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

TL;DR

This paper introduces a category theory-based framework for macroeconomic modeling, specifically applied to Argentina's bimonetary economy, capturing complex dynamics and structural misalignments more effectively than traditional models.

Contribution

It develops a novel category theory approach for macroeconomic modeling, integrating modern computational tools to better analyze and forecast complex economic systems.

Findings

Identifies significant structural misalignment between equilibrium and observed exchange rates.

Proposes a new aggregate indicator for devaluation risk.

Demonstrates the framework's synergy with machine learning tools.

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.