UCLA Master of Quantitative Economics (MQE) Program: An Overview

The UCLA Master of Quantitative Economics (MQE) program is a STEM-designated graduate program focused on providing students with extensive coursework focused on quantitative economics, data analytics, and finance, based on the skills desired by industry. It unites a global community of students, scholars, and industry leaders committed to leveraging data science, finance, and economics to address society’s greatest challenges. The program is designed to equip graduates with the applied concepts, technical tools, and analytical skills necessary to solve complex business problems facing government agencies, financial institutions, and global corporations.

Program Structure and Timeline

UCLA’s MQE is a 48-unit program that features a flexible timeline, allowing students to complete their degree in 9 to 18 months. Students can complete the degree in as few as 9 months (or 3 quarters - Fall, Winter, and Spring). However, students may choose to extend the time of the program up to 18 months (4, 5, or 6 quarters), completing 2 courses per quarter over the course of six quarters. The program requires 48 units of course work.

All students are required to take a foundational course in applied statistics and econometrics (Econ 430 and 441A) during their first term and enroll in Economists in Action (Econ 410) each term.

Curriculum and Coursework

The MQE program is focused on training students in data analytics, econometrics, machine learning, applied statistics, quantitative methods, forecasting, data mining, and finance through hands-on courses, applied business projects, research activities, group work, and assignments. Students will gain exposure to R, Python, SQL, Excel, and numerous financial platforms and tools throughout the program.

Once students have completed required foundational and subject-area courses, they can create their own course schedule to mirror their interests. The program requires 48 units of course work. Students are required to take Econ 430 and 441a and at least two of the following: 401, 402, or 433 during their first quarter in the program, and over the duration of the program choose from the following 400 series courses (ECON 401, 402, 405, 406, 409, 412, 414, 421, 422, 424, 425, 429, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 444, 445. Course list subject to change; MQE courses are only offered once per academic year). In addition, all students are required to enroll in Economics in Action (410) ever quarter in which they are enrolled in the MQE program.

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The MQE program is focused on providing students with extensive coursework focused on quantitative economics, data analytics and finance, based on the skills desired by industry.

Core Course Examples:

Econ 410 (Economists in Action): Through Econ 410, students will have the opportunity to attend several guest lectures and/or ‘mini courses’ hosted by the MQE. These distinguished speakers include noted academics, Nobel Laureates, government officials, and industry leaders. All have contributed greatly to their field and will impart knowledge and perspective making this a unique opportunity for students to learn about the key issues facing the world today. 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.
Econ 430 & 441A (Applied Statistics and Econometrics): Introduction to probability, statistics, econometrics, and time series methods used in economics, business, and government using R and Python.
Asset Valuation: 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.
Financial Accounting: 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.
Data Gathering, Cleaning, and Warehousing: Introduction to modern practices in data gathering, cleaning, and warehousing. Topics include Web scraping using API’s, engineering of R packages, and data manipulation in SQL. This course emphasizes applications of the data pipeline expected of an entry-level analyst.
Data Management Tools: Introduction to most requested data management tools in industry. Students gain hands-on experience with SQL database queries and database management through integrations with database management systems, query editors, and Python and R programming languages. Students practice saving advanced commands as stored procedures on collective database, simulating tasks seen in real world.
Fundamental Analysis: This course explores fundamental analysis, a method of measuring a security’s value by assessing economic and financial factors. Through lectures, readings, and interactive discussions, the course will explore macroeconomic and microeconomic factors that affect the intrinsic value of a security. This experiential course is designed to deepen student exposure to the world of fundamental equity research through the research and development of an investment memorandum. Students will also gain exposure to options through a series of lectures and applied activities.
Social-Emotional Learning: Designed to help students develop social-emotional learning skills through interactive activities and lessons to improve their abilities to succeed in variety of team settings. Lessons and activities are designed to be highly interactive, expressive, and creative and aid students in stress reduction, emotion management, and team building.
International Finance: Introduction to recent developments in international finance.
Information Economics: 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. Long-term Incentives - Investment and Efficiency Wages (incentivized to work via relational contracts.
Machine Learning: Covers set of fundamental machine learning algorithms, models, and theories, and introduces advanced engineering practices for implementing data-intensive intelligent systems. 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.
Financial Econometrics: 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.
Data Visualization: A course that will develop the data visualization toolkit of students using Tableau and Python packages. 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. Cointegration vs.
Cloud Services for Big Data: Introduction to cloud services software relevant for big data analytics and data scientists. Survey of Amazon Web Services. Study of automated solutions to data gathering, storage, and machine learning. Students acquire specific skill sets in application programming interfaces and web scraping with Python through hands-on problem solving.
Economic Forecasting: Outlook of economy is of vital importance for many key decisions. Introduction to theory and application of cutting-edge tools used by economists and business leaders to inform their views of economy. These tools are applied to forecast or nowcast key economic indicators such as inflation, unemployment, and gross domestic product.
Empirical Economics: 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?
Asset Pricing and Portfolio Theory: Study covers asset pricing and portfolio theory, critical areas for deeper understanding of financial markets and investments. Performance in Competitive Markets (Index vs.
International Economics: Investigation of several theoretical frameworks in international economics followed by applications to empirical questions. Neoclassical trade models, analysis of firms and heterogeneous producers, and economic geography topics. Case studies and empirical papers focus on understanding determinants of trade patterns and on measurement of aggregate and distributional effects of international trade.
Machine Learning and Econometrics: 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.
Modern Data Management Systems: 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.
Business Intelligence Software: 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.
Algorithmic Trading: 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.
Economic Growth: (contributions of capital accumulation and technological change in the process of growth. Understanding how frontier growth and catch-up growth are fundamentally different.
Global Economy: link between economic openness, trade, and capital flows, and debtor and creditor nations. The role of money, inflation, and exchange rates.

