Policymakers and institutions look to economists to evaluate and forecast trends that guide all areas of industry and government, from shaping policy, to safeguarding stock markets, improving production, reducing inflation and managing welfare.
The Mitchell E. Daniels, Jr. School of Business offers a comprehensive, customizable curriculum to allow graduates to achieve their aspirations in the field that most interests them.
Your Coursework
Economics master's students must complete 30 credit hours of coursework in the areas below. Required courses make up 9 credits. Students must earn a minimum 3.0 cumulative GPA to graduate.
Additionally, econ master's students have the option to specialize in Business and Data Analytics, Financial Economics, Public Economics and Policy, or Advanced Theory by earning at least 8 credit hours in the area. Other courses can be taken as electives. Students can also complete online graduate certificate programs, which serve as specializations.
Advanced Theory (Online/On-Campus)
Students interested in Advanced Theory are required to be on-campus for a portion of their program. They must complete the core classes online and then come to Purdue's campus for the final year (2 semesters) for an Advanced Theory specialization. In order to assess your eligibility for Advanced Theory, please contact your program specialist.
International students' eligibility for Advanced Theory may be impacted by visa restrictions under U.S. immigration law. Please check with a program specialist to learn more.
Combined Degree for Purdue STEM Majors
Current Purdue STEM undergraduates can apply up to 9 credit hours of coursework to both a BS and an MS degree in Economics. After graduation, these students are able to finish the MS program online as they pursue their careers.
Foundation Courses - required
This is a master’s level course in mathematics and its application to economics. Students in this class will review and practice the mathematical methods required to solve micro- and macroeconomic theoretical models, especially constrained optimization problems. The content covers a review of algebra, univariate and multivariate calculus, and constrained optimization methods, e.g., LaGrange multipliers. Mastering these methods is instrumental to preparing for ECON 511 & 512 (micro and macro theory).
This is a master’s level course in statistics and its application to economics. This course familiarizes students with the elements of statistics needed to perform econometrics, which students will practice extensively in the course, ECON 562. A crucial statistical tool is random variables, and this class will focus on: a) their distributions, properties (e.g., expected value and variance), and relationships (e.g., covariance), and b) their application to statistical inference, (e.g., confidence intervals and significance tests).
This master’s level course in econometrics covers the tools that will enable students to conduct empirical analysis using economics data. The course examines the statistical techniques used in testing economic theories, estimating casual effects and making predictions. Emphasis is placed on estimating a single equation (e.g., a demand function) and the problems associated with such estimation. As part of the course, students will estimate equations using STATA, a statistical software package.
Specialized Selective Courses
Selectives are a finite list of approved courses to meet your degree requirements (up to 21 credits). Electives may include any course taken to reach total credits but does not meet the degree requirements.
Business and Data Analytics Selectives
This is an introductory course in statistical and machine learning. It will cover fundamental concepts and essential tools that are critical in understanding cutting-edge machine learning techniques. Students will develop skills in applying a wide variety of modeling and prediction methods. Topics include linear regression, classification, regularization and shrinkage methods, tree-based methods, and support vector machines. An integral part of this course is the extensive use of the open-source statistical software R. Students will gain hands-on experience in analyzing datasets commonly used in business and economics.
This is a second course in machine learning. After studying the fundamental concepts and essential machine learning tools in Machine Learning I, this follow-up course will go over a range of more advanced topics and learning methods. Topics include support vector machines, deep learning and neural networks, principal components analysis and other unsupervised learning methods. As in the first course, we will be using the statistical software R and its packages extensively to implement various learning methods.
This course begins by reviewing basic concepts in probability, mathematical statistics and linear algebra. The purpose of this review is to provide a suitable foundation so that the student can obtain a better and more rigorous understanding of the linear regression model, its representation and assumptions, procedures for estimation and properties associated with the familiar OLS estimator. Once the mathematical preliminaries have been reviewed, we move on and apply what we have learned to the linear regression model. We discuss estimation and prediction, establish properties such as efficiency, unbiasedness, consistency and convergence in mean square. We conclude the course by going beyond linear models, specifically considering a few nonlinear models appropriate for modeling binary outcomes.
