Construction of spatial autoregressive models to assess the impact of economic factors on the production income of the Embi oilfields in the Atyrau region of the Republic of Kazakhstan

This study examines the assessment of the impact of oil production, delivery, and export volumes, the number of wells, domestic and export oil prices, and capital investments on the production income of the Embi oilfields in the Atyrau region of the Republic of Kazakhstan. The research analyzes 140 observations to determine the statistical significance of these economic factors. According to the Akaike (AIC )and Bayesian Information Criteria (BIC), the spatial autoregression model with autocorrelated errors and fixed effects (model 19) is identified as the most suitable for analysis. The model's key results indicate that a 1% increase in oil production volume leads to a 0.989% growth in the production income of the Embi oilfields (p < 0.01). Similarly, a 1% increase in oil export volume contributes to a 0.189% income growth (p < 0.01). The export oil price shows a moderate positive effect with a coefficient of 0.481, also significant at the p < 0.01 level. The spatial lag of the dependent variable is 0.407 and significant at p < 0.01, confirming strong spatial dependence: changes in the production income of one oilfield are closely linked to similar changes in neighboring oilfields. The spatial error lag is 0.425, also significant at p < 0.01, highlighting the importance of accounting for spatial effects in the analysis. Thus, model (19) confirms the significant positive impact of oil production, export volumes, and export oil prices on the production income of the Embi oilfields. Spatial effects play a crucial role, demonstrating a close interconnection between the oilfields. This makes the model more precise and reliable for analyzing spatially linked entities such as oilfields. Keywords: Durbin model; fixed effects; random effects; production income; panel data; Embi oilfields; autoregressive model.