🤖 AI Summary
Traditional static macroeconomic models fail to capture the dynamic structural features of Argentina’s dual-currency economy. Method: This paper introduces a novel modeling paradigm grounded in category theory—representing economic states as objects, dynamic intervariable relationships as morphisms, structural evolution via forgetful functors and limits/colimits, and constructing a composite indicator for depreciation risk. Empirical analysis spans 2018–2023 and integrates machine learning to enhance forecasting and policy simulation capabilities. Contribution/Results: Results reveal a significant structural divergence between equilibrium and real exchange rates, validating the framework’s applicability to complex, institutionally heterogeneous economies. This study pioneers the systematic application of category theory to macroeconomic modeling, overcoming expressive limitations inherent in static algebraic paradigms. It substantially improves theoretical robustness and empirical interpretability in economic forecasting and policy analysis.
📝 Abstract
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.