Curriculum

The Master Of Science In Economics Programme Offers Three Concentrations To Satisfy Diverse Professional Interests And Career Goals

Curriculum

The Master of Science in Economics is a full-time Programme that offers three Concentrations: Macro-Finance, Economic Data Science, and Economic Science. Students are required to complete 4 required core courses, 3 required concentration courses and 5 elective courses within the prescribed academic period. Students graduate after completion of these 12 professional courses (36 credits), and the Civic Education Course requirement*, while meeting a cumulative grade point average (CGPA) of 2.0 or above.

*Civic Education Courses (not applicable to international students) are noncredit and include required courses and elective courses, subject to actual course offerings.

Macro-Finance Concentration

The Macro Finance concentration builds a thorough and in-depth understanding of macroeconomics and financial markets. Students master key skills such as macro data analysis, quantitative modeling, risk management, and investment strategies. Focus is on building core competencies in many facets of Economics, to address diverse challenges such as assessment of macro and government policy impacts on financial markets, strategic plan formulation, macroeconomic forecasting and authoring industry research reports. This concentration is particularly suitable for careers in macro financial analysis, investment management, risk control, policy research, and strategic planning.

Economic Data Science Concentration

The Economic Data Science concentration develops professional skills in the field of digital economy. Students master advanced data science techniques to address the complex challenges facing the economic and business fields in today’s digital age, and provide intelligent solutions. This track equips students with the skills and knowledge to lay a solid foundation for future roles in the data-driven economy. This track is particularly suitable for careers in quantitative investment, market analysis, financial technology, business intelligence, and data analysis.

Economic Science Concentration

The Economic Science concentration is tailored for students planning advanced studies by helping them explore their interests in economics and business research. It provides a more in-depth treatments across a broad range of economic theories and emphasizes development of solid academic research abilities. Students have access to advanced doctoral courses, helping to lay a solid theoretical foundation for their future academic research. This concentration is particularly suited for students planning to pursue a Ph.D. in economics or related business disciplines.  


Pre-term Courses (No Credits)

Mathematics for Economics
Introduction to Economics
Programming for Economics

Required Core Courses

Macro-Finance Concentration
Economic Data Science Concentration
Advanced Microeconomics

This course provides an exposition of advanced microeconomic theory. The course covers the classical theories of consumer and producer behaviour. Topics include preference and utility representation, existence and properties of demands, expenditure functions, indirect utility, welfare evaluation, revealed preference, production sets, profit maximization, cost minimization, and duality. The course also briefly introduces a number of topics such as game theory, general equilibrium theory, and the economics of uncertainty and information.

Advanced Econometrics

This course is an introductory course in econometrics designed for the MSc program. It covers model specifications, estimation methods and causal inference, emphasizing empirical skills through learning-by-doing. The Python computer program will be used throughout the course. 

Advanced Macroeconomics

This course is an advanced course on conteporary macroeconomic theories.It emphasizes the application of recent theoretical analysis on current macroeconomic issues of both economic fluctuations and growth. It briefly covers topics such as business cycles, economic growth and development, monetary economics and public finance.

Machine Learning in Business and Economics

The course provides a broad introduction to modern machine learning algorithms with a foundational understanding of how these algorithms work in business and economics settings. Topics covered include: supervised/unsupervised learning, linear/nonlinear regression, classification, logistic regression, discriminant analysis, model selection, cross-validation, regularization, dimension reduction, tree-based methods, neural networks, etc. The emphasis will be placed on applications in economics and business data. The course also teaches how to implement these methods in R. 

Economic Science Concentration
Microeconomic Theory I

The aim of this course is to introduce students to some of the canonical results in microeconomic theory. This provides students with a solid basis for further research and give students examples of how economic problems could be formulated and analyzed in mathematical models. Topics include consumer theory, producer theory, choice under uncertainty, and general equilibrium theory.

Macroeconomic Theory I

This course is the first course of the one-year sequence covers core materials of entry level graduate macroeconomics. This course will cover materials of modern growth theories, business cycles and asset pricing. It will equip students with the ability to understand the economic growth, fluctuations and asset prices. Students will learn to evaluate the policies through the lens of modern economic theories.

