Political Science 787: Multivariate Analysis

Fall 2011
Monday 4-6 (7603 Haven)
Professor: Walter R. Mebane, Jr.
Office: 7735 Haven Hall (734/763-2220); email wmebane@umich.edu
Office hours: Wed 2-4 or other times by appointment.
Course web page: in CTools; syllabus also at http://www.umich.edu/~wmebane/ps787.html

Assignment Due Dates
due date	description	weight
TBA	several data analysis problems	80%
TBA	final paper	20%

Reading Availability

Much of the course will refer to journal articles. I plan to follow or refer to a few chapters in the following books (others also appear below).

Cameron, A. Colin, and Pravin K. Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge UP.

Sik-Yum Lee. 2007. Structural Equation Modelling: A Bayesian Approach. Wiley.

Kenneth E. Train. 2003. Discrete Choice Methods with Simulation. Cambridge UP.

In the following listing, required reading is preceded by a bullet. Other items are recommended.

Class meeting and reading schedule

1. computing (Sep 12)
   • John Chambers. 1999. Computing with Data: Concepts and Challenges. The American Statistician 53: (1, Feb.): 73-84.

   • Bierlaire, M. 2003. BIOGEME: A free package for the estimation of discrete choice models, Proceedings of the 3rd Swiss Transportation Research Conference, Ascona, Switzerland. http://biogeme.epfl.ch/

   • OpenBUGS. http://www.openbugs.info/w/

   • The BUGS Project. http://www.mrc-bsu.cam.ac.uk/bugs/

   Crawley, Michael R. 2007. The R Book. Wiley.

   Spector, Phil. 2008. Data manipulation with R. Springer.

   Albert, Jim. 2007. Bayesian Computation with R. Springer.

   Chambers, John M. 2008. Software for Data Analysis. Springer.

   Braun, W. John, and Duncan J. Murdoch. 2007. A First Course in Statistical Programming with R. Cambridge.

   Plummer, Martyn. 2003. ``JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling,'' Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), March 20-22, Vienna, Austria. ISSN 1609-395X.
   http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Proceedings/Plummer.pdf

   JAGS. http://mcmc-jags.sourceforge.net/

2. asymptotics, bootstrap and refinements (Sep 19)
   • Bradley Efron. 1987. ``Better Bootstrap Confidence Intervals (and discussion)'' Journal of the American Statistical Association 82 (397): 171-200.

   • Gary W. Oehlert. 1992. A Note on the Delta Method. The American Statistician 46 (1, Feb.): 27-29.

   • Thomas R. Fears, Jacques Benichou, Mitchell H. Gail. 1996. A Reminder of the Fallibility of the Wald Statistic. The American Statistician 50 (3, Aug.): 226-227.

   • Yudi Pawitan. 2000. A Reminder of the Fallibility of the Wald Statistic: Likelihood Explanation. The American Statistician 54 (1, Feb.): 54-56.

   • J. Scott Long and Laurie H. Ervin. 2000. Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model. The American Statistician 54 (3, Aug.): 217-224.

   • Dennis D. Boos and Jacqueline M. Hughes-Oliver. 2000. How Large Does $n$ Have to be for $Z$ and $t$ Intervals? The American Statistician 54 (2, May): 121-128.

   Bradley Efron. 1979. ``Bootstrap Methods: Another Look at the Jackknife,'' Annals of Statistics 7 (1): 1-26.

   Barndorff-Nielsen, O. E., and D. R. Cox. 1984. ``Bartlett Adjustments to the Likelihood Ratio Statistic and the Distribution of the Maximum Likelihood Estimator,'' Journal of the Royal Statistical Society. Series B (Methodological) 46 (3): 483-495.

   A.C. Davison and D.V. Hinkley. 1997. Bootstrap Methods and their Applications. Cambridge.

   Cameron and Trivedi. Chapters 5, 11 and Appendix A.

   Jeffrey M. Wooldridge. 2002. Econometric Analysis of Cross Section and Panel Data. MIT Press. Chapters 3, 12-14.

   Russell Davidson and James G. MacKinnon. 1993. Estimation and Inference in Econometrics. Oxford UP. Chapters 4, 8-9.

