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Product progression: a machine learning approach to forecasting industrial upgrading.

Giambattista Albora^1,2, Luciano Pietronero², Andrea Tacchella³

¹Dipartimento di Fisica, Universitá Sapienza, Rome, Italy.

Scientific Reports

|January 27, 2023

Summary

Economic complexity methods need better evaluation. Out-of-sample forecasting shows tree-based machine learning models best predict new product activation, offering policy insights for product introduction feasibility.

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Surrogate Model Development for Digital Experiments in Welding

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Area of Science:

Economics
Data Science
Machine Learning

Background:

Economic complexity and relatedness measures lack systematic evaluation frameworks.
Forecasting new product activation is crucial for economic development and policy.
Existing methods require robust comparison and validation.

Purpose of the Study:

To establish a systematic evaluation and comparison framework for economic complexity methods.
To benchmark machine learning models for forecasting new product activation.
To provide a quantitative measure for assessing new product introduction feasibility.

Main Methods:

Utilized out-of-sample forecast exercises for systematic evaluation.
Compared various machine learning models, including tree-based algorithms.
Employed cross-validation, excluding country-specific data from training sets.

Main Results:

Tree-based algorithms significantly outperformed auto-correlation benchmarks and other supervised models.
Forecasting the activation of new products emerged as the key predictive task.
Cross-validation excluding predicted country data yielded the best predictive performance.

Conclusions:

Out-of-sample forecasting provides a robust framework for evaluating economic complexity methods.
Machine learning, particularly tree-based algorithms, offers superior predictive power for new product activation.
The developed approach offers a scientifically tested, quantitative tool for policy decisions on product introduction.