Predictive economics: Rethinking economic methodology with machine learning
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908577854128128 |
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| author | Pereira, Miguel Alves |
| author_facet | Pereira, Miguel Alves |
| contents | This article proposes predictive economics as a distinct analytical perspective within economics, grounded in machine learning and centred on predictive accuracy rather than causal identification. Drawing on the instrumentalist tradition (Friedman), the explanation-prediction divide (Shmueli), and the contrast between modelling cultures (Breiman), we formalise prediction as a valid epistemological and methodological objective. Reviewing recent applications across economic subfields, we show how predictive models contribute to empirical analysis, particularly in complex or data-rich contexts. This perspective complements existing approaches and supports a more pluralistic methodology - one that values out-of-sample performance alongside interpretability and theoretical structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04726 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predictive economics: Rethinking economic methodology with machine learning Pereira, Miguel Alves General Economics Economics Machine Learning This article proposes predictive economics as a distinct analytical perspective within economics, grounded in machine learning and centred on predictive accuracy rather than causal identification. Drawing on the instrumentalist tradition (Friedman), the explanation-prediction divide (Shmueli), and the contrast between modelling cultures (Breiman), we formalise prediction as a valid epistemological and methodological objective. Reviewing recent applications across economic subfields, we show how predictive models contribute to empirical analysis, particularly in complex or data-rich contexts. This perspective complements existing approaches and supports a more pluralistic methodology - one that values out-of-sample performance alongside interpretability and theoretical structure. |
| title | Predictive economics: Rethinking economic methodology with machine learning |
| topic | General Economics Economics Machine Learning |
| url | https://arxiv.org/abs/2510.04726 |