Explore Our Rigorous Curriculum
UCLA’s MQE is a 48-unit program that features a flexible timeline, so you complete your degree in 9 to 18 months. Throughout the program, you’ll gain exposure to R, Python, SQL, Excel and numerous financial tools and platforms.
Create Your Course Schedule
Once you have completed required foundational and subject-area courses, you can create your own course schedule to mirror your interests. If you choose, you can complete a concentration in data analytics, finance, or international & monetary economics.
Fall Course Offerings: Theoretical Foundations
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.
Introduction to the Macroeconomy
Production, Distribution, and the Markets for the Factors of Production (general equilibrium model focusing on labor and product markets, human capital accumulation, the determination of the distribution of income between labor and
Consumption, Investment, and Savings (general equilibrium models focusing on capital markets, and the determinants of consumption and savings)
Financial Markets (process of financial intermediation and how it brings the suppliers and users of capital together; impact of technological change on financial intermediation; how venture capital promotes new firm creation)
Forecasting Methods (introducing simple and accurate methods, including smoothing, modeling trends, seasonal adjustment, ARIMA models)
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. The process of innovation and technology adoption)
Global economy (link between economic openness, trade, and capital flows, and debtor and creditor nations. The role of money, inflation, and exchange rates. International policy coordination)
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. In addition, professional development seminars will teach students how to effectively translate their academic training for career opportunities. Throughout the term students will have an opportunity to reflect on how their MQE coursework is helping to prepare them for future careers in applied and quantitative economics.
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.
Career goals, finding fit, discovering opportunities; targeting applications
Job search strategies: employment research, job search techniques, analyzing labor market trends, skill development
Personal Branding: marketing yourself for the job market; personal pitch; managing your brand
Professional writing; Resume and cover letter writing
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
Exploring and Transforming Data
Generalized Linear Models
Heteroskedasticity & Robust Estimation
IV – Two-Stage Least Squares; System Estimation
Cross-sectional & Panel Data
Time Series Introduction
Modeling and Forecasting Trend
Modeling and Forecasting Seasonality
ARMA & ARIMA
Regression with Time Series (VAR & VMA)
Volatility Modeling (ARCH & GARCH)
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.
Interest rate analysis and fixed income markets (Time value of money: simple and compound interest rate, present value formulas, internal rate of return; Fixed income security pricing, Interest risk, duration and immunization; The term structure of interest rates: expectations hypothesis, liquidity preference theory, segmented market theory)
Portfolio choice and equilibrium asset pricing (Static portfolio choice for mean-variance investors: efficiency frontier, optimal portfolio, two-fund separation theorem; Static market equilibrium with mean-variance investors: market portfolio, security market line, idiosyncratic and systematic risk; Net present value for risky projects
Factor models (Equity valuation: the Gordon Growth Model and valuation ratios; Dynamic portfolio choice, hedging demand, macroeconomic determinant of interest rates and risk premia)
Derivatives (Arbitrage: the no-arbitrage condition, state prices, risk-neutral probabilities; Forward and future contracts: pricing, equivalence; Option basics: payoff, option portfolios, put-call parity; Binomial option pricing: binomial tree, dynamic hedging portfolio, delta; Merton-Black-Scholes option pricing: Itoˆ’s Lemma, Black-Scholes Formula, implied volatility, greeks; Options are everywhere: real options, embedded options, equity as an option, distance to default)
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.
Balance Sheet; Income Statement
Statement of cash flows
Current assets and Non-current assets
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. This instruction taught is offered as a supplement to the MAE coursework by providing solutions to expedite R coding techniques and the dissemination of analytic findings.
Survey of Relevant R packages for Data Import, Wrangling and Visualization
Creation of sharable R packages
API’s with R
Examples of API’s
Uploading and editing SQL databases and Multitable commands
Data Visualizations With Excel, MySQL and NodeJS
Interactive data visualizations with R
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. Use of Excel and Visual Basic for Applications to make data cleaning, visualization, and data management processes more efficient.
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. This course requires a basic understanding of finance and financial markets concepts and theories.
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. Students are aided in translating topics covered in other courses into language and format that is accessible to industry/non-academic settings.
Winter Course Offerings: Applied Economics
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.
