This course introduces the empirical and computational techniques necessary for numerically solving and estimating economic models. The course covers topics in numerical methods, such as optimization, function approximation, and Monte Carlo techniques, as well as topics in data exploration, visualization, and estimation. Emphasis will be placed on developing effective programming and research practices. The course is structured through a series of applications in such topics as macroeconomic fluctuations, industrial organization, and asset pricing. The course will be taught primarily in Python. Though helpful, no previous experience with computer programming is necessary for this class.
Prerequisite(s): ECON 20100 and ECON 21020 or ECON 21030
Recommended: ECON 20200
This course will use GitHub to host lecture materials and to distribute and gather homework. Canvas will be used for grades. Please use the link below to reach the main course GitHub repository.
This class does a lot of live coding. For lecture's, I often use Python in Jupyter Notebooks. Examples of lectures from last year's course:
Course Material on Github