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A Masters in Economics will equip you with skills in:

  • Economic Modeling and Intuition
  • Data Science and Econometrics
  • Communications and Analytic Storytelling

8 Month Course Schedule (mid-August to late April)

Fall Session 1 (7 weeks) Individuals, Firms, and Markets Quantitative Methods Communicating Economic Insights
  Session 2 (7 weeks) Incentives and Information Economic Inference from Data  
Spring Session 1 (7 weeks) Global Economics and Finance Applications of Economic Analysis Techniques Data Design for Economic Applications (Capstone)
  Session 2 (7 weeks) Evidence-Based Analysis in Labor, Public, and Health Economics Big Data and Forecasting in Economics  
The MQE academic year begins with a two-week Base Camp that starts two weeks before the fall term (Monday, August 14, 2023 for the 2023-2024 school year).  

The Spring 2023 schedule is as follows:

  • Session 1 - August 28 - October 20, 2023 (no class September 4, October 6)
  • Session 2 - October 23 - December 15 (no class November 20 - 24)

Interested in learning more?  Request Information here.

MQE Course Descriptions & Schedule 


Individuals, Firms, and Markets provide a rigorous introduction to contemporary microeconomic theory, with a focus on optimization techniques, the development of modeling skills, and critical thinking strategies needed to understand and evaluate a wide variety of economic contexts. Topics covered include consumer and producer theory, decision-making under uncertainty, choice over time, welfare analysis, competitive markets, monopoly, externalities, and public goods. The course stresses the application of theory to economics and business problems, and a substantial amount of classwork is devoted to examining scenarios based on such problems.


Quantitative Methods present a framework for data-driven decision-making under conditions of uncertainty and partial information, and it covers data analysis methods and techniques used in economic applications. The class will use R throughout; among the topics covered are graphical and descriptive data analysis, conditional probability, random variables, distribution functions, sampling, estimation, confidence intervals, hypothesis testing, and an introduction to regression methods.


Incentives and Information are central to modern economics, and this course studies the question of how individuals and firms respond to incentives. These incentives are shaped, in large part, by the information possessed by each relevant party. In this course, we study how such informational issues can lead to market inefficiencies, and what can be done to exploit or combat them. How should a seller design an auction, or the products available, to extract as much revenue as possible from buyers? How can firms design compensation schemes to get efficient effort from employees? What are the limits of what can be achieved through regulation? Students will learn how to address these and other similar questions using rigorous economic tools.


Communicating Economic Insights helps students develop written and oral communication and presentation skills essential for career success. Students practice writing documents for a variety of professional audiences, collaborative writing as well as multi-author revising skills. Students also learn presentation skills to enhance clear communication of ideas. Written and oral skills will emphasize the importance of one’s audience as it determines style, tone, organization, and depth of concepts.


Economic Inference from Data provides hands-on experience with applied econometric methods, allowing the student to establish empirical relationships of cause and effect. The course will cover advanced methods in regression analysis as well as a full toolkit of quasi-experimental methods that will allow the study of causal relationships even in the absence of a randomized control trial. The course includes hands-on empirical applications to solidify the concepts, with many examples from business and public policy settings. It will also focus on learning the basic tools of programming and coding in R.


Global Economics and Finance presents the main topics in modern macroeconomics and touches on important questions in international economics and finance. Among the first set of topics, the course covers economic growth and business cycles, unemployment and labor market frictions, inequality, nominal frictions and monetary policy, and fiscal policy. The course also studies international linkages, including global imbalances, capital account sustainability, international capital market integration, international trade, nominal and real exchange rate determination, external debt, and the relationship between inequality and international trade.


Applications of Economic Analysis Techniques move beyond ordinary regression to look at more specialized models and data that are important to economists. The course expands students' knowledge of econometric methods to account for qualitative and selected dependent variables via maximum likelihood and presents more structured estimation models. Throughout, the focus is on building students' experience with more advanced techniques, both for estimation and inferences; their understanding of the methods' pros and cons of these methods; and, importantly, how best to extract insights from them to aid in decision making.


Evidence-Based Analysis in Labor, Public, and Health Economics allows students to further develop their MQE toolkits through exposure to both seminal and frontier applied research on a diverse set of topics such as education, environmental sustainability, the non-profit sector, and employment compensation. In addition to reviewing extant applied research, students will hone their own analytical approach working both individually and in groups to apply economic thinking and analysis to a broad set of business and policy problems.


Big Data and Forecasting in Economics covers cutting-edge methods typically used in statistical learning and is designed to help students learn how to apply the econometric techniques learned in the previous courses in big data environments. The course introduces students to machine learning, text learning analysis, as well as scraping and data mining techniques using R. Some methods encountered in this course are classification, resampling, regularization, tree-based methods, supporting vector machines, deep learning, and unsupervised learning.


Data Design for Economic Applications (Capstone) helps students formulate questions that are critical for an organization and then, guided by economic theory, deliver informative answers using data. The first portion of the course rounds out the data science toolbox of students by training them in techniques for data creation, including survey design and the design and implementation of randomized control trials. The second portion of the course provides students with examples of problems facing real organizations to apply what they have learned in the MQE program, working in groups to identify questions, analyze data, and present results. Check out the 2021 Capstone Projects!