Advanced Time Series Cross-Section Analyses
This course runs January 27 - 31, 2020.
Topic 1: Modeling Heterogeneity: Slopes
For the first topic, we will focus on testing various pooling assumptions about our coefficients of interest. We will introduce SUR (Seemingly Unrelated Regressions) models, which 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-unit variation. We will also discuss models that incorporate random slopes, another way to relax the assumption of a fixed effect across units.
Topic 2: The Mundlak Transformation and Missing Data
For the second topic, we will discuss the Mundlak transformation, an additional model to explore heterogeneity in effects between units, as well as within units. We will also discuss how to impute missing data, a common issue when working with TSCS data.
Topic 3: Models for Dichotomous Dependent Variables in TSCS
Topic 4: Modeling Dynamics with GMM Estimators
For the fourth 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 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 2020 Summer School) or the equivalent background in time series and time series cross-section (TSCS) analysis.