 2023 to 2030. The model achieved an R-squared of 0.633 and an adjusted R-squared of 0.652, indicating a substantial proportion of variance explained by the model and suggesting robustness and relevance of the predictors. Additionally, a p-value less than 0.05 confirms the statistical significance of the model. Conclusion. The OLS regression analysis forecasts a steady increase in Uzbekistan's GDP per capita from 2023 to 2030, suggesting positive economic prospects. The model's robustness and statistical significance make these predictions credible, providing valuable insights for policymakers and investors. However, the forecast assumes the continuation of historical trends and does not account for potential economic disruptions or significant policy changes.