Franz Prante (Chemnitz University of Technology, Germany) and Karsten Kohler (University of Leeds, UK) have created a new website that provides free pedagogical resources for the simulation of macroeconomic models in the open-source programming languages R and Python.
Why a website on macroeconomic model simulation?
Learning mathematical economic models is not an easy task. Between solving equations and shifting curves around, students of economics often struggle to grasp the underlying economic intuition. For some, seeing the connection between a system of equations and a chain of causality is straightforward. However, many get lost in the algebra which can become an obstacle rather than a vehicle for economic intuition. Similarly, shifting the curves of diagrams works well for some students, while it is confusing to others (when does a curve shift, and when does it rotate?).
Yet, there is another way of building a thorough understanding of economic models: computer simulations. Simulations allow learners to solve economic models and visualise their results through plots. They enable a comparison of a model’s results under different scenarios, helping to develop an understanding of the causal stories the model tells. In this way, any tedious algebra can be postponed to a later stage, prioritising the building of intuition first. Simulations can be run by anyone, anytime, with the use of free open-source programming languages.
To facilitate the use of such computer simulations for the learning of macroeconomics, we have built a new open-source website.
What does the website offer?
Our DIY Macroeconomic Model Simulation platform provides a code repository and online script for macroeconomic model simulation. It follows a “do-it-yourself” (DIY) approach, empowering users to numerically simulate key macroeconomic models on their own using the open-source programming languages R and Python. The platform offers resources to deepen the understanding of both macroeconomic modelling and coding and will be useful to university teachers, students, and early-career researchers alike. The platform’s DIY-approach aims to foster reproducibility and open-source principles in macroeconomic research and education by providing learning materials that are freely available and modifiable by everyone. The platform’s content will expand over time through new entries added by the project team.
The platform covers an array of macroeconomic models, including canonical textbook models, models from different economic paradigms (e.g. Neoclassical, New Keynesian, Post Keynesian), and seminal models from the history of economic thought (e.g. Malthus and Ricardo). It bridges a gap between intermediate and advanced level macroeconomics by providing detailed yet accessible treatments of seminal macroeconomic models.
How to use the website
To get started, the first chapter of the website provides an introduction to the programming languages R and Python. For users that are entirely new to these languages, working through the examples of basic operations as well as some simple exercises at the end of the chapter will be useful. Chapter 2 then introduces the general principles of numerical simulation and explains how to visualise the output from a simulation run.
After that, the website presents a series of different macroeconomic models. These model entries are largely self-contained and can be read independently of each other. For each model, the chapters provide three main components:
- Model descriptions that concisely explain the key ideas, assumptions, and equations of each model. This helps users grasp the underlying concepts and intuition behind the models.
- Annotated code that allows users to numerically simulate the models, examine their results under different scenarios, and produce visualisations to better understand the models’ structure and output.
- Analytical discussions for users who are interested in delving deeper into the mathematical properties of the models.
To further facilitate the understanding of dynamic models in which variables change over time, Chapter 9 offers a general introduction to the mathematical analysis of dynamic models.
Who is it for and what gaps does it fill?
The platform will be useful for students of economics, academics who teach macroeconomics, academics in other fields who want to get an understanding of macroeconomics, and anyone who wants to develop their coding skills, considering that R and Python are two of the major open-source programming languages that are widely used nowadays in academia as well as the public and private sector.
As the platform covers macroeconomic models of different degrees of complexity, it provides resources for students from the second-year undergraduate level up to the PhD level. The platform’s resources can be utilised in the teaching of macroeconomics courses where they will contribute an interactive element that hones intuition, e.g. in seminar sessions. In times of increased cross-disciplinary research, the platform will also help researchers outside of economics to understand key ideas in macroeconomics. For example, in fields such as engineering, environmental sciences, and epidemiology, numerical simulations are commonly applied. The platform’s simulation approach to macroeconomics thus provides a point of contact with those disciplines, thereby facilitating interdisciplinary research collaborations.
Taken together, our open-source website aims to address several gaps in the current provision of learning materials for macroeconomics at the intermediate and advanced levels. First, by providing resources to numerically simulate models, the platform hones intuition and establishes a connection to empirical examples, thereby complementing existing learning materials such as conventional textbooks. Second, by providing both numerical and analytical discussions of seminal macroeconomic models, the platform facilitates the transition from learning macroeconomics at the intermediate to the advanced level. Finally, coding has become a key skill required in academia and large parts of the private sector. The platform provides an accessible environment in which students and applied macroeconomists can develop their coding skills in two of the major programming languages.
By Franz Prante and Karsten Kohler
Franz Prante is a research associate at Chemnitz University of Technology, where he is currently working on the macroeconomic effects of monetary policy and price effects on energy demand. He is also a PhD student at Université Sorbonne Paris Nord.
Karsten Kohler is a Lecturer in Economics at Leeds University Business School. His research draws on macroeconomics, finance, and political economy. Karsten has investigated the connection between business and financial cycles; the role of exchange rates in emerging market business cycles; the impact of financialisation on income distribution, as well as different growth models in advanced countries.
DONATE
Rethinkers are working hard to promote a pluralist economics education and public debate through engaging campaigns and student led initiatives. But to stay in the fight, Rethinking Economics needs your support!