Specific Requirements

An MSQE student completes at least 30 credits maintaining at least a “B” in each course. Of these 30 credits, 24 are core courses, and 6 are electives. Each course counts for 3 credits.

Course Sequence: Semester I (Fall)

  • Microeconomics: Consumer and producer theory, economic efficiency, and welfare analysis
  • Macroeconomics: Conceptual framework, and application to current macroeconomic problems
  • Mathematical Economics: Matrix algebra, optimization, and comparative statics
  • Applied Econometrics I: Statistical theory and linear regression

Semester II (Spring)

  • Applied Econometrics II: Large sample linear regression, time series analysis, maximum likelihood, GMM, and qualitative choice models
  • Programming and Computation with R: R programming for computational tasks on data analysis and visualization
  • Economics Elective (Open Source Programming with Python)
  • Writing Communication Economics, and Business I (optional)


  • Internship (optional)

Semester III (Fall)

  • Panel Data Econometrics: Analysis of cross-sectional data measured over time, and its implementation in STATA.
  • Machine Learning for Economists:  Statistical methods to analyze Big Data, such as classification, resampling, lasso and tree based methods
  • Economics Elective (Causal Program Evaluation, Convex Optimization with Python, Financial Econometrics, Operations Research)
  • Writing Communication Economics, and Business II (optional)

Economic Electives

  • Causal Program Evaluation: Learn some of the statistical methods and tools commonly used to evaluate causal claims about the impact of public policies and programs
  • Open Source Programming with Python: Use object-oriented programming for rapid application development and scripting.
  • Convex Optimization with Python: Recognize and solve convex optimization problems that arise in economics and econometrics.
  • Financial Econometrics: Learn and master some of the basic tools and techniques of mathematical finance.
  • Operations Research: Optimization of input and output mixes, of delivery routes, and communication networks.