Less than 100 days left of the 2020 Summer Program!
We're almost through the second week of the program! Considering that just two months ago we announced our move to online-only instruction, we couldn't be happier with the way our workshops have run so far. In case you missed our announcement last week, we have a lot of information about how online courses function with recording, platform, and time zones posted on our homepage. Today though we're looking into the future a bit with these upcoming workshops.
Dynamical Systems Analysis (June 15-18)
Instructors: Brian Baucom, University of Utah; Jonathan Butner, University of Utah
Dynamic systems models provide a unique lens for studying change over time; the focus in these models is on patterns of fluctuations about the mean, the stability of these patterns, and how these patterns change under different conditions. Research focused on how individuals, families, groups, and communities grow and change over time is increasingly recognizing the vast potential of dynamic systems models for advancing understanding of complex temporal phenomena.
This four day short course includes a balance of didactic instruction in background material and guided practice in estimating and interpreting models.
Bayesian Multilevel Models (June 15-19)
Instructor: Ryan Bakker, University of Essex
Bayesian methods allow for an extremely flexible approach for estimating hierarchical models with a variety different types of dependent variables. The Bayesian approach simplifies several of the assumptions of the classical techniques for MLMS and directly estimates a variety of quantities of interest that require post-estimation methods in the non-Bayesian framework. Topics covered will be the hierarchical linear model, as well as a models with limited dependent variables, summarizing results, in and out of sample predictions, and measures of model fit. No prior knowledge of Bayesian modeling is required, but will be beneficial.
Advanced Topics in Dynamic Panel Models (July 20-22)
Instructors: Mark Pickup, Simon Fraser University; Andrew Philips, University of Colorado at Boulder
Data collected over both units (e.g., individuals, states, countries) and time (e.g., days, months, years) — known as time series cross-sectional data or panel data — are common in the social sciences. By gaining leverage both across units and over time, these data help us answer important questions that would be difficult if we only looked at a single point in time (e.g., cross section) or single unit (e.g., time series): the relationship between growth and democracy, whether or not the resource curse exists, or how economic perceptions shape support for the government. Despite these advantages, panel data often show types of heterogeneity and dynamics that make standard regression approaches inappropriate.
This course is designed to survey some advanced topics in panel data. After a review of panel data fundamentals, we will cover topics such as panel unit root and cointegration tests, panel error correction models, and approaches to modeling dynamics in panel data with a small T.