Python code for teaching computational economics, implementing Solow, Ramsey, and Real Business Cycle models.
pyeconomics is a collection of Python implementations of foundational macroeconomic models used for teaching computational economics. It provides code for the Solow growth model, Ramsey optimal saving model, and a Real Business Cycle model, enabling students and researchers to analyze economic dynamics through simulation and numerical methods. The project focuses on bridging economic theory with practical programming skills.
Economics students and instructors learning computational methods, as well as researchers needing reference implementations of classic macroeconomic models for educational or prototyping purposes.
It offers well-structured, educational code for key macroeconomic models with integrated visualization and numerical analysis tools, making it a practical resource for hands-on learning compared to purely theoretical textbooks.
Computational economics in Python
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Clearly organized for teaching, with labs that guide students through Python functions, OOP, and model analysis, as seen in the README's step-by-step approach to each model.
Provides discrete-time versions of Solow, Ramsey, and RBC models, essential for understanding foundational macroeconomic theory through hands-on computation and simulation.
Incorporates advanced techniques like forward-shooting algorithms and Jacobian eigenvalue analysis, bridging theoretical economics with practical numerical skills for stability assessment.
Uses 3D graphics and contour plots to visualize economic relationships, such as the Cobb-Douglas production function, enhancing comprehension of complex model dynamics.
Focuses only on classic models from academic curricula; lacks implementations of modern economic theories or empirical models, reducing its utility for cutting-edge research.
Designed primarily for course use, so it may have minimal documentation for general developers and lack features like robust error handling or scalability for large-scale simulations.
Assumes familiarity with Python and libraries like NumPy and Matplotlib, but the README lacks detailed setup instructions, which could hinder quick adoption by new users.