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Economic complexity methods, and in particular relatedness measures, lack asystematic evaluation and comparison framework. We argue that out-of-sampleforecast exercises should play this role, and we compare various machinelearning models to set the prediction benchmark. We find that the key object toforecast is the activation of new products, and that tree-based algorithmsclearly overperform both the quite strong auto-correlation benchmark and theother supervised algorithms. Interestingly, we find that the best results areobtained in a cross-validation setting, when data about the predicted countrywas excluded from the training set. Our approach has direct policyimplications, providing a quantitative and scientifically tested measure of thefeasibility of introducing a new product in a given country.

Product Progression: a machine learning approach to forecasting industrial upgrading