The MQE is an intense program, which integrates theory and applications.  Students can choose to complete the degree in 9 (3 quarters) to 18 months (6 quarters). 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. The third quarter will consist of a variety of elective courses on specific subfields within economics. Students also have access to special professional development, data analytics and finance skill development seminars each quarter.  Each student will prepare a final, capstone project during their final quarter. Students who choose to complete the program in 4-6 quarters will take fewer courses each term.


The list below details courses offered during the 2020-21 academic year.  Classes are subject to change.

Those wishing to graduate within 9 months must complete 4 courses per quarter.

Microeconomic Theory (Econ 401)
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.

Macroeconomic Theory (Econ 402)
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 (Econ 430)
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 (Econ 431)
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 (Econ 429)
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.

Those wishing to graduate within 9 months must complete 4 courses per quarter.

Exchange Rate Forecasting, Big Data and Portfolio Design (Econ 409)
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.

Incentives, Information and Markets (Econ 421)
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.

Machine Learning (Econ 425)
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.

Data Science for Financial Engineering (Econ 432)
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.

Core Finance (Econ 433)
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.

Applied Data Management for Economists (Econ 441B)
A course that will develop the data visualization toolkit of students using Tableau and Python packages. Focusses will be on techniques to simplistically communicate data, Excel functions/dashboards, Tableau dashboards, Matplot library, and interactive visualizations with Plotly.

MQE Finance Laboratory (Econ 442B)
This course broadens exposure to tasks seen in a financial analyst role. Coding tasks will be centered around options order book, depth chart, volume profile, cointegrated assets, and commodities data. Theory covered will consist of behavioral finance relating to technical analysis, applications of portfolio optimization and hedging techniques.

Those wishing to graduate within 9 months must complete 4 courses per quarter.

Income Inequality (Econ 405)
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.

Money and Banking (Econ 406)
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.

Fundamentals of Big Data (Econ 412)
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.

Asset Pricing and Portfolio Theory in Practice (Econ 414)

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.

Machine Learning & Big Data for Economists (Econ 434)
Twenty first century is the century of big data, with large datasets now appearing in many scientific fields. These datasets cannot be analyzed using classical econometric techniques. Instead, to extract useful information from these datasets, we have to rely on modern machine learning techniques. Some of these machine learning techniques, including lasso, regression trees, random forests, principle component regression, and neural networks, will be discussed in the first part of the class. In the second part, we will cover cutting edge developments at the intersection of machine learning and econometrics. In particular, we will study double machine learning in detail and discuss how to apply it to enhance the analysis of classical econometric problems, such as program evaluation, demand estimation, and asset pricing. Throughout the course, theoretical concepts will be illustrated via applications.

Principles of Big Data Management Systems (Econ 435)
This course focuses on modern data management systems that are used in data analytics. It exposes the students to cutting-edge data management concepts and systems and provides the students the working knowledge needed to manage large-scale data. Cloud storage systems, NoSQL databases, and the map-reduce computing paradigm are among modern data management techniques that are covered in this course.

Financial Accounting (Econ 436)
Financial accounting is concerned with the preparation and public dissemination of financial reports designed to reflect corporate performance and financial condition. By providing timely, relevant, and reliable information, these reports facilitate the decision-making of investors, creditors, and other interested parties. Financial markets depend on the information contained in these reports to evaluate executives, estimate future stock returns, assess firms’ riskiness, and allocate society’s resources to their most productive uses.  This course provides a base level of knowledge needed by corporate executives to understand and discuss corporate financial statements. The process of learning how various business activities impact financial statements will also give you opportunities to learn and think about the business activities themselves. In addition, accounting provides a foundation for courses in other areas.

Applied Data Management for Economists (Econ 441C)
Introduction to Business Intelligence software relevant for Big Data and Financial Services companies. This course will survey Amazon AWS, PowerBI, and Hadoop, then selectively teach deployment of automated solutions on these platforms. Development of presentation skills necessary for industry.

MQE Financial Forecasting Lab (Econ 442C)
This course broadens exposure to tasks seen in a quantitative analyst role. Coding tasks will be centered around options order book, depth chart, volume profile, and commodities data to create and deploy algorithmic trading bots. Theory covered will consist of behavioral finance, automation of portfolio rebalancing, and hedging techniques.

Economists in Action

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.

English as a Second Language Placement Exam (ESLPE)

If your first language is not English, you must certify proficiency in English when you apply to UCLA. The MQE requires you submit TOEFL or IELTS scores as part of the admissions process. Official test scores will be required if you are admitted. TOEFL scores must be at least 87 on the internet-based test. These scores represent the minimum required for acceptance to a graduate program at UCLA. Please see the Graduate Division website for additional information at

If you score 100 or higher on the TOEFL iBT, or 7.5 or higher on the IELTS, you do not need to take UCLA’s English as a Second Language Placement Examination (ESLPE). If you score less than 100 on the TOEFL iBT, or less than 7.5 on the IELTS, you are required, upon arrival at UCLA, to take the ESLPE. Depending on your results on the ESLPE, you may be required to complete up to two English as a Second Language courses, beginning in your first term at UCLA. If ESL courses are required, you should enroll as soon as possible to avoid scheduling conflicts.

Tuition costs associated with required ESL courses are the responsibility of the student and the MQE does not reimburse for the cost of those courses.

Capstone Project

During the final quarter of the MQE, each student will complete a Capstone project. The final project will be designed by the student in concert with their faculty advisor and is designed to enhance the student’s portfolio when they enter, or re-enter the job market. Results of the project are submitted in the form of a research paper. This capstone paper serves as a student’s “thesis” and is required for graduation.