Advanced economic theory, experimentation, econometrics, machine learning and programming skills are prerequisites to excel in the Data Science and Analytics marketplace, regardless of whether you want to work on the private sector, the public sector, or at a NGO. We cultivate this knowledge in our technically sound curriculum, which contain complex, realistic empirical assignments from the start. Class are held full-time on campus in small classrooms where students get the opportunity to receive highly personalized attention from Faculty.
Take all of the following:
|Course Number & Description||Course Title||Units|
This course covers the basic mathematical concepts necessary to study economics and econometrics at the graduate level. After a review of single variable calculus, the course covers linear algebra, multivariable calculus, implicit functions and comparative statics, and unconstrained and constrained optimization. For all topics, applications from economics and econometrics complement the mathematical concepts.
|Essential Mathematics for Economic Analysis||4|
This course provides students with the probability and statistics background needed for the graduate programs in Business Analytics and Economics. The focus is on the principles of statistical reasoning and on methods used in statistical inference. Probability theory is discussed mainly as a background for statistical inference. In addition, this course will introduce students to the programming language R, with a special focus on performing basic data manipulation and statistical analysis with actual data sets.
|Essential Statistics for Econometrics||4|
Basic microeconomic theory including theory of the firm, consumer theory, general equilibrium, capital theory, and welfare economics.
Basic macroeconomic theory including markets for commodities and credit, the demand for money, market-clearing and the labor market, inflation and interest rates, investment, real business cycles and unemployment, economic growth, government consumption and the role of public services, and taxes, transfers, and the public debt.
This course provides the econometric foundation to model and evaluate economic behavior. The focus is on the construction, estimation, and testing of a classical linear regression model. Estimation methods include ordinary least squares, maximum likelihood, and instrumental variable. Consequences of a misspecified regression model are studied and the appropriate remedial measures are suggested. Further topics include functional forms, dummy variables, tests of structural break, and binary choice models. The emphasis is on the use and application of various statistical methods rather than on their thorough theoretical investigation.
|Advanced Econometrics I||4|
An introduction to standard methods of time series econometrics, with special emphasis on financial applications. The course deals primarily with modeling and forecasting with time series data. Topics include ARIMA (autoregressive integrated moving average),VAR (vector autoregression) , and GARCH (generalized autoregressive conditional heteroskedasticity) models. Other topics include instrumental variables, generalized method of moments, and maximum likelihood.
|Advanced Econometrics II||4|
This course provides an introduction to computer programming using R. Although the course will be taught using R, an emphasis will be placed on general rules and best practices for programming that are applicable to many programming languages. The first part of the course will cover general programming concepts including data structures, flow control, functions, recursion, and debugging. The second part of the course will apply this general programming knowledge to a variety of problems that economists face. Some of the problems include data collection and management, where we will cover string manipulation and regular expressions, web scraping, and database interfacing using SQL. Other problems include specific economic and statistical methods that rely on computers for implementation. These include optimization and maximum likelihood estimation, Monte Carlo simulations and bootstrapping, and dynamic programming.
|Computational Methods in Economics||4|
Variety of standard and advanced econometric techniques employed in applied microeconomics. Emphasis on when and how to apply appropriate techniques.
Select 13 units from the following:
|Course Number & Description||Course Title||Units|
Economic analysis of pollution, congestion, public good provision, and natural resource conservation. Static and dynamic efficiency, economic growth and sustainability, pollution taxes, marketable permits, and the design of market-based regulations.
|Environmental and Resource Economics||4|
Analysis of the international movement of goods, services, capital and payments. The role of exchange rates, tariffs, quotas, and transport costs. Relationship between international trade and economic growth.
Economic analysis of the rationale for public expenditure and taxation. Externalities, pollution and public policy, income redistribution and public welfare, public goods, collective choice and political institutions, public budgeting techniques and cost-benefit analysis, taxation and tax policy, state-local finance and fiscal federalism.
This course is designed to help you understand the practical and empirical implications of industrial organization IO that relate to how firms set prices and make strategic decisions that affect the performance of markets. The goal of the course is to develop a greater understanding of how microeconomic principles apply to different market structures, such as perfect competition, monopoly, and oligopoly, affect firm profitability and influence social welfare.
Research methods in labor economics and application of modern empirical techniques to the analysis of labor markets. Topics include labor supply and demand, discrimination, migration, and human capital accumulation.
|Advanced Labor Economics||4|
This course integrates what the students have learned in previous courses regarding data analysis, statistical estimation and inference into a platform suited for the study of decision-making under uncertainty. Students learn how to formulate probabilistic forecasts, represent complex decisions as Bayesian decision problems, identify the optimal decisions
|Evidence-Based Decision Analysis||4|
Advanced topics in economics chosen according to the common interests and needs of the students enrolled.
|Seminar in Economics||1-4|