Data Analytics and Computation 790D - Temporal Dynamics

This course provides an introduction to statistical methods for analyzing temporal data, including time-series analysis, panel data modeling, and event history analysis. It equips students with the skills to model and interpret data that evolves over time and includes techniques for causal inference in temporal settings. Students will begin with foundational methods in time-series analysis, including descriptive tools, autoregressive integrated moving average (ARIMA) models, and autoregressive distributed lag (ADL) models. Key topics include stationarity, autocorrelation, unit root tests, intervention analysis, and spectral analysis. Advanced methods such as volatility modeling (ARCH/GARCH) and causal inference techniques will also be introduced. The course then transitions to panel data analysis, focusing on mixed-effects models and regression techniques for handling repeated measures. The final part of the course covers event history analysis, where students will learn statistical techniques for modeling time-to-event data, including discrete and continuous time models, duration dependence, time-varying covariates, competing risks, and repeated events. Throughout the semester, students will apply these methods to real-world datasets using R, emphasizing practical implementation and interpretation. By the end of the course, students will have a comprehensive toolkit for analyzing temporal data and conducting causal inference across a variety of social science disciplines, such as political science, public policy, and economics.