THE PROGRAM

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

Introduction to Statistical Methods & Econometrics

Introduction to probability, statistics, econometrics, and time-series methods used in economics, business, and government. Topics include random variables, hypothesis testing, estimation, distribution functions, simple and multiple regression, and estimation with stationary/nonstationary processes.

Macroeconomic Theory

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.

Microeconomic Theory

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.

Writing and Presentation Skills for Economists

Designed to help students develop communication and presentation skills essential for success in any aspect of business. Practice in writing economics documents for variety of professional audiences. Writing taught as process — brainstorming, collaborating, continually revising, and challenging ideas. Presentation skills to focus on presenting information clearly and organizing ideas, with emphasis on role of audience when presenting, because audience determines diction, style, tone, organization, research, and ideas. Grammar incorporated as needed, especially in regard to writing.

WINTER QUARTER – APPLIED ECONOMICS
Four required courses

Applied Econometrics

Basic tools necessary for high-level cutting-edge empirical research. Coverage of variety of methods suited for empirical studies that apply to experimental data, quasi-experimental data, panel data, and cross-sectional data.

Incentives, Information and Markets

Lecture, three hours; discussion, one hour. Limited to Master of Applied Economics students. 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. Letter grading.

International Economics

Lecture, three hours; discussion, one hour. Limited to Master of Applied Economics students. 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. Letter grading.

Machine Learning I

The learning outcomes of this class include the following aspects. i) Students will be able to obtain an understanding of the fundamental machine learning algorithms, concepts and techniques; ii) Students will be able to implement a set of widely used machine learning methods, and solve real learning problems with moderate challenges; iii) Students will be able to design a robust machine learning system, perform efficiency and complexity analysis, and improve system performance by diagnosing system bottlenecks.

SPRING QUARTER – ELECTIVES
Choice of three elective courses 

Data Analytics and Big Data

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.

Exchange Rate Forecasting, Big Data and Portfolio Design

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

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

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

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.

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.