This study examines the theoretical foundations and assumptions of the Ordinary Least Squares (OLS) method, which is widely used in econometric analyses, with a particular focus on the issue of heteroskedasticity that arises when the homoskedasticity assumption—requiring error terms to have constant variance—is violated. While the OLS estimator remains unbiased and consistent under heteroskedasticity, it loses efficiency, standard errors become unreliable, and the validity of hypothesis tests is weakened. Therefore, applying alternative methods in the presence of heteroskedasticity is a critical requirement for ensuring the reliability of econometric models. In the empirical application, OLS estimates reveal that export and employment variables have significant and positive effects on Gross Domestic Product (GDP), whereas agricultural land and migration variables do not exhibit statistically significant contributions. The Breusch–Pagan test confirms the presence of heteroskedasticity, prompting the use of the Weighted Least Squares (WLS) approach. WLS preserves the strong effects of exports and employment, but heteroskedasticity is only partially mitigated. This finding highlights the necessity of adopting a more flexible and robust method, such as the Generalized Least Squares (GLS) approach. The GLS model accounts for the general variance–covariance structure of error terms, addressing both heteroskedasticity and potential autocorrelation issues. Empirical results indicate a substantial increase in the model’s explanatory power (R² and adjusted R²) and the complete elimination of heteroskedasticity under GLS estimation. In conclusion, in econometric applications with heteroskedasticity, relying solely on OLS is insufficient. Employing alternative methods like WLS and GLS ensures more reliable and efficient parameter estimates, thereby strengthening econometric inferences. The determinant role of exports and employment on regional economic growth is consistently observed across all methods and is most reliably supported by GLS estimates.