This course offers an introduction of basic principles of econometric analysis that will help students understand finance theories and their empirical applications. It will also equip students with appropriate statistical techniques for doing applied financial research. The statistical techniques are particularly well suited for analyzing financial time-series data.
The main goal of this course is to introduce economics students to computation and programming in Python. In the first part of the course, we will cover Python essentials including basic programming techniques and the use of popular packages for data analysis. In the second part of the course, we will consider more advanced programming techniques including numerical methods, dynamic programming, and simulation-based methods. Throughout the course we will consider a number of applications related to microeconomics, macroeconomics, and econometrics covered in the MS Econ program curriculum.
Game theory is a popular subject and became a powerful tool for analyzing strategic interactions between players or decision-making units. Players are decision-making units, e.g., individuals, firms, workers, managers, countries, etc. The main goal of this course is to provide a basis for a good understanding of the logical mechanics and to provide a good intuition. The course concentrates on strategy and econ-related applications that relate to firm behavior in markets and different types of market structures. Therefore, the game theory decision-making concepts will be applied to topics such as: Entry, R&D, Patent Races, Mergers, Cartel, Collusion, Advertisement, Auctions, Dynamic Games, New Product Introductions, etc.
Financial Economics Selectives
This course covers topics in international trade with a focus on trade policy. The course opens with presenting main questions in international trade that are crucial to the welfare of nations in the 21st century. In order to address these key questions, the course covers selected topics in international trade theory with a focus on their applications in real-world policy debates.
This course begins by reviewing basic concepts in probability, mathematical statistics and linear algebra. The purpose of this review is to provide a suitable foundation so that the student can obtain a better and more rigorous understanding of the linear regression model, its representation and assumptions, procedures for estimation and properties associated with the familiar OLS estimator. Once the mathematical preliminaries have been reviewed, we move on and apply what we have learned to the linear regression model. We discuss estimation and prediction, establish properties such as efficiency, unbiasedness, consistency and convergence in mean square. We conclude the course by going beyond linear models, specifically considering a few nonlinear models appropriate for modeling binary outcomes.
This is an introductory course in statistical and machine learning. It will cover fundamental concepts and essential tools that are critical in understanding cutting-edge machine learning techniques. Students will develop skills in applying a wide variety of modeling and prediction methods. Topics include linear regression, classification, regularization and shrinkage methods, tree-based methods, and support vector machines. An integral part of this course is the extensive use of the open-source statistical software R. Students will gain hands-on experience in analyzing datasets commonly used in business and economics.
This is a second course in machine learning. After studying the fundamental concepts and essential machine learning tools in Machine Learning I, this follow-up course will go over a range of more advanced topics and learning methods. Topics include support vector machines, deep learning and neural networks, principal components analysis and other unsupervised learning methods. As in the first course, we will be using the statistical software R and its packages extensively to implement various learning methods.
This course comprises a comprehensive introduction to finance. The objective of the course is to provide you with the conceptual framework necessary to appreciate and understand how to make decisions based on sound financial reasoning. Many of you may have others in the organization that ‘run the numbers,’ but in grasping the underlying concepts it is essential to understand the mechanics of the thought processes and get your hands dirty. At the same time, as a manager, you will need to understand the broader concepts and trade-offs involved with Finance and the course also has that goal in mind. Readings, case analysis, and problem sets focus on the basic tools used by financial decision makers.
This course offers an introduction of basic principles of econometric analysis that will help students understand finance theories and their empirical applications. It will also equip students with appropriate statistical techniques for doing applied financial research. The statistical techniques are particularly well suited for analyzing financial time-series data.
This course provides an introduction to forecasting methods of current interest in economics. The goal is to equip students with a working knowledge of state-of-the-art techniques that are useful for forecasting macroeconomic variables. The strengths and weaknesses of the techniques will be analyzed and their empirical relevance will be demonstrated through a variety of applications to real data sets. The topics will include univariate prediction models, vector autoregressions, forecasting with large data sets, forecast combinations and forecast evaluation. Particular emphasis will be placed on applications and the issues involved with implementation of the various methods in practice.
