Courses

Microeconomics I (UW Course Guide)

First course in a two-semester sequence covering: 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

Macroeconomics I (UW Course Guide)

First master’s level macroeconomics course. A mathematical approach to the study of aggregate output determination, including analysis of consumption, labor markets, economic growth, and business cycles. Analysis of fiscal and monetary policies and their interactions.

Credits: 3
Pre-req: 3 semesters calculus (Math 234), linear algebra (Math 340) 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

Econometrics II (UW Course Guide)

Econometric methods, theory, and applications. Topics include instrumental variables, GMM, panel data, limited dependent variables, time series, and vector autoregressions.

Credits: 3
Pre-req: Econ 704 or consent of instructor.

Microeconomics II (UW Course Guide)

Second course in a two-semester sequence covering: 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: Econ 701 or consent of instructor

Mathematics for Economists (UW Course Guide)

Mathematical techniques used in economics analysis at a Master level. Topics include: proof techniques, optimization, introductory topology, functional analysis, linear algebra for microeconomists and differential equations.

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

Microeconomics I (UW Course Guide)

First course in a two-semester sequence covering: 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

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

Econometrics II (UW Course Guide)

Econometric methods, theory, and applications. Topics include instrumental variables, GMM, panel data, limited dependent variables, time series, and vector autoregressions.

Credits: 3
Pre-req: Econ 704 or consent of instructor.

Econometrics III (UW Course Guide)

This course focuses on using econometric methods to address empirical questions, conduct empirical research, and write empirical papers in economics.

Credits: 3
Pre-req: Econ 705 or consent of instructor

Microeconomics II (UW Course Guide)

Second course in a two-semester sequence covering: 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: Econ 701 or consent of instructor

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.

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.