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.
This course introduces students to basic microeconmetric methods. The objective is to learn how to make and evaluate causal claims. By the end of the course, students should be able to apply each of the methods discussed and critically evaluate research based on them. Throughout the course we will make heavy use of Python and its SciPy ecosystem as well as Jupyter Notebooks.
For both the Scientific Computing and the Data Science course, students are required to work on their own projects independently. We have build a documentation that includes basic instructions as well as example projects from earlier iterations from the Data Science Course. Please direct to our project documentation for more information.