This course explores human economic behavior, with a strong emphasis on laboratory and field experiment methodology used in behavioral economics research. Topics considered include behavior in markets for financial assets and auction markets and behavior in social dilemmas that arise when people try to provide public goods voluntarily or increase economic surplus through trust. Students will also study how people bargain with and exhibit social preferences towards others. Decision-making and anomalies for risky uncertain choices will also be covered.
This course introduces you to the experimental methods used by economists to study human behavior. These methods analyze data collected in controlled laboratory experiments. Throughout the course, we will discuss different types of experiments that have been extensively researched by experimental economists. To help introduce and motivate each topic, you will participate in weekly synchronous experiments with your classmates over the internet. You will then complete an assignment analyzing the data generated by your class in the online experiment. The following lecture will provide an overview of the theoretical framework and experimental predictions. We will also discuss commonly observed behavior and any relevant explanations for such behavior.
Game theory is a popular subject and became a powerful tool for analyzing strategic interactions between players or decision-making units. Players are decision-making units, e.g., individuals, firms, workers, managers, countries, etc. The main goal of this course is to provide a basis for a good understanding of the logical mechanics and to provide a good intuition. The course concentrates on strategy and econ-related applications that relate to firm behavior in markets and different types of market structures. Therefore, the game theory decision-making concepts will be applied to topics such as: Entry, R&D, Patent Races, Mergers, Cartel, Collusion, Advertisement, Auctions, Dynamic Games, New Product Introductions, etc.
Public Economics and Policy Selectives
This course will explore the determinants of market and firm structure, firm conduct, and market performance in imperfectly competitive markets, and different approaches to regulating such markets. Emphasis is placed on using basic economic models of firm and industry behavior to explain and analyze real-world markets.
Game theory is a popular subject and became a powerful tool for analyzing strategic interactions between players or decision-making units. Players are decision-making units, e.g., individuals, firms, workers, managers, countries, etc. The main goal of this course is to provide a basis for a good understanding of the logical mechanics and to provide a good intuition. The course concentrates on strategy and econ-related applications that relate to firm behavior in markets and different types of market structures. Therefore, the game theory decision-making concepts will be applied to topics such as: Entry, R&D, Patent Races, Mergers, Cartel, Collusion, Advertisement, Auctions, Dynamic Games, New Product Introductions, etc.
This course explores the operations of government in society and the economy. Using real-world policy examples, motivations for intervention and policy alternatives are explored. A strong emphasis is placed on how individuals respond to government action. Policy topics covered include taxation, environmental policy, education policy, and transfer programs.
This course is designed to introduce masters students in economics to the field of health economics. We will analyze health and health care theories, institutions, and key policy issues. The course covers how the markets for health and health services are different from other goods, with a particular emphasis on the role of government and market failure. We will examine the demand for and the production of health and health care, and the behavior and organization of health care providers. We will also explore information asymmetries and the functioning of health insurance markets. We will consider health and healthcare systems around the world, paying particular attention to the U.S. health care system and recent reforms to it.
This course is an applied economics course that focuses on race and gender discrimination in the labor market. We will use the tools you have developed so far in the MS program to study the extent, causes, and consequences of disparities in wages and employment across race and gender.
This course is designed to give the student an understanding of both legal and economic principles and the relationship between them. It will also show the student how to access various databases, Lexis/Nexis, to get a formal statement of the law and how the laws have actually been interpreted and enforced. Finally, through the use of economic analysis, the student will acquire the tools to predict the likely outcomes of particular laws and how they will affect their family and business decisions.
This course teaches students the macroeconomic perspective on public policy. It aims to deliver students (1) a coherent theoretical framework with which to understand the tradeoffs in various public policies, and (2) the empirical facts that drive academic debate on those policies. Each class will give an overview of contemporaneous topics, such as (1) economic growth, (2) immigration (3) robots and automation (4) inequality (5) universal basic income and the social safety net (6) the basics of monetary policy, including new thoughts on Modern Monetary Theory and cryptocurrency, (7) government debt, (9) bank runs and the U.S. financial crisis, and (10) the Covid-19 pandemic.
