A machine learning approach to economic complexity based on matrix completion.

ABSTRACT: This work applies Matrix Completion (MC) - a class of machine-learning methods commonly used in recommendation systems - to analyze economic complexity. In this paper MC is applied to reconstruct the Revealed Comparative Advantage (RCA) matrix, whose elements express the relative advantage of countries in given classes of products, as evidenced by yearly trade flows. A high-accuracy binary classifier is derived from the MC application to discriminate between elements of the RCA matrix that are, respectively, higher/lower than one. We introduce a novel Matrix cOmpletion iNdex of Economic complexitY (MONEY) based on MC and related to the degree of predictability of the RCA entries of different countries (the lower the predictability, the higher the complexity). Differently from previously-de

SUBMITTER: Gnecco G 

PROVIDER: S-EPMC9187690 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature