Harold D. Clarke is Ashbel Smith Professor in the School of Economic, Political and Policy Sciences, University of Texas at Dallas, and adjunct Professor, Department of Government, University of Essex. His research interests focus on the political economy of electoral choice and party support. He has published widely on this topic in journals such as the American Journal of Political Science, American Political Science Review, and British Journal of Political Science. He has been a principal investigator for the 2001, 2005 and 2010 British Election Study (University of Essex and University of Texas at Dallas), the 2011 Political Support in Canada Study, the 2012 Political Support in America Study and the 2020 Cometrends surveys at the University of Texas at Dallas. His most recent books are Absent Mandate—Strategies and Choices in Canadian Elections (University of Toronto Press), Brexit—Why Britain Voted to Leave the European Union (Cambridge University Press, 2017), Affluence, Austerity and Electoral Change in Britain (Cambridge University Press, 2013), and Austerity and Political Choice in Britain (Palgrave Macmillan, 2015).
Dr. Robert W. Walker: Is Associate Professor of Quantitative Methods in the Atkinson Graduate School of Management at Willamette University (2012-). He earned a Ph. D. in political science from the University of Rochester in 2005 and has previously held teaching positions at Dartmouth College, Rice University, Texas A&M University, and Washington University in Saint Louis. His current research develops and applies semi-Markov processes to time-series, cross-section data in international relations and international/comparative political economy. He teaches courses in quantitative methods/applied statistics and microeconomic strategy and previously taught four iterations in the U. S. National Science Foundation funded Empirical Implications of Theoretical Models sequence at Washington University in Saint Louis.
This is an applied course for social scientists that focuses on dynamic models of time series data in single and multiple units (panels). The course starts by introducing fundamental concepts in time series analysis and core issues of heterogeneity when combining multiple time series. After presenting basic graphical and statistical tools for studying dynamic processes, the course considers single and multiple-unit time series with ARMA structures. Then, several widely used models involving cointegration and error correction, VAR and VECM, ARCH and GARCH and Markov switching processes are discussed. Throughout the course, practical examples are used to illustrate the application of various models and students learn how to conduct analyses using the popular R and Stata software programs. Students are encouraged bring their own data to class for analysis.
Background knowledge required
Maximum Likelihood = elementary
Stata = elementary