Course Description 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.
The full class syllabus can be found here.
There should be about 19 classes and 9 TA sessions (first Monday excluded). This means that we have 28 in-class sessions total before the reading period.
Books Used
We will mostly used notes that I will provide. Sometimes we will cover lectures from the following sources:
Assignments
The schedule is listed in the "lectures" directory. Each day of lecture has its own directory. The agenda for each particular day is described in the readme file within each day's directory. For example, the agenda for the first class can be found here and the agenda for the second day can be found here.
Important: the agenda for each day includes tasks that you should do before lecture to prepare Please be sure to go over the agenda and do all of the tasks that need to be done before you come to class. These tasks are important to do beforehand so that you will be able to fully participate in that day's lecture. For example, on the agenda for the first day you need to do the following: