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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.13233 |
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| _version_ | 1866908493387137024 |
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| author | Pollicino, Luciano |
| author_facet | Pollicino, Luciano |
| contents | Traditional macroeconomic models, based on static algebraic systems, fail to capture the dynamics of a bimonetary economy like Argentina's. This paper proposes a framework based on category theory to develop a more flexible and structured model that represents the evolving relationships between key variables such as inflation expectations, interest rates, and currency demand. Using concepts like objects, morphisms, learning/forgetful functors, limits, and colimits, the model is applied to empirical data from 2018-2023. The findings reveal a significant structural misalignment between the equilibrium and observed exchange rates and propose a new aggregate indicator to measure devaluation risk. The framework demonstrates a strong synergy with modern computational tools like machine learning, offering a more robust approach to policy analysis and forecasting in complex economies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Category Theory Framework for Macroeconomic Modeling: The Case of Argentina's Bimonetary Economy Pollicino, Luciano General Economics Economics 18C10, 91B02 Traditional macroeconomic models, based on static algebraic systems, fail to capture the dynamics of a bimonetary economy like Argentina's. This paper proposes a framework based on category theory to develop a more flexible and structured model that represents the evolving relationships between key variables such as inflation expectations, interest rates, and currency demand. Using concepts like objects, morphisms, learning/forgetful functors, limits, and colimits, the model is applied to empirical data from 2018-2023. The findings reveal a significant structural misalignment between the equilibrium and observed exchange rates and propose a new aggregate indicator to measure devaluation risk. The framework demonstrates a strong synergy with modern computational tools like machine learning, offering a more robust approach to policy analysis and forecasting in complex economies. |
| title | A Category Theory Framework for Macroeconomic Modeling: The Case of Argentina's Bimonetary Economy |
| topic | General Economics Economics 18C10, 91B02 |
| url | https://arxiv.org/abs/2508.13233 |