Capilla, JavierAlcaraz, AlbaValarezo Unda, Ángel EduardoGarcía Hiernaux, Alfredo AlejandroPérez Amaral, Teodosio2025-02-052025-02-052025-02-032341-2356https://hdl.handle.net/20.500.14352/117836The authors acknowledge the support of the HORIZON Research and Innovation Program of the European Union, under grant agreement No 101120657, project ENFIELD (European Lighthouse to Manifest Trustworthy and Green AI).Eco-RETINA is an innovative and eco-friendly algorithm explicitly designed for out-of-sample prediction. Functioning as a regression-based flexible approximator, it is linear in parameters but nonlinear in inputs, employing a selective model search to optimize performance. The algorithm adeptly manages multicollinearity while emphasizing speed, accuracy, and environmental sustainability. Its modular and transparent structure facilitates easy interpretation and modification, making it an invaluable tool for researchers in developing explicit models for out-of-sample forecasting. The algorithm generates outputs such as a list of relevant transformed inputs, coefficients, standard deviations, and confidence intervals, enhancing its interpretability.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/working paperhttps://www.ucm.es/icae/working-papersopen accessC14C45C51C63Eco-RETINAPredicción fuera de la muestra.AlgoritmoEconometría (Economía)5302 Econometría