Dynamic Models for Policy, Economics, and Society: Time Series Methods
This course discusses time series statistical methods for analyzing economic, political and social data. The course has an applied focus and participants will learn how to undertake multivariate time series analyses in their substantive fields of interest. Methods considered will be helpful to graduate students and faculty in the social sciences as well as policy analysts and other researchers working in the public and private sectors. Several important time series models are considered. These include ARIMA, ARFIMA, Error Correction, Fractional Error Correction, ARCH, GARCH, Dynamic Conditional Correlations and State Space representations. Key concepts such as (non)stationarity, exogeneity and Granger Causality are introduced at the beginning of the course and used to inform model specification, model comparison techniques, and post-estimation diagnostic procedures. The course provides working knowledge of major software packages such as EVIEWS, R, RATS and STATA to analyze various time series models. Students are invited to bring their own data sets for analyses in daily lab sessions.
Course Prerequisites: Participants should have taken an introductory course in applied multiple regression analysis, and be familiar with the standard Windows computing environment. Basic knowledge of a major statistical software package such as R or STATA is helpful, but not required.
Fee: Members = $1500; Non-members = $3000