This course offers an overview of the federal budget and the budget process. We will begin with an introduction to some of the key concepts and discuss the current components of both federal spending and revenues. We will then look at its history and reflect upon what its past, current state, and future path reveal about federal priorities. That is, we will discuss how it allocates scarce public resources and distributes the burden of paying for public goods and services (including the intergenerational aspects). We will also examine both the theory and practice of the budget process (president’s budget, congressional budget resolution, reconciliation, sequestration, etc.) and the main players (the budget committees, Congressional Budget Office, Joint Tax Committee, etc.). We will discuss the role of the different branches of government including the use of executive and judicial power. Along the way, we will have an introduction to the principal sources of information on federal budgeting, some of the important methodological issues (e.g. cash versus accrual accounting), and some of the different academic perspectives on budget policy. And last, we will selectively focus on the largest, most important budget categories.
Electives (up to 8 credits)
Accounting is the language of business. Sometime in your career you will be evaluated based on accounting information and you most certainly will need or want to use accounting information to make decisions. Therefore, accounting knowledge is useful. It will make you a better student in other courses but, more importantly, the better you understand accounting the more likely you will be able to differentiate yourself as an employee and a manager. One of the reasons you take this course first is so that you can apply your knowledge in later classes. Accounting has an underlying mathematical logic combined with the nuances, complexity, and shortcomings similar to legal statutes. These facets make it appealing but challenging.
This course emphasizes the role of AI in big data analysis. By utilizing cloud computing, this course serves as a platform for managers and data scientists who want practical experience in handling and analyzing large amounts of data. The course follows a three-module layout, starting with an introduction to cloud computing, its enabling technologies, and its key components. This module includes assignments using the Google Cloud Platform (GCP), providing students with hands-on learning in the cloud environment. The second module is devoted to AI tools like Generative AI, ChatGPT, LLM, and LangChain. These tools are used for moving, cleaning, structuring, storing, examining, and visualizing big data in the cloud. The module also focuses on 'Prompt Engineering', a field that improves the performance of AI models through optimized input prompts. The final module covers the creation of data pipelines in GCP. The knowledge gained in this module enables students to manage big data for training, analyzing, and predicting using AI/ML and other data science techniques.
Data analysis and modeling are important skills for effective managerial decision making in business and industry. Advances in technology have made gathering data and extracting valuable information far easier. These technologies, such as tablets and cellphones, web trackers, “smart” products, and many others, generate significant amounts of data and are available to managers. For example, the Dow Jones Industrial Average is one of the best-known and most widely watched indicators of the direction in which stock market values are heading. Administration and Congressional policymakers rely on statistics for budget decisions and related fiscal policy choices. The Federal Reserve System bases the monetary policy on data analysis. A manager needs to know whether the manufacturing process produces a quality product based on monitoring and assessing process performance. An effective sales manager must develop tools to regularly monitor the performance of the sales force, while an electronics manufacturer needs to produce a forecast of future sales in order to decide whether or not to expand production. Banks use customer data to identify and design lucrative banking products and find new viable services. These are a few of the many examples from business where statistics can improve company performance. The techniques learned in this course will help you infer data and make better informed decisions. The course covers basic probability, decision analysis, statistical analysis (sampling distributions and hypothesis testing), and simulation. Probability models provide tools to handle uncertainty and risk. Statistical analysis focuses on the presentation of data and techniques to draw useful and valid inferences from data. Decision analysis is a technique to use data to inform decision-making.
This course asks learners to view innovation through the lens of collaboration - as a phenomenon that occurs horizontally across organizational boundaries. Learners will gain insights and skills to design and guide the conversations, platforms, and ecosystems that lead to shared-value innovation. The course brings together theories and insights from a variety of disciplines including management, psychology, and social science. Understanding how to design and guide collaborative innovation processes is a vital skillset and knowledgebase in the 21st Century economy, defined more by open networks than the rigid hierarchies of the past.
Artificial Intelligence is a rapidly growing group of technologies that will radically change the shape of business and culture over the next several decades. This self-paced course will walk you through a high-level overview of the technologies that are considered AI and what is state of the art today. From there, we will look at three industry examples: Agriculture, Medical, and Marketing. These examples will include industry and academic expert perspectives and look at how each of the industries are already being impacted by the disruptive changes that AI is creating. Each week will consist of a series of readings and videos covering the material.
