Rethinking Scale in Applied Econometrics: Practical Impacts of Log Transformations on Model Performance

Aim: This study examines the practical impact of measurement scale choices, particularly logarithmic transformations, on the accuracy, reliability, and interpretability of econometric models using macroeconomic indicators from the MENA region. Methodology: Using real-world MENA data, multiple techniques (OLS, Fixed Effects, Random Effects, GMM) were applied with both raw and log-transformed variables to compare outcomes for variance stability, normality, stationarity, and heteroskedasticity. Results: Log transformations substantially improve model diagnostics by stabilising variance, enhancing normality, and reducing heteroskedasticity, yielding more precise estimates. However, they alter coefficient interpretation, highlighting a trade-off between statistical robustness and economic meaning. Implications and Recommendations: The findings underscore the critical need for appropriate data transformations to ensure valid and interpretable results, offering practical guidance for researchers and policymakers in model specification for policy analysis and forecasting. Originality/Value: This study provides a novel comparative exploration of transformation effects across methodologies and one of the few region-specific investigations using MENA data, delivering actionable insights for methodological and applied research.