Analyzing Panel Data

About

Analysts increasingly find themselves presented with data that vary both over cross-sectional units and across time. Such panel data provides unique and valuable opportunities to address substantive questions in the economic, social, and behavioral sciences. This course will begin with a discussion of the relevant dimensions of variation in such data, and discuss some of the challenges and opportunities that such data provide. It will then progress to linear models for one-way unit effects (fixed, between, and random), models for complex panel error structures, dynamic panel models, nonlinear models for discrete dependent variables, and models that leverage panel data to make causal inferences in observational contexts. Students will learn the statistical theory behind the various models, details about estimation and inference, and techniques for the visualization and substantive interpretation of their statistical results. Students will also develop statistical software skills for fitting and interpreting the models in question, and will use the models in both simulated and real data applications. Students will leave the course with a thorough understanding of both the theoretical and practical aspects of conducting analyses of panel data

Course leader

Christopher Zorn from Pennsylvania State University, United States

Target group

Prerequisites (knowledge of topic)
Comfortable familiarity with univariate differential and integral calculus, basic probability theory, and linear algebra is required. Students should have completed Ph.D.-level courses in introductory statistics, and in linear and generalized linear regression models (including logistic regression, etc.), up to the level of Regression III. Familiarity with discrete and continuous univariate probability distributions will be helpful

Course aim

Students will learn how to visualize, analyze, and conduct diagnostics on models for observational data that has both cross-sectional and temporal variation

Fee info