This seminar is designed to help graduate students in economics cultivate ethical research practices they may apply in future work at or beyond the University. Students are encouraged to discuss concerns that may arise during the conduct of their research with experienced faculty and devise solutions for dealing with these concerns. The course provides necessary training for newly mandated RCR training for graduate students supported by government grants, and is required for successful completion of the program.

This course begins with extensions of the linear model in several directions: (1) pre-determined but not exogenous regressors; (2) heteroskedasticity and serial correlation; (3) classical GLS; (4) instrumental variables and generalized method of movements estimators. Applications include simultaneous equation models, VARS and panel data. Estimation and inference in non-linear models are discussed. Applications include nonlinear least squares, discrete dependent variables (probit, logit, etc.), problems of censoring, truncation and sample selection, and models for duration data.

The course surveys both the theoretical literature and the relevant empirical methods and results in selected current research topics in labor economics.

This course builds upon POL 571 and introduces students to applied regression analysis in cross-section settings. It begins with the basic principles of causal inference, and then covers various statistical techniques including linear regression, instrumental variables, structural equation models, instrumental variables, and structural equation models. The materials are taught at the level of Hansen's Econometrics and Imbens and Rubin's Causal Inference.

This course covers a range of advanced topics in statistical learning that are useful for empirical research in political science. These may include dimension reduction and regularized regression for large datasets; scaling models; models for text, audio, and image data; and spatial statistics among other topics. The course focuses in particular on estimation and inference to enable students to adapt and extend existing approaches.

This is a course on research methods for sociology PhD students. The seminar has four objectives: 1) to review foundational principles of research design and contemporary debates in sociological methodology; 2) to introduce students to the practice of different research methods (e.g., survey research, experiments, ethnography, historical and comparative analysis) while considering the strengths and limitations of various approaches; 3) to familiarize students with the components of a strong empirical paper and prepare them to identify a topic and data for their empirical paper; and 4) to train students in the conduct of responsible research.

Introduces theories of inference underlying most statistical methods and how new approaches are developed. The first half of the course covers maximum likelihood estimation and generalized linear models. The second half covers a number of topics useful for applied work including missing data, matching for causal inference and, others. The course concludes with a project replicating and extending a piece of work in the scholarly literature.

Preparation of quantitative research papers based on field experiments, laboratory experiments, survey procedures, and secondary analysis of existing data banks.

This course introduces central topics, questions, and methods in contemporary family sociology (family demography). We focus on growing family complexity, the mechanisms underlying this trend, and implications for inequality within and across generations. Readings and discussion emphasize relationships between work and family and the changing nature of employment as well as changing attitudes and behavior related to the gender division of labor within families. The six-week session concludes with a session on family behavior and family relationships across the life course and within the context of population aging.