3. M-estimators and QMLE (Sep 26)
   • Leonard A. Stefanski and Dennis D. Boos, 2002. The Calculus of M-Estimation. The American Statistician 56 (1, Feb.): 29-38.

   • White, Halbert. 1982. Maximum Likelihood Estimation of Misspecified Models. Econometrica 50 (1): 1-25.

   • D. A. Freedman. 2006. On the so-called ``Huber Sandwich Estimator'' and ``robust'' standard errors. The American Statistician 60: 299-302.

   Peter McCullagh and John A. Nelder. 1989. Generalized Linear Models. 2d ed. Chapman and Hall.

   McCullagh, Peter. 1983. Quasi-likelihood Functions. Annals of Statistics 11 (Mar.): 59-67.

4. choice models (Oct 3, 10)
   • Daniel McFadden and Kenneth Train. 2000. Mixed MNL Models for Discrete Response. Journal of Applied Econometrics 15 (5, Sep-Oct): 447-470.

   • Kenneth E. Train. 2009. Discrete Choice Methods with Simulation. 2d ed. Cambridge UP. http://elsa.berkeley.edu/books/choice2.html

   • John E. Jackson, Bogdan W. Mach and Radoslaw Markowski. 2010. ``Party Strategies and Electoral Competition in Post-Communist Countries: Evidence from Poland.'' Electoral Studies 29 (2): 199-209.

   • John E. Jackson, Bogdan W. Mach and Radoslaw Markowski. 2010. ``Party Strategies and Electoral Competition in Post-Communist Countries: Evidence from Poland. Appendix A: Methodological Appendix.'' (in file jelsmethapp.docx)

   • Garrett Glasgow. 2001. Mixed Logit Models for Multiparty Elections. Political Analysis 9 (1): 116-136.

   Hensher, David A., and William H. Greene. 2003. The mixed logit model: the state of practice. Transportation 30 (2): 133-176.

   McFadden, Daniel. 1974. ``Conditional logit analysis of qualitative choice behavior.'' In P Zarembka, ed., Frontiers of Econometrics, New York: Acadmic Press. pages 105-142.
   http://emlab.berkeley.edu/reprints/mcfadden/zarembka.pdf

   McFadden, Danel. 1981. ``Structural Discrete Probability Models Derived from Theories of Choice.'' In Charles F. Manski and Daniel L. McFadden, eds, Structural Analysis of Discrete Data and Econometric Applications, Cambidge, MA: MIT Press, chapter 5, pp. 198-272.
   http://emlab.berkeley.edu/discrete/ch5.pdf

5. MCMC (Oct 24)
   • Brooks, S. P. 1998. Markov chain Monte Carlo method and its application. The Statistician 47: 69-100.

   • Brooks, S. P. and A. Gelman. 1998. Alternative methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7: 434-455.

   • Spiegelhalter, D. J., N. G. Best, B. P. Carlin and A. van der Linde. 2002. Bayesian measures of model complexity and fit (with discussion). J. Roy. Statist. Soc. B 64: 583-640.

   George Casella and Edward I. George. 1992. Explaining the Gibbs Sampler The American Statistician 46 (3, Aug.): 167-174.

   Siddhartha Chib and Edward Greenberg. 1995. Understanding the Metropolis-Hastings Algorithm The American Statistician 49 (4, Nov.): 327-335.

   Andrew Gelman, John B. Carlin, Hal S. Stern and Donald B. Rubin. 2004. Bayesian Data Analysis, 2d ed. Chapman & Hall.

   Andrew Gelman and Jennifer Hill. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge.

   Jeff Gill. 2002. Bayesian Methods: A Social and Behavioral Approach. Chapman & Hall.

   Simon Jackman. 2009. Bayesian Analysis for the Social Sciences. Wiley.

6. latent variable models (Oct 31)
   • Jian-Qing Shi and Sik-Yum Lee. 2000. Latent Variable Models with Mixed Continuous and Polytomous Data. Journal of the Royal Statistical Society. Series B (Statistical Methodology) 62 (1): 77-87.

   • Vincent Arel-Bundock and Walter R. Mebane, Jr. 2011. ``Measurement Error, Missing Values and Latent Structure in Governance Indicators''. Paper presented at the 2011 Annual Meeting of the American Political Science Association, Seattle, WA, September 1-4.