With approval of the MQE faculty committee, students may be permitted to enroll in PhD courses in the Department of Economics to count towards the degree requirements.

Concentrations

Students can focus their studies by choosing a concentration, such as Data Analytics. Courses for the Data Analytics concentration include: 412, 424, 425, 432, 434, 435, 445.

Capstone Project

Students must complete a capstone course in their final term. The capstone for the Master of Quantitative Economics degree is a required course which entails the completion of either a final project or a final exam that is evaluated by three instructors. Students should complete the capstone project in their final quarter of study.

Applied Projects

Throughout the year, the MQE partners with companies to provide students with opportunities to apply their MQE coursework and training to solve business problems faced by corporations of all sizes. Students work in small teams under the guidance of a faculty coach to analyze data and present solutions to the corporate partner. Applied projects range in length and focus based on the needs of the business partner.

Summer Bootcamps and Preparation

The MQE is a challenging program that picks up pace early on. As such, the MQE has designed a series of recommended and required offerings to help you prepare. Success in this program requires a proactive personal commitment. To meet the needs of students and the demands of industry, we will offer two sections of our Economics 430 “Applied Econometrics with Python” course. Eligibility for the advanced section of Econ 430 will be determined by scores on the technical assessment conducted on 9/23. Completing all MQE bootcamps and performing well on the technical assessment can lead to qualifying for the advanced track of Econ 430. Students who successfully complete advanced Econ 430 will later qualify for special advanced Machine Learning (ML) courses. Completion of all items listed below is imperative if your goal is to qualify for the advanced Econ 430. Summer course and bootcamps are essential for your readiness and success in the MQE program. We strongly encourage all incoming students to take the Python self-assessment to evaluate your current technical capabilities. The assessment should be taken without accessing notes or other resources. Campus fees noted above are an estimation. The bootcamps are conducted in person to ensure you receive hands-on experience and direct interaction with instructors. Your participation in these summer programs is critical for your success and progression in the MQE program.

Admission Requirements

Applicants must hold a bachelor’s degree from a regionally accredited institution, comparable in standard and content to a bachelor’s degree from the University of California. If your undergraduate degree is not in economics, it is recommended that you have Calculus I and II for engineering and math majors, Intermediate Microeconomics and Intermediate Macroeconomics. Prior work experience is not required for admission to the MQE program.

Standardized Tests

The GRE is not required for admission to the Master of Quantitative Economics (MQE) program. Applicants may choose to submit official GRE scores, which will be considered as part of a holistic review process.

Any applicant whose first language is not English must certify proficiency in English when applying to UCLA and, if admitted, upon arrival. Such applicants must submit scores received on the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS) as part of their application. Official TOEFL scores must be sent directly by the Educational Testing Service (ETS). A TOEFL score of at least 560 on the paper-and-pencil test or 220 on the computer- based test is the minimum required. For the internet-based TOEFL, applicants must have a minimum total passing score of 87. You do not need to meet the minimum passing score for each section if the minimum overall score of 87 is met. 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 or 7 or higher on the IELTS. These scores represent the minimum required for acceptance to a graduate program at UCLA. 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.

Application Materials

Applicants are required to submit a personal history statement. Applicants must submit a resume or CV. Two letters of recommendation are required from individuals, preferably academic or professional supervisors, who are well acquainted with your performance in academic or work settings. Although only two letters are required, a third letter will be reviewed if provided. applicant is a bachelor’s degree from a regionally accredited institution, comparable in standard and content to a bachelor’s degree from the University of California.