Models of Exchange Rate Determination: Uncovered Interest Parity and exchange rates over the short-run; Monetary Policy, the Taylor Rule and interest rates; Forecasting a time series and the Kalman Filter; Foreign Exchange Rate Anomalies (Forward Premium Puzzle; Delayed Overshooting; The Carry Trade); Rationalizing the Anomalies (Rational expectations equilibria; Behavioral Finance and Cognitive biases); Purchasing Power Parity and exchange rates; Foreign Currency Futures and Options
Forecasting Exchange rates and Strategy Design: Basic Structure of strategy coding; Entry and exit Order types; Filters; Creating Indicators and trading setups; Fundamentals-based forecasts; Sentiment-based forecasts; Forecasts based on Big-data
Evaluation of Forecasts and of Portfolio Strategies: The Sharpe Ratio and alternative statistics; Degree of Robustness of a strategy; The Diebold-Mariano and Clark-West tests against the random-walk; Binomial test of directional forecasts
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.
Introduction, Performance-Pay (welfare theorems, externalities and market power;
Performance Pay – Applications (Informativeness Principle; how to approach a Case Study)
Identification (Identification strategies)
Multi-tasking (how to incentivize agent who can manipulate performance measures, and how to motivate an agent to take multiple actions)
Teamwork and Team Incentives (use tournaments incentivize workers, collusion, free-ride
Long-term Incentives – Investment and Efficiency Wages (incentivized to work via relational contracts. We test this using car plants (Capelli, Chauvin) and fast-food restaurants (Krueger))
Asymmetric Information (examines how a firm overcame the ratchet effect; asymmetric info and derive the best way to price discriminate)
Pricing and Auctions (optimal way to price discriminate; examine the performance of auction vs posted-price on eBay)
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.
Python Programming: List, Array, Data Processing and Visualization
Introduction to Machine Learning
Linear Models for Regression
Implementation of Linear Regression and Its Variants (Lasso, Ridge)
Linear Models for Classification Gradient Descent and its Variants
Polynomial Expansions and Filtering Spline Models and Regularizations
Feedforward Neural Networks LSTM and Time-Series Data
Max-margin Learning and Line Support Model
Kernel Support Vector Machine
Bagging and Boosting Adaboost Algorithms
Clustering and K-means methods
Fuzzy C-means and Constrained Clustering
PCA and Robust PCA
ICA and Multidimensional Scaling
Subspace Learning: NMF and LLE Visualization of High-Dimensional Data
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 develop a targeted plan to achieve professional success.
Networking and the hidden job market
Leveraging social media: LinkedIn; online portfolios; GitHub
Interview strategies: preparation, behavioral interviews, industry-specific/technical interviews; mock interview lab
Presentation design and delivery: audience targeting; persuasive presentations
Job and salary offer negotiation; professional workplace skills
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.
Stylized Facts of Financial Data (Compounding and future value of asset; simple return and continuous compounding return; returns of portfolio)
Concepts in Probability and Risk Measure (Characteristics of distributions; Quantiles of a distribution; Value-at-Risk (VaR) and Expected Shortfall (ES); Bivariate distributions; Covariance, correlation, autocorrelation)
Statistical Methods and Risk Management (constant expected return model; descriptive statistics: histograms, sample means, sample variances/covariances; Standard errors of estimates; Confidence intervals; Estimation and inference of VaR and ES; Testing the efficient market hypothesis
Linear Time Series Models (Strictly stationarity and covariance stationarity; information set and conditional expectation; Martingale and martingale difference; ARMA process; Nonstationary time series; Random walk tests)
Heteroscedastic Volatility Models (Volatility clustering of asset returns; ARCH(1) model; GARCH(1,1) model; Estimation and inference of GARCH model
Portfolio Theory (Financial risk of portfolio; Portfolio frontier and efficient portfolios; Statistical analysis of efficient portfolios; Portfolio theory with matrix algebra)
Efficient Portfolios and the CAPM (CAPM Review; Sharpe-Lintner Version; Black Version; Validating the CAPM based on the statistical tests; Cross-sectional regression
Factor Pricing Model (Multifactor pricing model; Applications of multifactor models; Model validation with tradable factors; Selection of factors)
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.
Python reusable functions and modules
Python Plotting libraries (Matplotlib, Plotly; Exporting interactive plots to sharable formats)
Python APIs and API wrappers
Excel Functions (Vlookup, Pivot tables, Concatenate,…)
Building an Excel Dashboard and Publishing to HTML (build interactive filters and sliders into dashboards; appropriate use of graph types; online sharing of interactive data visualizations)
Building Tableau Dashboards
Ways to Share and Integrate Tableau Dashboards (Connect to browser, use starters, .twb files)
Automating Tableau Data Integration for Live Dashboards
Presentation Skills for Graphs and Charts
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.