   Sik-Yum Lee. 2007. Structural Equation Modelling: A Bayesian Approach. Wiley.

   Sik-Yum Lee, Xin-Yuan Song, John C. K. Lee. 2003. Maximum Likelihood Estimation of Nonlinear Structural Equation Models with Ignorable Missing Data. Journal of Educational and Behavioral Statistics 28 (Summer): 111-134.

   Sik-Yum Lee and Xin-Yuan Song. 2004. Maximum Likelihood Analysis of a General Latent Variable Model with Hierarchically Mixed Data. Biometrics 60 (Sep.): 624-636.

   Sophia Rabe-Hesketh, Anders Skrondal and Andrew Pickles. 2004. Generalized Latent Variable Modelling: Multilevel, Longitudinal and Structural Equation Models. Chapman & Hall.

7. selectivity or nonignorability and missingness (Nov 7)
   • Heckman, J. J. 1978. Dummy endogenous variables in a simultaneous equation system. Econometrica 46: 931-959.

   • Heckman, J. J. 1979. Sample selection bias as a specification error. Econometrica 47: 153-161.

   • Charles F. Manski. 1990. Nonparametric Bounds on Treatment Effects. American Economic Review 80 (2, Papers and Proceedings): 319-323.

   • Francesca Molinari. 2002. Missing Treatments. Journal of Business and Economic Statistics. 28 (1): 82-95. (see also file Missing Treatments.pdf).

   • Peter Aronow, Walter R. Mebane, Jr., and Paul Poast 2011. ``Causal Inference without Ignorability: Identification of Treatment Effects with Missing Treatment Data and Nonrandom Assignment.'' Working paper.

   David A. Freedman and Jasjeet S. Sekhon. 2010. Endogeneity in Probit Response Models. Political Analysis 18 (2): 138-150.

   James J. Heckman and Edward Vytlacil. 2005. Structural Equations, Treatment Effects, and Econometric Policy Evaluation. Econometrica 73 (3, May): 669-738.

   Charles F. Manski. 1995. Identification in the Social Sciences. Harvard UP.

   Charles F. Manski. 2003. Partial Identification of Probability Distributions. Springer.

8. instrumental variables and treatment effects (Nov 14)
   • Joshua D. Angrist, Guido W. Imbens, Donald B. Rubin. 1996. ``Identification of Causal Effects Using Instrumental Variables,'' Journal of the American Statistical Association 91 (434): 444-455.

   Joshua D. Angrist and Jörn-Steffen Pischke. 2009. Mostly Harmless Econometrics. Princeton UP.

9. hypothesis tests (Nov 21)
   • Cameron and Trivedi. Chapter 7.

   • Benjamini, Yoav and Yosef Hochberg. 1995. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, Series B 57 (1): 289-300.

   • Benjamini, Yoav and Daniel Yekutieli. 2005. False Discovery Rate-Adjusted Multiple Confidence Intervals for Selected Parameters. Journal of the American Statistical Association 100 (Mar.): 71-81.

   • Vuong, Quang H. 1989. ``Likelihood-ratio Tests for Model Selection and Non-nested Hypotheses.'' Econometrica 57 (2): 307-333.

10. bounded influence estimation (Nov 28)
    • Stefanski, Leonard A., Raymond J. Carroll, David Ruppert. 1986. Optimally Bounded Score Functions for Generalized Linear Models with Applications to Logistic Regression. Biometrika 73 (Aug): 413-424.

    • Mebane, Walter R., Jr., and Jasjeet S. Sekhon. 2004. Robust Estimation and Outlier Detection for Overdispersed Multinomial Models of Count Data. American Journal of Political Science 48 (April): 392-411.

    • Mebane, Walter R., Jr. 2010. Fraud in the 2009 Presidential Election in Iran? Chance 23 (Mar.): 6-15.

    Hampel, Frank R. and Peter J. Rousseeuw and Elvezio Ronchetti. 1981. The Change-of-Variance Curve and Optimal Redescending M-Estimators. Journal of the American Statistical Association 76 (Sep): 643-648.

    Croux, Christophe and Peter J. Rousseeuw and Ola Hossjer. 1994. Generalized S-Estimators. Journal of the American Statistical Association 89 (Dec): 1271-1281.

11. papers and clean-up (Dec 5, 12)