[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123021-en":3,"doc-seo-123021-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123021,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Product progression - a machine learning approach to forecasting industrial upgrading","Product progression uses a comparative machine learning framework to forecast industrial upgrading. The study argues that the central forecast object is the activation of new products and evaluates multiple supervised models against an auto-correlation benchmark. Tree-based algorithms outperform both baselines and other supervised methods. Best performance arises under cross-validation that excludes data from the predicted country. The resulting quantitative measure supports policy testing for introducing new products within specific countries.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nProduct progression: a machine learning approach to forecasting industrial upgrading  \nGiambattista Albora1,2, Luciano Pietronero2, Andrea Tacchella3 & Andrea Zaccaria2,4*  \nEconomic 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 outperform 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.  \nIn her essay The Impact of Machine Learning on Economics, Susan Athey states: “Prediction tasks [...] are typically not the problems of greatest interest for empirical research in economics, who instead are concerned with causal inference ” and “economists typically abandon the goal of accurate prediction of outcomes in pursuit of an unbiased estimate of a causal parameter of interest ”1. This situation is mainly due to two factors: the need to ground policy prescriptions2,3 and the intrinsic difficulty to make correct predictions in complex systems4,5. The immediate consequence of this behavior is the flourishing of different or even contrasting economic models, whose concrete application largely relies on the specific skills, or biases, of the scholar or the policymaker6. This horizontal view, in which models are every time aligned and selected, in contrast with the vertical view of hard sciences, in which models are selected by comparing them with empirical evidence, leads to the challenging issue of distinguishing which models are wrong. While this situation can be viewed as a natural feature of economic and, more in general, complex systems6, a number of scholars coming from hard sciences have recently tackled these issues, trying to introduce concepts and methods from their disciplines in which models’ falsifiability, tested against empirical evidence, is the key element. This innovative approach, called Economic Fitness and Complexity7–12 (EFC), combines statistical physics and complex network based algorithms to investigate macroeconomics with the aim to provide testable and scientifically valid results. The EFC methodology studies essentially two lines of research: indices for the competitiveness of countries and relatedness measures.  \nThe first one aims at assessing the industrial competitiveness of countries by applying iterative algorithms to the bipartite network connecting countries to the products they competitively export13. Two examples are the Economic Complexity Index ECI14 and the Fitness7. In this case, the scientific soundness of either approach can be assessed by accumulating pieces of evidence: by analyzing the mathematical formulation of the algorithm and the plausibility of the resulting rankings15–18, and by using the indicator to predict other quantities. In particular, the Fitness index, when used in the so-called Selective Predictability Scheme19, provides GDP growth predictions that outperform the ones provided by the International Monetary Fund10,20. All these elements concur towards the plausibility of the Fitness approach; however, a direct way to test the predictive performance of these indicators21 is still lacking. This naturally leads to the consideration of further indices, that can mix the existing ones22 or use new concepts such as information theory23. We argue that, on the contrary, the scientific validity of relatedness indicat","cbCaip996B8l537p","https://ap.wps.com/l/cbCaip996B8l537p","pdf",1506033,1,17,"English","en",105,"# Product progression: machine learning for forecasting industrial upgrading\n## Motivation: prediction versus causal inference in economics\n## Economic Fitness and Complexity (EFC) and relatedness\n## Competitiveness indices and relatedness indicators\n## Relatedness measures and their use cases\n## Forecasting framework and model comparison","[{\"question\":\"What is the main forecasting target in the study?\",\"answer\":\"The study identifies the activation of new products as the key object to forecast for industrial upgrading.\"},{\"question\":\"Which model family performs best in the comparisons?\",\"answer\":\"Tree-based algorithms outperform the auto-correlation benchmark and other supervised machine learning models.\"},{\"question\":\"How does the cross-validation setup improve results?\",\"answer\":\"Best results occur when the training set excludes data from the predicted country, implemented through a cross-validation scheme.\"}]","Product progression - 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