Econometrics I

This is the first advanced course in econometrics for first year PhD students in economics. The objective of the course is to provide students with rigorous training in probability theory, statistic, and basic econometric theory. Topics covered include: basic concepts in probability and statistics, the law of large numbers, the central limit theorem, ordinary and generalized least squares, asymptotic approximations, two-stage least squares, generalized method of moments. In this course, students will work on problem sets and simulation studies to enhance their understandings of materials.

Machine Learning in Business and Economics

The course provides a broad introduction to modern machine learning algorithms with a foundational understanding of how these algorithms work in business and economics settings. Topics covered include: supervised/unsupervised learning, linear/nonlinear regression, classification, logistic regression, discriminant analysis, model selection, cross-validation, regularization, dimension reduction, tree-based methods, neural networks, etc. The emphasis will be placed on applications in economics and business data. The course also teaches how to implement these methods in R. 

Required Concentration Courses

Macro-Finance Concentration
International Finance

This is a graduate-level international finance course. It aims to provide students with a solid understanding of the modern theories and empirics of international finance. Topics covered include balance of payments and current account, exchange rate determination, the Mundell-Fleming model of output and exchange rate determination under fixed and flexible exchange rates, international financial systems, capital controls and cross-border capital flows, international policy spillover and coordination, and Chinese economy in the context of financial globalization.

Advanced Monetary Economics andFinancial Markets

This course introduces the functions of financial markets and how monetary policy impacts the economy through financial markets. The course topics generally include: why people use financial markets, national income accounts, interest rate and central bank policy, banking, and international capital flows.

China's Economic Policies and Their Effects

The course is designed to bridge the gap between academic research and China’s economic developments with a standard analytical framework. It starts with the economic fundamentals and its indicators, then moves on to policy analysis and impacts. It will help students understand how to study the Chinese economy in an international/cross-country content, and how fiscal/financial/industrial policies affect the economic outcome. The course includes two parts: (1) the lecture part will discuss the analytical framework with different components of the economy, as well as the policy analysis with case studies; (2) the students will form small study groups with each group working on a research paper with a final presentation in the class. The course will be conducted in Chinese.

Economic Data Science Concentration
Data Mining for Economics

The course explores different aspects of data mining, from the fundamentals to the complex data types and their applications, capturing the vast diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. We will cover (1) Basic mining methods for clustering, classification, association pattern mining, and outlier analysis; (2) Specific methods for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data; and (3) Important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. 

Machine Learning in Finance

This course discusses some basic algorithms in classic machine learning, including supervised learning and unsupervised learning, and reinforcement learning, and introduces applications of these algorithms in financial settings, like option pricing, loan default prediction, grouping investors, and learning-based trading strategy.

Machine Learning for Text

Text analytics has become an important tool in business and economics. It is a field that lies on the interface of information retrieval, machine learning, and natural language processing. This course carefully covers a coherently organized framework drawn from these intersecting topics. We will start with basic algorithms of machine learning from text. We then discuss the learning methods from text via advanced technique from NLP. Finally, we cover hot topics in NLP such as LLMs, AI agents, etc.

Economic Science Concentration
Microeconomic Theory II

This course is the second course for the 1st year sequence of microeconomics study for Ph.D. students. Topics covered include game theory, contract theory and mechanism design, and applications of the theories in related fields such as industrial organization and network economics.

Econometrics II

This is a sequel to ECON 6231, delving into more advanced concepts within econometric theory. Our exploration will encompass a comprehensive understanding of discrete choice models, non-parametric and semi-parametric methods, panel data analysis, and the intricacies of causal inference. Time permitting, we will also delve into the dynamic realm of machine learning in the context of econometrics. Through this sequel, students will navigate the nuanced landscape of advanced econometric topics, building on the foundational knowledge acquired in the preceding course.

Macroeconomic Theory II

This course is designed to provide the students with basic analytical tools for macroeconomic analysis.

Elective Courses

Contemporary lssues in Chinese Financial Development (Practitioner Workshops)

The course aims to broaden students’ perspectives on many contemporary issues in Chinese financial development. Practitioner speakers will work with students on both classroom case presentations and projects. By offering students opportunities of integrating theories with existing finance practices, the course helps students gain practical insights into the latest trends of corporate finance and investment management in China.

