STAT 4342 Time Series Analysis
Topics include: identification of models for empirical data collected over time; use of models in forecasting. Students should understand the differences between
cross-sections and time series, and those specific problems, which occur while working with data of these types. In this course, students should master traditional
methods of time series analysis of univariate time series data including: autoregressive and moving average models (denoted as ARIMA models); smoothing methods and trend/seasonal decomposition methods; longitudinal analysis and repeated measures models for comparing treatments when the response is a time series; and intervention analysis (before/after analysis of a time series to assess effect of a new policy, treatment, etc.)