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Weeks 1 and 2: Models for Panel and Time-Series Cross-Section Data

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Weeks 1 and 2: Models for Panel and Time-Series Cross-Section Data Advanced Topics in Maximum Likelihood Estimation 2015 Weeks 1 and 2: Models for Panel and Time-Series Cross-Section Data Summer 2015 Professor David Darmofal [email protected], [email protected] University of South Carolina Weeks 1-2 (July 20th-July 31st) Course Description These two weeks of the advanced MLE course will focus on methods and models for panel and time-series cross-sectional (TSCS) data. These types of data occur when we have observations for multiple units collected at multiple points in time. Panel data typically are sample data in which there is a large number of cross-sectional units and few time points. TSCS data are typically data in which the units are of interest in themselves, the number of time points is much larger than in panel data, and the number of time points is either larger or approximately equal to the number of cross-sectional units. Since TSCS data are more widely used in political science outside of the area of panel survey studies, much of these weeks will focus on TSCS data rather than panel data. This portion of the class will cover several questions that are central to the use of TSCS and panel data. Among these are fixed effects and random effects models, dynamic models, random coefficient models, models for limited dependent variables, models for spatial dependence, and panel attrition. Students will be asked to complete problem sets that involve estimating models for data collected in time and space. Because the focus of this class is on providing students with knowledge of methods they can apply in their own research, students will also be asked to write a short research proposal that describes a research question and hypotheses that can be tested using the methods learned in this course. Readings The primary text is Cheng Hsiao’s Analysis of Panel Data, 2nd Ed. (Cambridge University Press, 2003). There are also several required articles during this course. You should read both the required pages in Hsiao and the articles before the portions of the course for which they are assigned. Requirements Although not a formal prerequisite, familiarity with statistics at the level of a linear algebra treatment of regression is highly advisable for students enrolled in the course. In addition to the required reading, students will complete a few short problem sets during the course. We will make use of both Stata and R during the course. In addition to these problem sets, students will write a short (1-2 page) research proposal that describes a research question, testable hypotheses, and data that might be used to test these hypotheses for a research question of interest to the student. Course Outline The schedule below lists the topics that will be covered during these two weeks. To provide flexibility in the coverage of this material, I don’t provide specific dates for this material, but we will follow the order below in covering these topics. 1. Course Introduction/Panel vs. TSCS Data Required Reading: Hsiao, Chapter 1 2. Heterogeneity and Pooling/Analysis of Covariance Required: Hsiao, Chapter 2 Stimson, James. 1985. “Regression in Space and Time: A Statistical Essay.” American Journal of Political Science 29: 914-47. 3. Fixed Effects Models Required: Hsiao, Chapter 3 Zorn, Christopher. 2001. “Estimating Between- and Within-Cluster Covariate Effects, with an Application to Models of International Disputes.” International Interactions 27(4): 433-45. Recommended: Green, Donald P., Soo Yeon Kim, and David Yoon. 2001. “Dirty Pool.” International Organization 55: 441-68 (and commentary by Beck and Katz, Oneal and Russett, and King). 4. Random Effects Models Required: Hsiao, Chapter 3 Clark, Tom S., and Drew A. Linzer. 2014. “Should I Use Fixed or Random Effects?" Political Science Research and Methods December: 1-10. Recommended: Baltagi, Badi H. 2001. Econometric Analysis of Panel Data, 2nd Ed, Chapter 2. Chichester: John Wiley & Sons. Park, Jong Hee. 2012. “A Unified Method for Dynamic and Cross-Sectional Heterogeneity: Introducing Hidden Markov Panel Models." American Journal of Political Science 56(4): 1040- 1054. 5. Panel-Corrected Standard Errors Required: Beck, Nathaniel, and Jonathan N. Katz. 1995. “What To Do (And Not to Do) With Time-Series Cross-Section Data.” American Political Science Review 89: 634-647. Beck, Nathaniel, and Jonathan N. Katz. 1996. “Nuisance vs. Substance: Specifying and Estimating Time-Series Cross-Section Models.” Political Analysis 6: 1-36 Recommended: Wilson, Sven E., and Daniel M. Butler. 