Theoretically develops extensions to the standard econometric toolbox, studies their application in scientific research, and applies them to data. Emphasis is on using techniques, and on understanding and critically assessing others’ use of them. Requires practical work on the computer using a range of data from around the world. Topics include advanced regression analysis, limited dependent variables, nonparametric methods, and new methods for causal inference.

Survey of theoretical and practical aspects of statistical and economic forecasting. Such topics as long-run and short-run horizons, leading indicator analysis, econometric models, technological and population forecasts, forecast evaluation, and the use of forecasts for public policy.

Decision making when the consequences are uncertain. Decision trees, payoff tables, decision criteria, expected utility theory, risk aversion, sample information.

Financial market functions, institutions and instruments: stocks, bonds, cash instruments, derivatives (options), etc. Discussion of no-arbitrage arguments, as well as investors’ portfolio decisions and the basic risk-return trade-off established in market equilibrium.

Introduces the firm’s capital budgeting decision, including methods for evaluation and ranking of investment projects, the firm’s choice of capital structure, dividend policy decisions, corporate taxes, mergers and acquisitions.

Linear and integer programming, elements of zero-sum, two-person game theory, and specific combinatorial algorithms. Nonlinear programming, deterministic and stochastic dynamic programming, queuing theory, search models, and inventory models.

Probability and statistics used in economics. Probability and sampling theory, statistical inference, and use of spreadsheets.

Basic econometric methods, including the linear regression, hypothesis testing, quantifying uncertainty using confidence intervals, and distinguishing correlation from causality.

Advanced econometric methods: estimation of linear regression models with endogeneity, economic methods designed for panel data sets, estimation of discrete choice models, time series analysis, and estimation in the presence of autocorrelated and heteroskedastic errors.

Economic analysis of household determination of the demand for goods and services, consumption/saving decisions, and the supply of labor.

Analysis of firms’ production and costs, the supply of output and demand factors of production. Analysis of perfectly competitive markets.

Analysis of the effects of imperfect market structure, strategy, and imperfect information.

Honors sequence expanding on the material taught in ECON 120A. Major GPA of 3.5 or better required.

Honors sequence expanding on the material taught in ECON 120B. Major GPA of 3.5 or better required.

Senior essay seminar for students with superior records in department majors.

Senior essay seminar for students with superior records in department majors.

This introductory course develops computational thinking and tools necessary to answer questions that arise from large-scale datasets. This course emphasizes an end-to-end approach to data science, introducing programming techniques in Python that cover data processing, modeling, and analysis.

Provides an understanding of the structures that underlie the programs, algorithms, and languages used in data science by expanding the repertoire of computational concepts introduced in DSC 10 and exposing students to techniques of abstraction. Course will be taught in Python and will cover topics including recursion, higher-order functions, function composition, object-oriented programming, interpreters, classes, and simple data structures such as arrays, lists, and linked lists.