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)
- 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.