Python Packages for Financial Forecasting (Pandas, Quandl, Pyfolio, Numpy, Scipy, Statmodels)
Cointegration vs. Correlation (Calculate a hedge ratio; Mean reversion)
Monte Carlo simulation, Grid search, and optimization (Use optimization of parameters to target a desired metric)
Technical analysis / Behavioral Finance (Behavioral Finance Theory behind TA; Fibonacci retracement; How to build momentum indicators, MACD, RSI, 9OC)
Sentiment Data / Combining Sentiment Data with TA (Jake Bernstein’s DSI; sentiment data as a contrarian indicator)
Level II market data (Reading order book data; How to deal with large financial data; Hedging with options)
Identifying Smart/Dumb Money Indicators (Parsing large order book data to create indicators)
Building an Automated trading Algorithm in Python (Walkthrough of an automated trading bot form data collection to signal)
Deploying an Automated trading Algorithm with Python (Using Alpaca API, a timestamped Python trading bot that can be used to demonstrate skill in trading)
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. Use of blockchain and smart contracts to make business processes more efficient through technical and theoretical application.
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. Examination of how forecasts of fundamentals can be used to inform our views on asset prices.
Spring Course Offerings: Applied Electives
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.
National Income Accounting and the Balance of Payment, Global Imbalances: Current account sustainability; sudden stops; international capital flows and development; home bias in international portfolios
International relative prices and exchange-rate pass-through: real exchange rates and economic activity; currency unions and economic adjustment, the dollar and international prices
International production and business cycles
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.
Interest Rates and Term Structure; Macro variables and Interest Rates
Solow Growth Model (balanced growth and transition dynamics; applying model to stock market benchmarks)
Growth accounting: determinants of economic growth
NIPA Accounting using Growth Model
Measuring and projecting potential output
Benchmarks for Stock Price
Efficient Portfolio Theory & CAPM
Equity Risk Premium
Aggregate Asset Pricing Anomalies
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.
Big Data (volume, velocity, variety, voracity)
Machine Learning & Data Mining (assessing model accuracy, logistic regression, Bayesian Decision Theory, Naïve Bayes, regression, gradient descent, LDA, QDA, K-Means, kNN, resampling methods)
Regularization methods (LASSO, Ridge, Elastic Nets), PCA, Partial Least squares, Non-linear methods, association rules, collaborative filtering
Decision trees; Classification Trees; Bagging; Random Forests; SVM; Neural Networks
Economic Methods and applications
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.
Applied Quantitative Methods (Probability, Regression, Optimization, Simulation)
Valuation and Efficient Market Theory (Valuation methodologies and the DCF framework; Efficient market theory)
Empirical Asset Pricing (Capital asset pricing model; Empirical risk factors; “Smart-beta” strategies)
Performance in Competitive Markets (Index vs. active; Performance analysis;
Bond-market risk factors and manager performance; Stock-market risk factors and manager performance)
Alternative Investments (Hedge funds, Private equity, Real Estate)
Portfolio Theory and Practice (Modern portfolio theory; CAPM and I-CAPM;
Liability-relative and goals-based asset allocation; Factor-based asset allocation)
The Optimal Portfolio (Return and risk forecasts; Mean-variance portfolio optimization and the efficient frontier;
I-CAPM portfolio optimization)
Portfolio Management (selection, construction, monitoring)
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. Discussion of recent research on effects of NAFTA and Brexit, effect of trade on inequality in developed and developing countries, and impact of infrastructure investments on trade and development.
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.
Asymptotic Theory: Convergence in probability and in distribution; Law of large numbers and central limit theorem; Continuous mapping theorem, Slutsky lemma, and delta method; Consistency and asymptotic normality
Intermediate-level econometrics: Linear regression; Logistic regression; Instrumental variable regression; Maximum likelihood estimation
High-dimensional linear regression: Regularized estimation; Lasso; Variable Selection; Cross-validation; Related methods
Nonparametric estimation and machine learning: Kernel methods; Nearest neighbor methods; Regression trees; Random forests; Bagging
Neural networks: Constructing neural networks; Training neural networks; Stochastic gradient decent; Back-propagation
Double machine learning: Estimating equations; Neyman orthogonality; Double robustness; Cross-fitting
Program evaluation: Potential outcomes; Conditional unconfoundedness; Average treatment effects; Quantile treatment effects; Local average treatment effects; Diff-in-diff estimation
Machine learning in finance: Asset pricing via machine learning; Text data and asset pricing
Demand estimation with big data: Hedonic model; BLP model
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.
