We investigate the impact of adding countries as a panel dimension to Macroeconomic data for economic forecasting. We find that this approach significantly improves the generalization ability of both structural and reduced-form models, and works synergistically with Machine Learning methods to further outperform traditional macroeconomic forecasting models. The addition of country-specific data reduces the root mean squared error (RMSE) by 12-24% across horizons and models. Furthermore, excluding US data and forecasting each country individually enhances the policy invariance and performance of both structural and reduced-form models. Additionally, the study demonstrates that the use of Recurrent Neural Network (RNN) models and Automated Machine Learning (AutoML) outperform baseline economic models in a data-rich regime. The findings are robust, reproducible, numerically stable, and applicable across various models.