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Kreinovich, V., Sriboonchitta, S., & Yamaka, W. (2024). Machine Learning for Econometrics and Related Topics. Springer.

has been cited by the following article:

• TITLE: From Econometrics to E.conometrics: Models, Forecasting, and Conditions of Validity

  AUTHORS: Luciana La Stella

  KEYWORDS: E.conometrics, Economic Forecasting, Economic Epistemology, Artificial Intelligence, Validation, Controlled Epistemological Circularity, Algorithmic Reflexivity, Economic Policy

  JOURNAL NAME: Modern Economy, Vol.17 No.9, September 24, 2026

  ABSTRACT: The transition from econometrics to e.conometrics proposed by Antonio Simeone, Marco D’Ambra and Paolo Savona on this Journal arises from the need to model economic systems characterised by nonlinearity, interdependence, shocks, emergent behaviour and increasingly rich, high-frequency information. This paper develops the concepts of controlled epistemological circularity, dynamic validity, and algorithmic reflexivity to distinguish forecasting from explanation and intervention. The paper argues that stronger predictive systems must state their uncertainty, applicability conditions, and limitations when informing economic-policy decisions.