Course Requirements
Second year students must take either three fields, or two plus the breadth requirement.
Course schedules and instructors may always change. Up-to-date listings and class details can be found through https://sa.ucla.edu/ro/Public/SOC.
The exact topics covered by any one class may vary from year-to-year, depending on the instructor. For detailed course descriptions and syllabi, please check the individual course website.
2024-2025 Field Requirements
Past Field Exams
Other Useful Classes
Below is a list of classes offered outside the economics department, along with the fields to which they broadly relate. These classes change from year to year, so they should be thought of as indicative of the options at UCLA, and students are encouraged to investigate further. Please email econserv@econ.ucla.edu with suggestions or updates.
Anderson:
Francis Longstaff: MGMT239A: theory of finance (finance, macro)
Barney Hartman-Glaser; MGMT239B: theory of corporate finance (theory, finance)
Avanidhar Subrahmanyam; MGMT239D: behavioral finance (applied, theory, behavioral, experimental)
Mikhail Chernov: MGMT239C: exchange under uncertainty (finance, macro)
Romain Wacziarg: MGMT298, methods in political economy (theory, applied, history, political economy)
Robert Zeithammer: MGMT269B, theory of marketing (theory, IO)
Peter Rossi; MGMT269E, Bayesian methods in marketing (IO, applied, metrics)
Political Science:
Michael Chwe; PS209: formal models in political economy (theory, political economy)
Daniel Treisman; PS259: growth and political economy (theory, macro)
Chad Hazlett; PS200D: empirical methods in political science research (applied)
Public Policy:
Magali Delmas; Environment 297B: seminar for environmental and behavioral economics (applied, IO)
Mathematics:
MATH 131 (a,b): undergraduate real analysis (everyone)
MATH245 (a,b,c): graduate real analysis (theory, metrics)
MATH275: probability theory (theory, metrics)
Statistics:
STAT 200,201,202 (a,b,c): first year PhD sequence (theory, applied, metrics)
STAT 235: machine learning (theory, metrics, applied)
Electrical Engineering:
EE231A: information theory (theory, macro)
