Master of Science in Economics

To make graduates of the College of Business specializing in economics more employable, the Economics Department has updated its master’s program to reflect recent developments in quantitative business.  In revamping the program, the department has taken advantage of the experience and training of one of its newest hires, Professor Harry J. Paarsch, who was employed for more than three years as a principal economist and data scientist by in Seattle. From his experiences working with dozens of new hires at Amazon, Professor Paarsch has identified a set of skills required to be successful in quantitative business today.

Recent graduates of the program have been admitted to graduate school at the University of Chicago, while others have interviewed with Amazon, Applied Quantitative Research, Hotwire, Indeed, Microsoft and Thumbtack, as well the National Security Agency. In addition, several grads have taken positions at Bank of America, Barbaricon, GEICO, NBC Universal, RTI International, Siemens Gamesa Renewable Energy, Sonny’s BBQ, and ZS. Eighty percent of graduates have been placed in jobs by the fourth month after graduation, while all have been placed within six months.

Foundation in Economics

First and foremost, quantitative methods of business are informed by theories from economics. That is, at some point or another, virtually every discipline in business uses basic principles from economics. Particularly important are models of incomplete information. For example, the models of moral hazard (where economic actors take hidden actions in their own self-interests, ones that surely are at variance with other economic actors) and adverse selection (where unobserved differences among economic actors known only privately to each result in their taking different actions) that economists have developed over the latter half of the 20th century.

Also important are models of equilibrium strategic behavior, such as those derived in the theory of noncooperative games of incomplete information, which allow the equilibrium features of models of moral hazard and adverse selection to be investigated. Perhaps the best known and most successful applications of equilibrium strategic behavior in the presence of adverse selection involve models of auctions. Auctions have garnered billions of dollars for firms such as eBay and Google and have also been important in determining which firms have access to the radio spectra that make using cellphones and WiFi so convenient. In the future, personalization on the Internet will make heavy use of models of incomplete information having private values, that is, models of adverse selection.

Methods of quantitative business rely heavily on tools from econometrics as as well as those from mathematical economics and operations research.

Current Curriculum

Although the structure of the master’s program has not changed, some of the courses have been replaced with those we believe are more relevant in the current business and economic environment. For example, in keeping with the previous program, two courses in microeconomic theory, a course in mathematical economics, a course in econometrics and a seminar in contemporary economic issues remain. Two capstone courses have been substituted for two field courses. We have replaced the macroeconomics components with a course in business analytics and a course in operations research. We augment econometrics with a course in data preparation.

At the heart of the solution to any business problem is a decision problem. Thus, in the first semester of the program, students will learn how to cast a business problem as a decision problem (ECO 6118); how to characterize the solution to that problem using methods from optimization theory and how to describe how optimal solutions are affected by changes in the environment (ECO 6403); how to implement computing the optimum using modern software (MAP 6207); and how to embed this whole framework within the ecosystem of a firm that uses business analytics (ECO 5445).

ECO 6118 Microeconomic Theory I Fall 3
ECO 6403 Mathematical Economics Fall 3
MAP 6207 Convex Optimization Fall 3
ECO 5445 Introduction to Business Analytics Fall 3


Having laid the foundations of decision theory in the first semester, in the second, students will learn how to implement decision problems using data: first, how to organize data (ECO 6445); then how to implement business analytic methods on a computer (ECO 6424); next how to embed decision problems in complex business environments containing incomplete information (such as those involving moral hazard or adverse selection) having equilibrium interactions (ECO 7116); and, finally, how to put it all together within the context of contemporary business and economic issues (ECO 6315).

ECO 6445 Data Wrangling Spring 3
ECO 6424 Econometrics I Spring 3
ECO 7116 Microeconomic Theory II Spring 3
ECO 6315 Seminar in Contemporary Economic Issues Spring 3


Capstone Experience

Capstone I and Capstone II are the culminating academic experience of the master’s program providing students with a forum in which to develop, carry out and write up research of a well-defined problem in business analytics using the tools developed in the program. Students will be required to pose a relevant, important problem in business analytics; develop the necessary economic theory to provide an interpretation of the empirical specification developed; gather and organize the relevant data; train, validate, and test the empirical specification; and write a report in which this research and the conclusions are presented in a convincing manner. During these courses, each student will give four presentations, settings in which personalized feedback can be provided in a timely manner.

ECO 6935 Capstone in Business Analytics I Summer A 3
ECO 6936 Capstone in Business Analytics II Summer B 3


In Capstone I, after two to three weeks of introductory presentations by the instructor, during which timely examples of good business analytics will be presented, students will be required to pose a relevant, important problem in business analytics, and then to give two presentations, one devoted to describing the economic model that will be used to structure the answer to the problem and the other devoted to describing available data sources. The main assignment, however, will be to produce a written research outline that will form the basis of the research planned for completion in Capstone II; feedback during the presentations will help students to refine their written outlines that are due at the end of the course.

In Capstone II, students will be required to implement their outlines: For the first two to three weeks, the instructor will illustrate useful ways in which to gather and organize data as well as to train, validate and test empirical specifications. Students will be required to give two presentations, one devoted to describing how they gathered and organized their data and the other devoted to describing how they trained, validated, and tested their empirical specifications as well as summarizing the preliminary results and conclusions of their research. Finally, students will be required to write a report in which their research and the conclusions are presented in a convincing manner. Again, feedback during the presentations will help students to refine their research papers that are due at the end of the course.

The two-course capstone sequence will prepare students for the initial assignment that virtually every business analyst gets during the first month on the job: take an ambiguous problem; put interpretable structure on the problem using theory; gather and organize data; train, validate and test the empirical specification; formulate the conclusions; and write up the research in a concise, effective way.


What Our Graduates Are Saying


Mike Pabon (UCF, Master of Science in Economics 2018)


Cody Krug (UCF, Master of Science in Economics 2018)


Joshua Eubanks (UCF 2017, Master of Science in Economics 2018)
currently working as a data analyst in the Wind Power and Renewables Department of Siemens Gamesa.



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