A Category Theory Framework for Macroeconomic Modeling: The Case of Argentina's Bimonetary Economy

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.