Product Progression: a machine learning approach to forecasting industrial upgrading
This provides a quantitative measure for policymakers to assess the feasibility of introducing new products in specific countries, addressing an incremental improvement in economic forecasting.
The authors tackled the lack of systematic evaluation for economic complexity methods by using out-of-sample forecasting to compare machine learning models, finding that tree-based algorithms outperform benchmarks in predicting new product activations, with cross-validation excluding the predicted country yielding the best results.
Economic complexity methods, and in particular relatedness measures, lack a systematic evaluation and comparison framework. We argue that out-of-sample forecast exercises should play this role, and we compare various machine learning models to set the prediction benchmark. We find that the key object to forecast is the activation of new products, and that tree-based algorithms clearly overperform both the quite strong auto-correlation benchmark and the other supervised algorithms. Interestingly, we find that the best results are obtained in a cross-validation setting, when data about the predicted country was excluded from the training set. Our approach has direct policy implications, providing a quantitative and scientifically tested measure of the feasibility of introducing a new product in a given country.