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 current research interests focus on the political economy of 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 is chief editor of Electoral Studies. 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, and the 2012 Political Support in America Study. His most recent books are 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.
Course Content 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 Statistics Maximum Likelihood = e
Computer Background Stata = e
e = elementary, m = moderate, s = strong
Daily Schedule and Readings
Day 1 Regression Overview, Introduction to Pooling/Time Series: Berk and Freedman (2003) and Hsiao (N.d.) (optional: Hicks (1994)). Key Issue:T = B + W Exercise: Summarizing data in Stata.
Day 2 Easing Us In: Models for TSCS/CSTS Data: Wooldridge, ch. 10; Stimson (1985), Beck (2001), Beck and Katz(1995) Key Issue: Separating Dimensions and Effects Exercise: Estimate the basic models in Stata for replication data.
Day 3 Unit Heterogeneity and Slope Heterogeneity: Hsaio, ch. 6; Mundlak (1978); Hausman (1978); Beck and Katz (2007) [optional: Troeger (N.d.)]. Key Issue: What models do we compare and how? Exercise: Implementation: What model would you choose? Why? Monte Carlo Simulation and Bootstraps.
Day 4 Recapping and Getting Practical Wilson and Butler (2007); Plumper, Troeger and Manow (2005) Key Issue: Work backward from substance. Exercise: Mixtures and Comparison
Day 5 A Pre-Weekend Think: Dynamics and Thinking about Time: Enders, ch. 2; Beck and Katz (N.d.); Whitten and Williams (2012) Key Issue: What do dynamics mean WITH heterogeneity? Exercise: The range and diversity of dynamic models and interpretation.
Day 6 Dynamic Panel Data Estimators (With a little IV) Cameron and Trivedi, ch. 22; Plumper and Troeger (2007); Wawro (2002) Key Issue: Valid Instruments and Instrumentation in two dimensions. Exercise: Estimating DPDs and FEVD.
Day 7 Exploring Missing Data and Missingness Honaker and King (2010) and Horton and Kleinman (2007) Key Issue: Missing Data are nasty but 2-D gives leverage. Exercise: Imputation and Combination
Day 8 To Generic Data, Part I Baltagi, ch. 11; (Dirty Pool controversy *). Key Issue: Information and Fixed Effects Exercise: Replicate the DP.
Day 9 To Generic Data, Part II Arrellano, Appendix; Beck, Katz and Tucker (1998); Carter and Signorino (2010); Beck et al. (N.d.). Key Issue: Dynamics are interesting with limited outcomes. Exercise: Comparing non-nested models?
Day 10 Causation in a Panel Setting and Course Review Hood, Kidd and Morris (2008); Blackwell and Glynn (N.d.) Key issue: Causation and order in time are a crucial source of potential leverage. Exercise: Applications in the lab
*: Skim the International Organization debate including Green, Kim and Yoon (2001), Oneal and Russett (2001), Beck and Katz (2001), and King (2001).
Beck, Nathaniel, David Epstein, Simon Jackman and Sharyn O’Halloran. N.d. “Alternative Models of Dynamics in Binary Time-Series-Cross-Section Models: The Example of State Failure.” Paper presented at the 2001 Annual Meeting of the Society for Political Methodology, Emory University (Draft: July 12, 2002). URL: http://www.nyu.edu/gsas/dept/politics/faculty/beck/emory.pdf
Beck, Nathaniel and Jonathan N. Katz. 2001. “Throwing out the Baby with the BathWater: A Comment on Green, Kim, and Yoon.” International Organization 55(2):487–495. URL: http://www.jstor.org/stable/3078640
Beck, Nathaniel, Jonathan N. Katz and Richard Tucker. 1998. “Taking time seriously: Time-series-cross-section analysis with a binary dependent variable.” American Journal of Political Science 42(4):1260–1288. URL: http://www.jstor.org/stable/2991857
Beck, Nathaniel L. and Jonathan Katz. N.d. “MODELING DYNAMICS IN TIME SERIES? CROSSSECTION POLITICAL ECONOMY DATA.” California Institute of Technology Social Science Working Paper 1304 (June 2009).
Beck, Nathaniel L. and Jonathan N. Katz. 1995. “What to Do (and Not to Do) with Time-Series-Cross-Section Data in Comparative Politics.” American Political Science Review 89(3):634–647. URL: http://www.jstor.org/stable/2082979
Berk, R. A. and D. A. Freedman. 2003. Statistical Assumptions as Empirical Commitments. In Law, Punishment, and Social Control: Essays in Honor of Sheldon Messinger, ed. T. G. Blomberg and S. Cohen. Second ed. Aldine de Gruyter chapter 10, pp. 235–54. URL: http://stat-www.berkeley.edu/˜census/berk2.pdf
Blackwell, Matthew and Adam Glynn. N.d. “How to Make Causal Inferences with Time-Series Cross-Sectional Data.” version: July 12, 2013.
Hood, M. V., Q. Kidd and I. L. Morris. 2008. “Two Sides of the Same Coin: Employing Granger Causality Testing in a Time Series Cross-Section Framework.” Political Analysis 16(3):324–44.
Horton, Nicholas J. and Ken P. Kleinman. 2007. “Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models.” The American Statistician 61(1):79–90.
Oneal, John R. and Bruce Russett. 2001. “Clear and Clean: The Fixed Effects of the Liberal Peace.” International Organization 55(2):469–485. URL: http://www.jstor.org/stable/3078639
Plumper, Thomas and Vera Troeger. 2007. “Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects.” Political Analysis 15(2):124–139. URL: http://pan.oxfordjournals.org/cgi/reprint/15/2/124