Courses

University of Pittsburgh – M.S. in Quantitative

Economics (MQE)

The MQE program is a one-year, intensive graduate program designed to train students to think like economists and work like data scientists. Students develop a rigorous foundation in economic theory, econometrics, and machine learning, while gaining hands-on experience with real-world data. The curriculum emphasizes the full analytical pipeline—from problem formulation and data design to empirical analysis and communication—preparing graduates for careers in business analytics, finance, consulting, and public policy.

Tools and Technologies: R, Python, SQL, machine learning methods, econometric modeling, data visualization, and real-world datasets across finance, policy, and business contexts.

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

Interested in learning more?  Request Information here.

MQE Course Descriptions & Schedule 

INDIVIDUALS, FIRMS, AND MARKETS

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

Quantitative Methods presents a framework for data-driven decision-making under conditions of uncertainty and partial information, covering data analysis methods and techniques used in economic applications. The class uses 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

Incentives and Information are central to modern economics, and this course studies the question of how individuals and firms respond to incentives. In large part, each relevant party’s information shapes these incentives. In this course, we study how such informational issues can lead to market inefficiencies, and what parties can do 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 regulation can achieve? Students will learn how to address these and other similar questions using rigorous economic tools.

COMMUNICATING ECONOMIC INSIGHTS

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. Work on written and oral skills emphasizes the importance of one’s audience as it determines style, tone, organization, and depth of concepts.

ECONOMIC INFERENCE FROM DATA

Economic Inference from Data provides hands-on experience with applied econometric methods, allowing students to establish empirical relationships of cause and effect. The course covers 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 also focuses on learning the basic tools of programming and coding in R.

FINANCIAL ECONOMETRICS

Financial Econometrics focuses on the application of cutting-edge statistical models of time-varying data to the analysis of financial variables. Topics include forecasting, quantification of time-varying asset volatility, excess return decomposition using observable and unobservable Factor Models, and portfolio optimization. 

APPLICATIONS OF ECONOMIC ANALYSIS TECHNIQUES

Applications of Economic Analysis Techniques moves beyond ordinary regression to the study of 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 MACRO, INTERNATIONAL, AND APPLIED MICROECONOMICS

Evidence-Based Analysis presents theoretical explanations and empirical investigations of real-world phenomena ranging from international patterns of growth, development, and trade; to national-level observations of business-cycle activity and policy-intervention strategies; to micro-level studies on topics spanning education, environmental sustainability, the non-profit sector, and employment compensation. Students will hone analytical skills to apply economic thinking and analysis to a broad set of business and policy problems.

BIG DATA AND MACHINE LEARNING IN ECONOMICS

Big Data and Machine Learning in Economics covers cutting-edge methods typically used in statistical learning and is designed to help students learn how to apply core econometric techniques 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)

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. It does so through a series of semester-long projects conducted by groups of teams working to meet the needs of clients. Each client is matched with a specific team, where matching results from a rank ordering by students based on client presentations. Clients provide their teams with an empirical question or project, along with relevant organizational data; and teams spend the semester working to meet the client's needs. This typically entails cleaning and/or augmenting the client's data, and applying the theoretical and quantitative skills developed throughout the program to complete the task at hand. The result is a polished deliverable for the client, and valuable hands-on experience for the students that will enhance their employability and ensure their ability to hit the ground running as experienced data analysts. Check out Previous Capstone Projects!