MSQE Courses

Use core economic datasets such as the Panel Study on Income Dynamics, Consumer Expenditure Survey, National Income and Product Accounts, and the American Community Survey for quantitative economic research. Learn to clean and manipulate data to create datasets usable for economic research and to implement theory-based and atheoretic econometric models.

Credits: 3
Pre-req: 3 semesters calculus (Math 234), linear algebra (Math 340) & Graduate standing, or consent of instructor

Econometrics I (UW Course Guide)

Econometric methods, theory, and applications. Matrix algebra will be used. Topics include linear regression, least-squares estimation, inference, and hypothesis testing. Suitable for graduate (master’s level) students.

Credits: 3
Pre-req: 3 semesters calculus (Math 234), linear algebra (Math 340), mathematical statistics or consent of instructor

Key ideas in Microeconomics: consumer theory, producer theory, and markets under partial and general equilibrium, and with externalities or market power. The sequence will include an introduction to decision theory and game theory, and applications to auction theory and partially informed trade.

Credits: 3
Pre-req: 3 semesters calculus (Math 234), linear algebra (Math 340) & Graduate standing, or consent of instructor

Introduction to the use of Machine Learning (ML) in economic analysis. Covers basic techniques of ML, much attention will be devoted to evaluating the use of these tools in economics. Learn how economists are integrating the tools of ML with econometric techniques in current empirical research. Gain hands on experience in using these techniques to answer traditional questions of interest to economists. Topics include (i) an in-depth discussion of the differences and similarities in goals, empirical settings and tools between ML and econometrics, (ii) supervised learning methods for regression and classification, unsupervised learning methods, large data analysis and data mining, (iii) recent methods at the intersection of ML and econometrics, designed for causal inference, optimal policy estimation, estimation of counterfactual effects. The methods are taught with an emphasis on practical application.

Credits: 3
Pre-req:Graduate standing or consent of instructor

Most empirical work in economics focuses on estimation of causal effects, including causal effects of an economic policy, a program, or a person’s decision. Discussion of the basic causality issue and contemporary methods in econometrics for identifying and estimating causal effects.

Credits: 3
Pre-req: Economics 704 or consent of instructor

Techniques of machine learning and the use of these tools in economics. Exploration of how economists are integrating ML tools with econometric techniques in current empirical research. Hands on experience in using these techniques to answer traditional questions of interest to economists. Topics include (i) an in-depth discussion of the differences and similarities in goals, empirical settings and tools between ML and econometrics, (ii) supervised learning methods for regression and classification, unsupervised learning methods, large data analysis and data mining, (iii) recent methods at the intersection of ML and econometrics, designed for causal inference, optimal policy estimation, estimation of counterfactual effects. The methods are taught with an emphasis on practical application.

Credits: 3
Pre-req: Economics 725 or consent of instructor

Introduction to the basic concepts of Artificial Intelligence and its use in economics. Covers general principles and specific economic applications. Discusses implementing these approaches using economic data.

Credits: 3
Pre-req: Economics 725 or consent of instructor