Spatial and Temporal Methods Workshop Spring 2026

Mondays from 9:30-11:00 via Zoom: Feb 2, Feb 9, Feb 16, Feb 23, Mar 2 

 

Registration: Contact instructors Andrew Philips to register for this non-credit workshop, receive the zoom link, and join the email list.

 

Course Description: In this course we investigate how to model time-dependent and spatially-dependent processes related to social science questions. Students dealing with time series, cross-sectional data, and/or spatio-temporal/panel/longitudinal data will find this workshop useful. This course places a healthy emphasis on implementing these models using statistical software like Stata and R, as well as interpreting results. By the end of this workshop you should 1) understand various issues that can arise when working with spatio-temporal data, 2) model spatio-temporal data using a variety of approaches, 3) incorporate these approaches to your own work. Prerequisites: none, although a basic regression course (e.g., introductory OLS) is highly recommended.

 

Topic I: Introduction to time series. Topics covered include stationarity/non-stationarity, unit roots and autoregressive distributive lag models.

Topic II: Introduction to spatial data. Topics covered include spatial dependence, types of dependence, and tests for associations.

Topic III: Panel data. Topics covered include diagnostic tests, random and fixed effects modeling, and adding dynamics

Topic IV: Advanced spatial analysis. Topics covered include spatial [temporal] autoregressive models, spatial OLS vs ML vs 2SLS, calculating and interpreting spatial effects and diffusion

Topic V: Bringing it all together. Modeling spatio-temporal processes, interpretation and final topics