The MAE is an intense, full-time, 9-month (3 quarter) program, which integrates theory and applications. The first quarter will consist of microeconomic theory, macroeconomic theory, and quantitative methods courses that cover the basic tools and models used in economics literature. These same courses will continue into the second quarter, but with greater emphasis on applying the tools and methods. In addition to the three economics courses, the first two quarters will also include courses on written and oral communication of economic ideas, which will concentrate on how to structure a presentation or paper and how to communicate effectively when presenting arguments. The third quarter will consist of a variety of elective courses on specific subfields within economics. Each student will prepare and present a final project based on the content of one of these courses.
Below is a tentative schedule. More details will be posted as they become available. Classes are subject to change.
FALL QUARTER – THEORETICAL FOUNDATIONS
Four required courses
澳门真人游戏网址注册Coverage of fundamentals of optimization, choices by price-taking agents, consumer and producer surplus, monopoly and competition, Walrasian equilibrium and two welfare theorems, constant returns to scale economy, choice over time, uncertainty, and information and market design.
Introduction to main topics of graduate macroeconomics, including macroeconomic data, models of economic growth, supply and demand of factors of production, business cycle models, unemployment, monetary policy and inflation, and fiscal policy and deficits.
Applied Statistics, Econometrics and Time Series with R and Python
澳门真人游戏网址注册Introduction to probability, statistics, econometrics, and time series methods used in economics, business, and government using R and Python. Topics include estimation, simple and multiple regression, cross-sectional and panel data, instrumental variables, and estimation with stationary/non-stationary processes
Intro to Econometrics, Cross-Sectional and Panel Data, and Time Series
澳门真人游戏网址注册Introduction to econometrics, cross-sectional and panel data, and time series methods used in economics, business, and government. Topics include estimation, simple and multiple regression, cross-sectional and panel data, instrumental variables, and estimation with stationary/non-stationary processes.
Professional Development for Emerging Economists
澳门真人游戏网址注册Designed to help students develop professional skills essential for success in professional business settings. Aids students in translating topics covered in other courses into language and format that is accessible to industry/non-academic settings. Students conduct labor market research, identify and analyze industry trends, and develop targeted plan to achieve professional success. Exploration of skills identification, goal setting, researching employment market, and resume writing.
WINTER QUARTER – APPLIED ECONOMICS
Four required courses
Incentives, Information and Markets
澳门真人游戏网址注册Introduction to concepts of information economics that lie at heart of modern economics and application of them to understand incentives within firms, as well as competition between them. Study of theoretical models and functioning of real-life markets, such as insurance, labor, and consumer markets. Consideration of whether we can design policies that improve market outcomes. Role of models in economics, and how to tie data and theory together.
Investigation of rise of earning inequality (with emphasis on U.S.), focusing on learning how to use models and data to quantify impact of range of forces on inequality. Overview of broad empirical trends, with emphasis on understanding how to document these facts ourselves. Consideration of three classes of potential explanations for these patterns: international connections (e.g., trade and immigration), institutional change (e.g., minimum wage and unionization), and technical change (e.g., computerization and spread of robots). Focus on quantifying these forces ourselves. Study of top income inequality: why have extremely rich become much richer than very rich? Focus on CEO compensation.
Machine Learning I
Covers set of fundamental machine learning algorithms, models, and theories, and introduces advanced engineering practices for implementing data-intensive intelligent systems. Topics involve both supervised methods (e.g., support vector machine, neural network, etc.) and unsupervised methods (e.g., clustering, dimensionality reduction, etc.), and their applications in classification, regression, data analysis, and visualization.
澳门真人游戏网址注册Data Science for Financial Engineering
澳门真人游戏网址注册Data science provides many useful tools for modeling financial data and testing hypotheses on how markets work, and prices are formed. Study of these important tools. Focus on econometric models and methods to understand financial market dynamics. Topics include returns of financial assets, statistical tests on financial market efficiency, linear time series models, time-varying expected return models, heteroscedastic volatility models, optimal portfolio choice problem, capital asset pricing models, factor models, portfolio allocation, tracking and risk management.
Introduction to core principles of asset valuations. Emphasis on common economic reasoning used in valuation problems. Derivations and study of valuation formulas for three broad asset classes: fixed income securities, equity, and derivatives. Practical applications to investment problems, and relation to current financial news.
SPRING QUARTER – ELECTIVES
Choice of four elective courses
澳门真人游戏网址注册Data Analytics and Big Data
澳门真人游戏网址注册Designed for end users of big data, those who translate analytic results into business applications, with guest lecturers from wide spectrum of industrial and corporate big data users. Presentations of their business models for leveraging big data, sharing of data sets, and guiding students to extract actionable business insights for those industries. Taught by industry leader Dr. Rashed Iqbal.
澳门真人游戏网址注册Exchange Rate Forecasting, Big Data and Portfolio Design
澳门真人游戏网址注册Introduction to recent developments in international finance. Coverage of lending booms and financial crises both theoretically and empirically, as well as foreign exchange market anomalies and different approaches to forecasting exchange rates.
Fundamentals of Big Data
澳门真人游戏网址注册Introduction to basic concepts, uses, and challenges of big data, with emphasis on pragmatic hands-on applications using real-world data for current and future big data practitioners — consumers of big data insights for economic applications.
Macroeconomic Implications of Globalization
Development of understanding of some main macroeconomic implications of increasing integration of world economy through trade linkages, multinational production, and financial markets.
Money and Banking
澳门真人游戏网址注册Introduction to models and data used to understand connection between asset prices, health of financial sector, and macroeconomy, including review of recent papers to gain introduction to questions being addressed on research frontier.
Asset Pricing and Portfolio Theory in Practice
澳门真人游戏网址注册Study covers asset pricing and portfolio theory, critical areas for deeper understanding of financial markets and investments. Building from theory, incorporation of empirical analysis and real-world issues to bridge theory with practice through case studies.
澳门真人游戏网址注册Knowledge Discover and Data Mining
The courses will teach both theoretical and practical techniques in the field of data mining and knowledge discovery. The subjects include data processing, association rules, supervised learning, clustering, etc., and their applications in visualization, social network analysis, sentiment mining and opinion analysis. This course will focus on making sense of large-scale or web-scale dataset and bringing students with first-hand project experiences.
Applied Machine Learning
This course is a foundational course with the primary application to data analytics, but is intended to be accessible to students from backgrounds such as economics or mathematics; and to students from less technical backgrounds. The course covers some fundamental topics in Machine Learning such as Bayesian Learning, Optimization for Learning, Metric Learning, and various classification, regression, clustering techniques and other advanced topics. The students will work on real-world data intensive problems. A basic understanding of technology principles is needed, as well as basic programming skills, sufficient mathematical background in probability, statistics, and matrix analysis. Letter grading.
Students will learn how economic theory maps into policy-making. Renowned and influential policymakers from central banks, economics ministries, and international organizations will lecture on today’s most compelling policy-relevant topics. Students will complete a capstone project that fully engages the economic theories explored in lecture.
Each spring students will choose four elective courses and will prepare a final project based on the content of one of these courses. The final project will be designed by the student in concert with their faculty advisor and would enhance the student’s portfolio when they enter, or re-enter the job market. They will submit and present the results of their project in the form of a research paper. This capstone paper serves as a student’s “thesis” and is required for graduation.