Dynamic Models for Policy, Economics, and Society: Practical Time Series Methods


  • Harold D. Clarke, University of Texas at Dallas

This is a course on time series methods for analyzing economic, political, and social data. The course has an applied focus and participants will learn how to specify, estimate, and evaluate multivariate time series models 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, GARCH, VAR/VECM, and State Space models. 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/selection techniques, and post-estimation diagnostic procedures. The course teaches participants how to use major software packages such as EVIEWS, R, RATS, and STATA to analyze time series models in their field of interest. Students are invited to bring their own data sets to analyze in daily lab sessions.

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, SPSS, or STATA is helpful, but not required.

Fee: Members = $1500; Non-members = $3000

Tags: time series, dynamic models

Course Sections

Section 1

Location: ICPSR -- Ann Arbor, MI

Date(s): July 20 - July 24

Time: 9:00 AM - 5:00 PM


  • Harold D. Clarke, University of Texas at Dallas


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