Graduate Studies-Economics (GSE)

Advanced study planned and completed under the direction of a departmental faculty member. Open only to graduate students demonstrating ability to do independent work. Enrollment by petition.

Review and discussion of the basic math tools needed for graduate work in economics, including set theory, linear algebra, properties of functions, static and dynamic optimization. 4 lectures.

Preferences and choice, preferences over commodities, consumer demand theory, producer theory, choice under uncertainty, simultaneous and sequential move games, incomplete information games, mechanism and incentive design. 4 lectures.

Finite Markov chains, linear state space models, dynamic programming, rational expectations equlibrium, Markov perfect equilibrium, Stackelberg plans, general equilibrium under certainty and uncertainty, Arrow securities, consumption-based asset pricing, incomplete markets. 4 lectures.

Statistical concepts for use in theoretical and applied econometric applications including random variables, independence, expectations, probability, distributions, covariance and correlation, large sample theory, and properties of estimators. 4 lectures.

Identification and estimation of linear and nonlinear regression models for analyzing business data. Topics include multiple linear regression; model selection; robust standard errors; instrumental variables; maximum likelihood estimation; logit/probit, ordered logit/probit, and other microeconometric models. Course may be offered in classroom-based, online, or hybrid format. 4 lectures.

The linear regression model. Confidence and prediction intervals. Hypothesis testing. The generalized regression model and heteroscedasticity. Identification and causal inference: randomization, regression, instrumental variables, regression discontinuity, differences in differences. 4 lectures.

Maximum likelihood estimation. Binary, multinomial, and ordered discrete response models. Truncated, censored regression. Structural equation modeling. Factor models, filtering and Bayes rule. Random utility and mixed logit models. Demand estimation. 4 lectures.

Use of computers for advanced data analysis in economics and analytics. Topics include computer programming using statistical software, data gathering and cleaning, and machine learning. 4 lectures.

Potential outcomes framework and causal treatment effects. Unconfoundedness designs, including matching and propensity score methods. Selection on unobservable designs. Quantile regressions. The econometrics of randomized experiments. 4 lectures.

Economic analysis of pollution, congestion, public good provision, and natural resource conservation. Static and dynamic efficiency, economic growth and sustainability, pollution taxes, marketable permits, and the design of market-based regulations. 4 lectures.

Analysis of the international movement of goods, services, capital and payments. The role of exchange rates, tariffs, quotas, and transport costs. Relationship between international trade and economic growth. 4 lectures.

Economic analysis of the rationale for public expenditure and taxation. Externalities, pollution and public policy, income redistribution and public welfare, public goods, collective choice and political institutions, public budgeting techniques and cost-benefit analysis, taxation and tax policy, state-local finance and fiscal federalism. 4 lectures.

Economic theories of industrial organization with specific reference to such topics as cartels, market concentration and performance, vertical integration, franchise contracts, ownership and control of firms, multipart and discriminatory pricing, and tie-in sales. Economic aspects of antitrust law and government regulation of industry. 4 lectures.

Research methods in labor economics and application of modern empirical techniques to the analysis of labor markets. Topics include labor supply and demand, discrimination, migration, and human capital accumulation. 4 lectures.

Representing uncertainty using discrete and continuous conditional probabilities. Monte Carlo simulation of independent and correlated random variables. Optimization of decision variables. Randomization in program evaluation. Model mis-specification. Visualization and representation of the results of a decision analysis. Case studies. 4 lectures.

Ascending, first-price, second-price, and double auctions. Revenue equivalence, multi-unit auctions, the Vickrey-Clarke-Groves mechanism, and matching markets. The deferred acceptance algorithm, the immediate acceptance algorithm, and the many-to-one matching model. 4 lectures.

Regularization, model selection, and supervised and unsupervised learning. Post model selection inference for causal effects. Double/debiased machine learning, causal trees, casual forests, and synthetic controls. 4 lectures.

Directed group study of selected topics for graduate students. Open to undergraduate and graduate students. The Class Schedule will list topic selected. Total credit limited to 8 units. 1 to 4 lectures.

Advanced topics in economics chosen according to the common interests and needs of the students enrolled. The Class Schedule will list topic selected. 1 to 4 seminars. Total credit limited to 5 units.

Individual research under the general supervision of the faculty, leading to a graduate thesis of suitable quality. Minimum of 8 units required for degree.