Advanced Time Series Cross-Section Analyses

Andrew PhilipsUniversity of Colorado at Boulder and Lorena Barberia, University of São Paulo


Data collected over both units (e.g., municipalities, states, countries) and 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. In this week, building off Module I’s Essentials of Time Series for Time Series Cross-Section Analyses (TSCS) and Module II’s Fundamentals of Time Series Cross-Section Analyses, we cover several advanced topics regarding these data. This includes a focus on establishing identification, model selection testing procedures, as well as more advanced estimation methods, such as GMM and SUR models.

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. On the fifth day, students will work on a specific project assignment that applies the concepts introduced in the course.  


This course runs January 28 - February 1,2019.

TEACHING FELLOW: Yoo Sun Jung, Texas A&M University


Topic 1:  Modeling Heterogeneity: Intercepts and Slopes

For the first topic, we will focus on reviewing the different pooling assumptions for models of cross-sectional time series data and how these effect the interpretation of our hypotheses tests.   We will review pooled time series models commonly used by applied researchers including random and fixed effects models, and panel corrected standard context.

Topic 2: Assessing and Presenting Robustness in Models of TSCS

For the second topic, we will explore how to examine robustness by exploring the magnitude, size and statistical significance of findings from TSCS models. We will introduce graphical simulations that allow researchers to see long-term relationships between key variables and allow for examination of the impact of exogenous and/or endogenous shocks.

Topic 3: Modeling Dynamics with GMM Estimators

For the third topic, we will introduce the one and two-step generalized method of moments (GMM) estimators for dynamic panels, which have become increasingly popular. We will show how these models handle the endogeneity of regressors and unit fixed effects, as well as discuss some of the potential pitfalls that should be avoided in estimation.

Topic 4:  Multiple Equation Models: Seemingly Unrelated Regressions (SUR)

For the fourth topic, we will introduce SUR (Seemingly Unrelated Regressions) models where we have two or more equations (one for each cross-sectional units) whose errors are correlated. This modeling strategy is appropriate for testing the pooling assumptions that we make in models of TSCS data but does not work well for models that include variables that have little or no within variation.

Topic 5:  Student Presentations

For the last topic, we will have student presentations of your research project you have developed over the week. Everyone will provide feedback. If needed, we will also finish up any lectures.


A full-semester graduate-level course in multiple regression analysis and Essentials of TS for TSCS and Fundamentals of Time Series Cross-Section Analyses (offered in the IPSA-USP 2019 Summer School) or the equivalent background in time series and time series cross-section (TSCS) analysis.