Introduction to Chinese Economy

This course helps students to understand how the market-oriented economic reform and opening since 1978 have transformed China from a closed, centrally planned, and under-developed economy into the second largest economy in the world through rapid industrialization, urbanization, internationalization, and digitalization. It examines how the role of the state and the market, the global environment, and the technology are shaping the evolving growth model of China, and the implications on future challenges. In addition to a review of historical achievements, the course will discuss a broad range of economic, social, environmental, and geopolitical challenges as China strives to build a more open, innovative, inclusive, and sustainable growth model for the peace and prosperity of China and the World.

Big Data for Business and Economics

The ability to handle large scale data set has become an essential job market skill. Those who masters big data analytics have become very popular in current job market. The quarter of this course is to equip students majored in Information System basic knowledge of how large IT firms operates in the backend, such as Distributed File System, Hadoop ecosystem, Relational DB and NoSQL DBMS, and Spark. 
The second quarter will be devoted to advanced deep learning techniques, such as MLP, CNN and CV, RNN etc. We will also talk about two important application of Deep learning: AIGC and Graph Neural Network.
The third quarter will be about a new learning paradigms of today’s ML and AI, which is Reinforcement Learning.
In addition, four to five lab sessions will be included (about half of them are optional) to cover the following element: Advanced Python programming and Data Analytics in Python; Programming in Spark; Deep Neural Network with TensorFlow; Reinforcement Learning. 

Applied Econometrics

This course provides a unified framework to study the properties of popular econometric methods used in economic analysis such as least-squares, maximum likelihood and generalized method of moments estimators. Topics in this class include the applications of these popular econometric methods to cross-sectional data and time series data.

Financial Economics

This course is an introduction to investments and asset pricing. It provides an overview of financial markets and instruments including stocks, bonds, futures, options, and other derivatives. The no-arbitrage principle is used to price bonds and derivatives. A risk-return framework is then used to understand optimal asset allocation and equilibrium pricing of securities. Topics covered in this course include classic topics such as binomial-tree and Black-Scholes models, Capital Asset Pricing Model, two-fund separation theorem, and recent behavior models that explore the limits of arbitrage if time permitted.

Behavioral Economics

This class covers recent topics in behavioral economics, with a focus on empirical applications using tools including non-experimental data analysis, lab experiment, and field experiment. This is a fundamental research field, related to almost all areas of existing economics. Our topics include deviations from the standard neoclassical model in terms of (i) preferences (risk, time, and social preferences), (ii) beliefs and learning (overconfidence, projection bias, and attribution bias), and (iii) decision-making (cognition, attention, and framing). Applications will cover a wide range of fields, including labor economics, development economics, public economics, health economics, finance, and environmental economics. 

Public Economics and Finance

This course discusses the theory and practice of public finance, focusing on the quantitative effects of public policies. There are two parts in this course: government spending and government revenue (taxes). The course emphasizes the experience of United States public finance; students will be trained to apply sophisticated cross-section and panel data to analyze real-life economic issues and SAS/STATA programming techniques will be taught extensively throughout the course.

Entrepreneurial Finance and Economics

The course examines key elements of entrepreneurial finance and economics, focusing on technology-based start-up ventures and the early stages of company development. It includes topics on: (1) valuation, which covers techniques to forecast financials, to assess financial needs, and to value new ventures; (2) venture capital and private equity, which focuses on structure of private equity markets and contracts in private equity deals; (3) capital structure of entrepreneurial ventures, which covers financing alternatives and their pros and cons; (4) harvesting, discussing methods of cashing out from a venture and their pros and cons. The course helps students gain insights into real business issues, focusing on how to make good judgements in new environments characterized by high degrees of uncertainty.

Directed Research in Economics

This course prepares students to engage in productive and original research in the broad area of economics under the instruction of leading professors.

Graduate Internship

This course aims to provide students with professional skills and real-world experiences. Internships in Economics will be structured to help students combine in-class theories with real-world applications in business, government, consulting and research, as well as gain the skills and experiences that are necessary to prepare for a successful career. Upon completion of the internship, students are required to submit written reports that describe what they have learned from the internship experience and how they apply theories to address practical problems encountered during the internship period. Students are also required to meet with faculty sponsors regularly (every two weeks) via conference calls to report progress and receive feedback. All meeting details and comments should be documented as the supporting materials required by the course. 

*The course offerings are subject to change and not all listed elective courses will be offered in each academic year.