Not too easy. Not too difficult.
Course Overview:
the course gives an introduction to multiple regression techniques with focus on economic applications.
Course highlights:
extensions to discrete response, panel data, and time series models, as well as issues such as omitted variables, missing data, sample selection, randomized and quasi-experiments, and instrumental variables. Also develops the ability to apply econometric and statistical methods using computer packages.
Hours per week:
9-11 hours
Advice for students:
A strong statistical base is mandatory for this course.
This class was tough.
Course Overview:
His knowledge of advanced statistics is impressive, and although he moves very quickly through the material, it is easy to follow his steps and the class structure is very interactive. (Note: I have not yet started studying at UChicago, so these are observations from a model class.)
Course highlights:
I learned how to make predictions (e.g., inferential statistics) using variables like phi and theta, which advanced upon my prior knowledge of linear regressions.
Hours per week:
0-2 hours
Advice for students:
Make sure you have taken the prerequisites (e.g., multivariate calculus and intermediate microeconomics). This course appeals especially to economics majors and math majors with an interest in fields like consulting.
Not too easy. Not too difficult.
Course Overview:
Required of students who are majoring in economics; those students are encouraged to meet this requirement by the end of their third year
Course highlights:
This course covers the single and multiple linear regression model, the associated distribution theory, and testing procedures; corrections for heteroskedasticity, autocorrelation, and simultaneous equations; and other extensions as time permits. Students also apply the techniques to a variety of data sets using PCs.
Hours per week:
9-11 hours
Advice for students:
It requires a pretty strong knowledge base. You should have done the following courses before you try this one: ECON 20100, STAT 23400, and MATH 19620 (or MATH 20000 or STAT 24300 or MATH 20250)