The study aims to quantitatively assess the extent to which sovereign ratings could be explained by a set of economic variables. A wide variety of factors could potentially bias a credit rating agency’s decision. The analysis begins with replicating the results found in a seminal analysis by Cantor and Packer (1996). This analysis expanded by including more countries, dynamic over time and time lags. Multiple complementary statistical models and a Random Forest model are explored in this study. To ensure robustness of the model, out-sample-testing is applied. The results show that GNI per capita, GDP growth, total debt to GDP, inflation rate, default amount, default indicator, HDI, change in HDI and IMF indicator are statistically significant. It is observed that current account to GDP, GDP growth and inflation rate have a time-lagged effect on sovereign ratings. A further analysis by separating between developing and developed countries using the IMF indicator suggests that there is a discrepancy between developing countries ratings and developed country ratings. The model results also support the existence of subjective decisions or adjustments in sovereign risk assessment.