# Product Progression: a machine learning approach to forecasting industrial upgrading

arXiv: https://arxiv.org/abs/2105.15018 · HTML: https://mlsanity.com/paper/2105.15018/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 95 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [Unfolding the innovation system for the development of countries: co-evolution of Science, Technology and Production](https://mlsanity.com/paper/1707.05146/) (2017)

## Built on by (most cited first, 3 of 3)

- [Machine learning to assess relatedness: the advantage of using firm-level data](https://mlsanity.com/paper/2202.00458/) (2022)
- [Matrix Completion of World Trade](https://mlsanity.com/paper/2109.03930/) (2021)
- [Which products activate a product? An explainable machine learning approach](https://mlsanity.com/paper/2212.03094/) (2022)

## Abstract (arXiv)

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.