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Transcripts

Official transcripts are not needed during the initial stages of application. Please upload an unofficial copy of your transcripts to the online application. The letters should be uploaded by the letter writer to the online application. Official transcripts are not needed during the initial stages of application. Please note that submitted records become the property of the University and cannot be returned. If you are a university/college senior, do not risk missing the deadline by waiting for senior-year grades to be posted before submitting your application and transcript. International applicants should submit transcripts in both the original language AND the authorized, complete, and exact English translation certified by the issuing institution. Degree and diploma certificates (or other evidence of conferral of all degrees, diplomas, or professional titles) must accompany the transcripts and must also be submitted in the original language AND in English. This evidence may be in the form of officially certified copies of the actual diploma, entries on official records or official statements from granting institutions.

Application Process and Deadlines

Applications are reviewed on a rolling basis. Complete applications will be reviewed in the order in which they are received. The MQE program reviews applications on a rolling basis. For the 26-27 admissions cycle, the MQE will follow the review dates listed below. Applicants who apply by November 1, 2024 will receive a response from the MQE program by December 15, 2024. Applicants who apply by December 1, 2024 will receive a response from the MQE program by February 15, 2025. The deadline to be considered for the 2025-26 program is March 15, 2025. Applications for Fall 2026 are now open. The application deadline is March 15, 2026.

UCLA policies allow applicants to apply to one major/degree only, except for concurrent or articulated degrees.

Tuition and Fees

Total charges for the 2025-2026 academic year, including student fees, health insurance and tuition, are estimated at $67,680.40 based on 48 units. This cost excludes books and other costs of attendance. Individual costs will vary by quarter based on the number of units enrolled. citizens and permanent residents and $155.00 for all other applicants.

As a self-supporting program, the MQE is billed to veterans at the in-state tuition rate, and thus is eligible for tuition coverage under the Post 9/11 GI Bill® up to the amount of your eligibility percentage found on your Certificate of Eligibility from the VA. The cost of tuition for 2023-24 is $1,117.80per unit. Apply for your VA benefits by going online to either www.gibill.va.gov or www.eBenefits.va.gov. Once your application is approved, you will receive a Certificate of Eligibility (COE) which will list the benefit(s) you are eligible for.

There are two types of financial support available through the University: merit-based awards and financial need-based awards. The MQE program considers all accepted applicants for merit-based fellowships. No additional application is necessary.

Housing

There are several housing options available for UCLA MQE students. The assignment of university housing is lottery-based. You must first create your personal student account at my.ucla.edu. Go to my.ucla.edu. After logging in, you will land on the application page. The Community Housing Office (CHO) provides rental resources to the entire UCLA community.

Student Life and Resources

The MQE is not just a graduate degree program, it’s a community of students, scholars, industry professionals, notable economists, and employers. UCLA’s Veteran’s Resource Center offers programs, resources, events, and a connected community for Veterans. The Student Affairs Officer will counsel students on visas, enrollment, graduation procedures, and other relevant issues.

Academic Disqualification

A student who fails to meet the above requirements may be recommended for academic disqualification from graduate study. A graduate student may be disqualified from continuing in the graduate program for a variety of reasons. The most common is failure to maintain the minimum cumulative grade point average (3.00) required by the Academic Senate to remain in good standing (some programs require a higher grade point average). Other examples include failure of examinations, lack of timely progress toward the degree and poor performance in core courses. Probationary students (those with cumulative grade point averages below 3.00) are subject to immediate dismissal upon the recommendation of their department. Recommendations for academic disqualification are made by the Master of Quantitative Economics Committee. Students must submit an official advancement to candidacy form by Week 2 of the quarter in which they plan to complete their degree. For students who will be on filing fee during their final quarter, this form is due by Week 2 of the quarter prior to degree completion.

Why Choose UCLA's MQE Program?

Many students choose UCLA's MQE program for its unique blend of economics, data science, and finance. The program offers a rigorous curriculum, a prime location in Los Angeles, excellent career services and networking resources, UCLA's prestigious reputation, and a beautiful campus. The focus on applying what you learn in class and learning methods which would be useful in the real world have been extremely helpful. The most attractive aspect of MQE is the extensive career prospects it offers. At UCLA, one would have exposure to a diverse set of people both in respect of cultures and professional or academic background. The MQE program offers a bridge between finance, data science, and economics.

Career Prospects

UCLA Ranked No. of UCLA’s MQE graduates secure post-MQE plans and are employed across a range of fields within six months of completing the program. Whether you’re considering a career in data analytics, consulting, or finance, you’ll discover how the MQE program can help you achieve your career goals.

Staying Up-to-Date

Want to stay up-to-date on all things MQE? Join our info sessions to learn more about MQE career pathways, program requirements, curriculum, and our distinguished faculty .

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