Investment Dynamics of the Samarkand Region: Analysis and Forecast Using a Polynomial Model

Аннотация

The economic development of any region relies heavily on investment since it affects growth along with employment and infrastructure development. The research examines the investment trends in Samarkand region and generates predictions through polynomial modeling. We build a polynomial regression model based on historical investment data to detect investment trends and estimate future investment directions. The investigation determines model precision through comparison against different forecasting methods and examination of primary economic elements that influence investment. The results present essential information for policymakers together with investors and economic planners to make well-informed regional investment strategy decisions. Keywords: Investment dynamics, polynomial model, forecasting, economic development, Samarkand region. forecasting approaches, like linear regression models, Introduction: Investment plays a crucial role in driving often struggle to capture the nonlinear patterns that economic growth, influencing industrial development, characterize investment dynamics. On the other hand, infrastructure projects, and job creation. In regional polynomial models provide a more adaptable solution economies, the strategic allocation of investments is by accommodating complex relationships and vital for promoting sustainable development and pinpointing turning points in investment trends. Given boosting competitiveness. The Samarkand region, Uzbekistan's shifting economic landscape, especially recognized as one of Uzbekistan’s key economic with market liberalization and investment policy centers, has seen significant investment in recent reforms, a strong forecasting framework is crucial for years. However, predicting future investment flows aligning investment strategies with regional remains a complex challenge due to the ever-changing development objectives. nature of economic factors. Being able to forecast investment trends is essential for policymakers, Several prior studies have examined the impact of investors, and economic planners, as it helps them investment on economic growth and the effectiveness create informed strategies that optimize resource of various forecasting models. Research focused on allocation and support long-term economic stability. investment trends in transition economies has underscored the significance of policy stability, The importance of this study arises from the growing infrastructure availability, and the development of demand for reliable forecasting methods to aid financial markets in attracting capital (Dunning, 2009; investment decisions at the regional level. Traditional Aghion et al., 2013). Additionally, studies that have American Journal Of Social Sciences And Humanity Research 11 https://theusajournals.com/index.php/ajsshr American Journal Of Social Sciences And Humanity Research (ISSN: 2771-2141) employed polynomial regression models in economic investments made in all regions of the Samarkand forecasting have shown their capability to capture region in 2012-2023. cyclical trends and structural changes in investment. To analyze investment trends and predict future values, METHOD a polynomial regression model of degree nnn is specified as follows: This study employs a quantitative research approach, utilizing econometric modeling to analyze historical investment trends in the Samarkand region and y=a +a x+a x2+…+a xn+e 0 1 2 n forecast future investment flows. A polynomial where: regression model is chosen due to its capability to capture nonlinear trends and structural changes in • y – dependent variable investment behavior over time. The research follows a • x – independent variable systematic process, including data collection, model specification, estimation, validation, and interpretation • 𝑎 0 , 𝑎 1 ,…,𝑎 𝑛 are the estimated coefficients, of results. • n – degree of the polynom Data Collection and Sources • e - is the error term. The study relies on secondary data obtained from The estimation of the polynomial regression model is official sources, including: conducted using the Ordinary Least Squares (OLS) method. Model validation is performed through: • State Committee on Statistics of Uzbekistan – providing historical investment data at the regional Adjusted R2 to evaluate model accuracy. level. In the graph 1. above, functions were constructed using • Ministry of Investment, Industry, and Trade of a polynomial trend for the volume of investments in Uzbekistan – offering insights into investment policies fixed capital in the regions of Samarkand region in and trends. 2012-2023. However, since the polynomial model was not suitable for expressing the given indicators for The dataset covers annual investment figures for the investments in Payarik, Akdarya, Ishtikhon districts and Samarkand region over the past 12 years to ensure the city of Kattakurgan, an exponential function was sufficient data for trend analysis and forecasting. To used to