Courses and Learning Objectives

Learning Objectives

The MAE degree program has five inter-related learning objectives.

  1. Understand and be able to apply economic theory to real-world scenarios.
  2. Understand and be able to apply econometric and statistical analysis to analyze data, estimate relationships, and forecast.
  3. Acquire the ability to obtain, organize, and clean data, analyze and visualize data, and use version control.
  4. Achieve familiarity and skills with programming languages (e.g., R, Python and various statistical software).
  5. Develop communication and presentation skills essential for success in any aspect of business and other forms of collaboration.



Students must complete a minimum of 27 graded semester credits. Of these, a maximum of 9 graded credits can be at the 400-level (equivalent to three 3-credit courses), and all other graded credits must be at the 500-level.

A student who takes 4 graded courses a semester can finish the program in one academic year, including the late summer special session prior to fall semester.

August prior to the Fall semester (three weeks, 3 cr.):

EconS 523 Data Management and Processing for Economics (Python and R), 3 cr.

Fall semester courses (12 credits):

  • EconS 525 (Masters Econometrics), 3 cr.
  • EconS 527 (Masters Microeconomic Analysis), 3 cr.
  • EconS 528 (Masters Macroeconomics), 3 cr.
  • EconS 529 (revised course) Writing and Presentation Skills for Economists (research methods), 3 cr.

Spring semester courses (12 of graded credits and 3 S-U credits):

  • EconS 524 (new course) Applied Machine Learning for Economics and Business, 3 cr.
  • EconS 536 (Applied Statistics and Econometrics for Economics and Finance), 3 cr.
  • EconS 701 Economics Capstone/Examination, 3 cr. (S-U graded)
  • Elective courses: Choose 1 electives, 3 cr. (including but not limited to the following):
  • EconS 522 Financial and Commodity Derivatives, 3 cr.
  • Stat 435 (Statistical Modeling for Data Analytics), 3 cr.
  • Stat 437 High Dimensional Data Learning and Visualization, 3 cr.
  • EconS 533 International Trade and Policy, 3 cr.
  • AgEc 535 Applied Industrial Organization, 3 cr.
  • EconS 573 – Economics of Development and Health, 3 cr.

EconS 701: Students must complete 3 semester credits of EconS 701 to graduate (1 credit project proposal in the first semester, followed by remainder in second semester). In this course, students will complete a capstone project that integrates economic analysis with data analytics. Assessment for this course will be conducted by a committee of at least three faculty.

Courses in other disciplines: Students can enroll in elective courses in other disciplines, such as Statistics, Environmental Science, Management, or related fields, as long as they are at the 500 level.

A student enrolled in full-time study can finish the program in one academic year, including the late summer special session prior to the Fall semester. This degree program classifies as STEM (CIP Code 45.0603: Econometrics and Quantitative Economics).

More information about course content:

Stat 435 Statistical Modeling for Data Analytics, 3 cr. Multiple linear regression with model selection, dealing with multicollinearity, assessing model assumptions, the LASSO, ridge regression, elastic nets, Loess smoothing, logistic regression, Poisson regression, and the application of the bootstrap to regression modeling.

Stat 437 High Dimensional Data Learning and Visualization, 3 cr. Course Prerequisite: STAT 435. Data visualization, metric-based clustering, probabilistic and metric-based classification, algebraic and probabilistic dimension reduction, scalable inferential methods, analysis of non-Euclidean data. Typically offered Spring.

EconS 522 Financial and Commodity Derivatives, 3 cr. Design, trading, structure, and pricing of derivatives; working knowledge of how derivative securities work, how they are used, and how they are priced.

EconS 523 (new course) Big Data for Economics and Business, 3 cr. Introduction to data and programming. Students will work with data in spreadsheet format, explore what it means to have a ‘tidy’ dataset and its benefits, gain experience programming in R and version control with Git, engage in data exploration and the benefits of exploratory analysis and data visualization as a subspace of data exploration.

Econs 524 Applied Machine Learning for Economics3 cr Introduction to machine learning algorithms and concepts; supervised and unsupervised learning methods; foundational theory and application to data; statistical and computational methods. Recommended preparation: linear algebra, calculus, and statistics with calculus. Cooperative: Open to UI degree-seeking students.

EconS 525 Master’s Econometrics, 3 cr. Theory and practice of multiple regression methods; applications to the study of economic and other phenomena; use of computer regression programs. Required preparation must include introductory statistics course. Cooperative: Open to UI degree-seeking students.

EconS 527 Microeconomic Analysis, 3 cr. Consumer and producer behavior; partial and general equilibrium; game theory; imperfectly competitive markets; and market failures. Required preparation must include intermediate microeconomics and calculus course work. Cooperative: Open to UI degree-seeking students.

EconS 528 Master’s Macroeconomics Analysis, 3 cr. Master’s-level course to develop a coherent theoretical framework to interpret macro data and to analyze macro policy. Cooperative: Open to UI degree-seeking students.

EconS 529 (revised) Research Methods/Writing and Presentation Skills for Economists, 3 cr. Designed to develop communication and presentation skills essential for success in any aspect of business. Practice in writing economics documents for variety of professional audiences. Writing taught as process—brainstorming, collaborating, continually revising, and challenging ideas. Presentation skills to focus on presenting information clearly and organizing ideas, with emphasis on role of audience when presenting, because audience determines diction, style, tone, organization, research, and ideas. Grammar incorporated as needed, especially in regard to writing.

EconS 533 International Trade and Policy, 3 cr. International trade theories, policies, and research issues related to world trade with emphasis on agricultural commodity markets. Cooperative: Open to UI degree-seeking students.

EconS 536 Applied Statistics and Econometrics for Economics and Finance, 3 cr. Data and problem driven approach to formulating, estimating, and interpreting models that address problems in the area of finance and financial economics; review relevant basic statistics and probability concepts, and apply these to linear regression, regression diagnostics, and time series econometrics.

EconS 573 Economics of Development and Health, 3 cr. This course will introduce students to selected methodological and topical issues related to the study of development and health economics, within the context of low- and middle-income countries (LMICs). In addressing important issues surrounding methodology and current topics, development and health are conceptualized as an integrated whole in this course. The course is divided in two. The first 6-weeks introduces the domains of development and health economics as they relate to low-middle income countries and then focuses on common methodological approaches. The following topics are included in this section of the course: a case study of the integration of health and development in improvements in maternal health, and causal identification in study design with examples from random control trials and behavioral experimental designs. The second part of the course focuses on selected topics. These include: poverty, education and health, nutrition, and economics of epidemiology, among others. Applications will tend to focus on human health.

EconS 701 (new course) Economics Capstone, 3 cr. Course Prerequisite: Admitted to the Master of Applied Economics program. Capstone for professional master’s degree under the Graduate School. Integration of coursework in a project; assessment. The course will be graded and include a balloted evaluation of the student’s completion of the capstone project by the program’s graduate faculty.