logo OSE scientific computing

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The purpose of (scientific) computing is insight, not numbers.

—Richard Hamming.

The sound analysis of computational economic models requires expertise in economics, statistics, numerical methods, and software engineering. We first provide an overview of basic numerical methods for optimization, numerical integration, approximation methods, and uncertainty quantification. We then deepen our understanding of each of these topics in the context of a dynamic model of human capital accumulation using respy. We conclude by showcasing basic software engineering practices such as the design of a collaborative and reproducible development workflow, automated testing, and high-performance computing.

Students learn how to use Python for advanced scientific computing. They acquire a toolkit of numerical methods frequently needed for the analysis of computational economic models, obtain an overview of basic software engineering tools such as GitHub and pytest, and are exposed to high-performance computing using multiprocessing and mpi4py.

We build the course on the Nuvolos.cloud as an integrated research and teaching platform. The platform provides a simple, browser-based environment that allows for complete control over students’ computational environment and simplifies the dissemination of teaching material. It enables students to seamlessly scale up their course projects from a prototype to a high-performance computing environment.


                                    mirandafackler                   juddnumeriacal                                    

We use the book Applied computational economics and finance by Mario Miranda and Paul Fackler throughout the course. A special thanks to Randall Romero Aguilar who has also built a course around this book and maintains a Python implementation of the CompEcon toolbox. Many of our code examples are building on his implementation there. In addition, we will also consult Numerical methods in economics by Ken Judd for some of the more advanced material.


A review of machine learning (ML) literature for economics and econometrics Machine Learning Methods That Economists Should Know About by Susan Athey and Guido W. Imbens also serves as a useful resource.

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