File Formats, Network File Systems
Semi-structured date: JSON, XML
Data modeling: Entity-relationship model; relational data
Constrains and views indexing
Hadoop map-reduce; Big data management
Cloud storage; NoSQL; MongoDB
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.
AWS Intro (EC2 instances, Subnets; Databases, RDS, setting up PostgreSQL and other databases)
AWS lambda Functions (Deploying a Python/R lambda function; Cloud computing architecture and logic)
AWS ML / AI services (Intelligent search, Demand Forecasting, Demand projection, Text analytics)
Power BI Reports (Import data; Creating maps; DAX and M)
Power BI dashboards (Creating interactive visualizations with filters)
Microsoft Azure services overview (Hadoop; Parallel processing; when to use Azure; Potentials of Big Data Analytics software)
Apache Hive (Sending data to Apache databases; HiveQL commands)
Data-mining with Hadoop (Using map reduce for data mining efficiency)
Advanced SQL (Difference in SQL commands across Database services; Subqueries, declarations, null handling)
Alternate programming languages (SPSS, Julia)
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.
Momentum Indicators and Behavioral Finance (Constructing MACD, RSI, Divergence, VWAP; Elliott Wave theory; Dutch Tulip Mania Study)
Index Fund rebalancing (automatically adjusting allocations of Indexes based on price movements)
Contrarian Strategy / Combining Sentiment Data (Jake Bernstein’s DSI; How to use sentiment data as a contrarian indicator)
Working with live data (Warehousing and updating live financial data; High frequency data sources)
How to deploy an algorithmic trading bot (Timestamped Python trading bot; Pine script)
Bloomberg Terminal (Plotting multiple series and exporting data; Integration with Excel)
Smart/Dumb Money Indicators With Options (Parsing high frequency order book data to create indicators)
Level II market data (Reading order book data; Dealing with large financial data)
Alternate Data Sources (Volume Profile; Futures deliveries; Central Bank assets)
Frequently Asked Questions
The MQE is a 48-unit degree program. 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). Students are only admitted in the Fall quarter.
All students are required to take foundational courses in applied statistics and econometrics (Econ 430 and 431) and three quarters of Economists in Action (Econ 410). Beyond this, students select from elective courses offered by the MQE to reach their 48-unit degree requirement.
Yes, students can elect to complete the program in 18-months, which allows students to complete 2 courses per quarter over the course of six quarters. Please note that some courses may take place during the workday so students will need to ensure they have flexibility with their employer to accommodate live classes. Students who choose to complete the program in 9 months (three quarters) must be full-time students.
Yes, the MQE program is STEM Certified (CIP Code 45.0603: Econometrics and Quantitative Economics). To learn more about STEM OPT, please visit USCIS.GOV or UCLA’s Dashew Center.
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. Our unique hands-on curriculum and approach equips graduates with the applied concepts, technical tools, and analytical skills necessary to solve complex business problems facing government agencies, financial institutions, and global corporations.
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 professional portfolio. The capstone project is submitted in the form of a research paper during the final quarter. Satisfactory completion of this project serves as the comprehensive examination.
The final (capstone) project is designed by the student in concert with a faculty advisor and will center on the student’s interest in a particular field of economics. MQE students select their faculty advisor and capstone topic. Many students choose to focus on a topic related to their professional goals.
Here are a few of the past MQE students have explored as part of their Capstone Project.
- Bridging Venture Capital to the Public Market
- Utilizing High Dimensional Data to Analyze the Effect of Uber on Public Transportation Use
- Green vs. Grown: An Analysis of How Nigeria’s Electric Power Sector Reform Act Impacted
Greenhouse Gas Emissions
Starting Fall 2021, MQE students will be eligible to take select PhD courses within The Department of Economics. Courses offered by other units at UCLA are not permitted.
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. Past projects have served clients in cybersecurity, financial services, digital marketing, analytics, sustainability, entertainment, and non-profit fields.
The UCLA Department of Economics offers undergraduate majors in Economics and Business Economics, a PhD program, and the Master of Quantitative Economics program. PhD students earn a Master of Arts along the way, but students cannot be directly admitted into that Master of Arts program.
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. Most PhD programs are focused on advanced economics and research training.