2007. “A Lot More to Do: The Sensitivity of Time- Series Cross-Section Analyses to Simple Alternative Specifications.” Political Analysis 15(2): 101-123. 6. Variable Coefficient Models/Rarely Changing Variables Required: Hsiao, Chapter 6 Beck, Nathaniel, and Jonathan Katz. 2007. “Random Coefficient Models for Time-Series-Cross- Section Data: Monte Carlo Experiments.” Political Analysis 15(2): 182-195. Plümper, Thomas, and Vera E. 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. Recommended: Beck, Nathaniel, and Jonathan Katz. 2004. “Random Coefficient Models for Time-Series-Cross- Section Data.” Social Science Working Paper 1205, Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, September, 2004. Shor, Boris, Joseph Bafumi, Luke Keele, and David Park. 2007. “A Bayesian Multilevel Modeling Approach to Time-Series Cross-Sectional Data.” Political Analysis 15(2): 165-181. 7. Modeling Dynamics in TSCS Data Required: DeBoef, Suzanna, and Luke Keele. 2008. “Taking Time Seriously.” American Journal of Political Science 52(1): 184-200. Keele, Luke, and Nathan J. Kelly. 2006. “Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables.” Political Analysis 14(2): 186-205. Recommended: Achen, Christopher. 2000. “Why Lagged Dependent Variables Can Suppress the Explanatory Power of Other Independent Variables.” Paper presented at the Annual Meeting of the Society for Political Methodology, UCLA. Wawro, Gregory. 2002. “Estimating Dynamic Panel Data Models in Political Science.” Political Analysis 10: 25-48. 8. Models for Categorical and Limited Dependent Variables/Panel Attrition Required: 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: 1260-88. Hsiao, Chapters 7 and 8. Recommended: Honaker, James, and Gary King. 2010. “What to do About Missing Values in Time Series Cross- Section Data.” American Journal of Political Science 54(3): 561-581. 9. Spatial Models for TSCS Data Required: Elhorst, J. Paul. 2003. “Specification and Estimation of Spatial Panel Data Models.” International Regional Science Review 26(3): 244-268. Franzese, Robert J., Jr., and Jude C. Hays. 2007. “Spatial Econometric Models of Cross- Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data.” Political Analysis 15: 140-164. Advanced Topics in Maximum Likelihood Estimation Weeks 3 and 4: Survival Analysis Summer 2015 Professor Christopher Zorn Department of Political Science Pennsylvania State University E-mail: [email protected] August 3-14, 2015 Description The second two weeks of the “Advanced Maximum Likelihood” course is designed as an in-depth introduc- tion to survival analysis: models for data where the response variable takes the form of a time to an event. Models for survival data are also known as “duration models” and “event history models,” and have seen increasingly widespread use in the social sciences over the past thirty years. Modeling survival data presents a unique set of challenges. The materials in this part of the course will concentrate on practical applications of models for survival data, including issues relating to data setup, software, and interpretation of model estimates. All homework exercises can be conducted using either Stata 14.0 or R 3.2.0 or thereabouts. All notes, exercises, and data will be available at the course github repo. This syllabus is designed to provide an overview to the course. Clickable links are printed in Penn State blue. Course Readings Required Text/Materials Box-Steffensmeier, Janet M., and Bradford S. Jones. 2004. Event History Modeling: A Guide for Social Scientists. New York: Cambridge University Press. Additional readings as necessary, all of which will be available on github and/or through JSTOR. A Few Other Potentially Useful/Recommended Readings Beck, Nathaniel. 1998. “Modeling Space and Time: The Event History Approach.” In E. Scarbrough and E. Tanenbaum, eds., Research Strategies in the Social Sciences. London: Oxford University Press. Box-Steffensmeier, Janet M. and Bradford Jones. 1997. “Time is of the Essence: Event History Models in Political Science.” American Journal of Political Science 41(October):1414-61. Brostrom,¨ Goran.¨ 2012. Event History Analysis with R. New York: Chapman & Hall. 1 Chung, Ching-Fan, Peter Schmidt, and Ann D. Witte. 1991. “Survival Analysis: A Survey.” Journal of Quantitative Criminology 7(March):59-98. Collett, Dave. 2003. Modeling Survival Data in Medical Research, 2nd Ed. London: Chapman & Hall. Cox, David Roxbee and Dallin Oakes. 1984. Analysis of Survival Data. London: CRC/Chapman & Hall. Fleming, Thomas R., and David P. Harrington. 2005. Counting Processes and Survival Analysis, Second Edition. New York: Wiley. Hosmer, D., Stanley
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