Guy Whitten, Texas A&M University
Data collected both across units (e.g., municipalities, states, countries) and over time (e.g. days, months, years)—known as pooled time series—are common in social science. By gaining leverage both across units and over time, this data structure helps us answer important questions that would be difficult if we only looked at a single year (e.g., cross section) or single country (e.g., time series): the relationship between growth and democracy, whether or not the resource curse exists, and how institutions shape political and economic outcomes.
However, pooled time series often show types of heterogeneity that make standard regression approaches inappropriate. We will explore why we might want to use pooled time series data, what issues might arise when using such data, and how we can test for violations of assumptions and account for them. We discuss the fundamentals behind pooling data, such as accounting for heterogeneity and temporal dependence. We will also discuss issues relating to making causal inferences. During the first four days, the course will involve about three hours of lecture time with breaks, then lunch, and then three to four hours of hands-on instruction in analysis that takes place in smaller groups using Stata and R. On the fifth day, students will present their projects that apply the concepts introduced in the course.
A full-semester graduate-level course in multiple regression analysis or the equivalent background in time series analysis.