This course introduces students to critical marketing frameworks, concepts and terminology. Students are also introduced to marketing tools and methods used in segmenting markets and targeting customers, positioning products, conducting research, and developing strategic plans and tactical activities involving products, pricing, communications, and channels.
This course focuses on developing your negotiating skills and making you a more confident negotiator. By the conclusion of this course, you will have improved your ability to diagnose negotiation situations, strategize and plan upcoming negotiations, and engage in more fruitful negotiations, even in situations where you are dealing with difficult negotiation counterparts. Because negotiating agreements is as much art as science, learning in this course will take place mainly by doing experiential exercises, and I will draw on negotiations research to supplement this learning. I will place you in numerous realistic online negotiation settings, and you will need to actively prepare for, participate in, and analyze your negotiations. My hope is that this is a safe, substantial way for you to practice a valuable lifelong skill.
As goods and services are produced and distributed, they move through a set of inter-related operations or processes in order to match supply with demand. The design of these operations for strategic advantage, investment in improving their efficiency and effectiveness, and controlling these operations to meet performance objectives is the domain of Operations Management. The primary objective of this course is to provide an overview of this important functional area of business.
Strategic Management deals with the organization, management, and strategic positioning of the firm so as to gain long-term competitive advantage. To accomplish this objective, this course introduces and employs various analytical frameworks that help us to identify the sources of competitive advantage from both an industry and firm perspective. By focusing on what makes some competitive strategies strong and viable, while others remain weak and vulnerable, we shall develop the ability to consider the impact of change and other important environmental forces on the opportunities for establishing and sustaining competitive advantage.
Supply chains are connecting various entities involved in material, cash, and information exchange that eventual leads to product or service offering to customers. Efficient management of supply chains means not only optimizing firms’ internal operations but also going cross the boundaries of companies, industries, and countries to build capabilities, often together with business partners, in the process of product and service offerings. As an introductory course on the topic of supply chain management, we will layout the basic components of supply chains and introduce basic concepts in supply chain management. The focus of discussion will be given to how to effectively managing the supply stream and how to efficiently manage the demand. The overarching theme of the course emphasizes an integrated view of upstream and downstream in strategic planning for supply chains, as opposed to the conventional sales-driven planning paradigm.
R has been one of the leading open-source programming languages used by analytics professionals today. You will have the opportunity to get familiar with using the popular RStudio integrated development environment (IDE) to perform R programming and data analytics tasks. Nearly every class you will learn a new popular R package and become familiar with integrating these packages and corresponding functions with previously learned packages. A free DataCamp subscription will be provided to all enrolled students and those short examples will help reinforce certain concepts covered in the course. The culmination of this course entails a final team project where you develop and present a YouTube video of a Shiny app that supports a business problem.
This course, applicable across industries, aims to train leaders in algorithmic business problem-solving skills within the context of web data. It emphasizes the development of abilities to communicate with and manage teams that collect data and analyze them, and make data-driven decisions. We will cover tools to collect, manipulate, and analyze data from the web and other sources, with the objective of making students data savvy and comfortable with deriving insights from real-world, large datasets. Students will be exposed to the power of clickstream analysis and the possibilities that can be unleashed from testing and experimentation. A key aspect of the course is the integration of algorithmic thinking and computational abstraction. Students will learn to break down complex problems into manageable parts, filter out irrelevant information, identify key components, and create simplified solutions to problems. This approach not only simplifies problem-solving but also enhances efficiency and accuracy.
Electives
Students may choose elective courses to suit their individual interests. They may use as electives any MGMT, ECON or OBHR courses or credits that they have NOT used for filling other requirements.
Plan of Study
The Online MS Economics program at the Daniels School of Business helps students elevate their careers with critical expertise to draw insights through econometrics, machine learning, and applying financial and economic theory to make informed decisions that predict trends that influence industries and organizations. With a wide variety of electives, you’ll be able to specialize, furthering your career and reaching your aspirations.
Want to Learn More?
If you would like to receive more information about the Online MS Economics program at Purdue, please fill out the form and a program specialist will be in touch.
We are available to meet with you virtually to discuss how your goals align with our curriculum, community, student experiences, and outcomes to determine if Purdue is the right fit for you!
