Before the PhD · Build your foundation

Math and coursework

Strong quantitative preparation is the single most consistent requirement across economics PhD programs. More math is almost never held against you.

2 min read · By Marc Sleiman

Modern economics relies on advanced mathematics, statistics, computation and econometric methods, and the first year of a PhD assumes you are fluent in them. That is why quantitative preparation is one of the most important parts of an application. Take as much rigorous quantitative coursework as your schedule reasonably allows. Additional mathematics is rarely viewed negatively: it signals analytical maturity and prepares you directly for first-year coursework.

Real analysis is the key signal

If you take one thing from this page, take this. Real analysis, the rigorous, proof-based study of calculus, is the course admissions committees look for first, because it is the clearest evidence that you can handle the proof-based mathematics of a PhD. Course names vary: at many universities it is called “Analysis I,” “Introduction to Analysis” or “Advanced Calculus.” Check the course description, not just the title. Take it in a semester when you have room to learn it well, because the grade matters.

The core: minimum preparation for strong programs

• Calculus I–III, including multivariable calculus
• Linear algebra, ideally both an introductory and a proof-based course
• An introduction to proofs (often called “bridge to higher mathematics” or “methods of proof”)
• Real analysis I

Strong additional preparation

• Differential equations
• Real analysis II
• Mathematical statistics, two semesters
• Applied or advanced matrix algebra

Useful for many fields

• Numerical analysis
• Optimization
• Numerical linear algebra

If you are drawn to theory or econometric theory

Students interested in econometric theory, mathematical economics, optimization or microeconomic theory should go further: graduate-level real analysis and statistical theory. These courses are particularly valuable for research at the intersection of economics, statistics and applied mathematics.

Econometrics

Take as many econometrics courses as you can. Beyond the standard undergraduate sequence, look for advanced econometrics or special topics, panel data methods, time series or financial econometrics, causal inference and machine learning. Strong performance in advanced econometrics is viewed favorably by both predoc programs and PhD admissions committees, and it gives you the skills research assistant jobs require.

Programming

Research today is computational. Learn at least one of Stata, R or Python well, and ideally more than one. Courses with real data analysis components count, and so does a research assistantship where you clean and analyze data every day.

An example: Georgia State University

To make this concrete, here is how the list maps onto courses at Georgia State. Your university’s numbers will differ; match the content, not the number.

Once you have the core, consider going one step further: taking a graduate or PhD course is one of the strongest signals you can send.

Sources

American Economic Association, Recommended Mathematical Training to Prepare for Graduate School in Economics.