Political Science 794: Methodology Seminar

Winter 2012
Tuesday 5-7 (2336 MH)
Professor: Walter R. Mebane, Jr.
Office: 7735 Haven Hall (607/592-0546); email wmebane@umich.edu
Office hours: Tue 12-2 or other times by appointment.
Course web page: http://www.umich.edu/~wmebane/ps794.html

Assignment Due Dates
due date	description	weight
TBA	participation	10%
April 17	presentation	10%
April 24	course paper	80%

Class meeting and reading schedule ($\bullet$ required)

1. scaling models (Jan 17)
   • Simon Jackman and Shawn Treier. 2008. Democracy as a Latent Variable. American Journal of Political Science 52 (1): 201-217.

   • Joshua Clinton, Simon Jackman and Douglas Rivers. 2004. The Statistical Analysis of Roll Call Data. American Political Science Review 98 (2, May): 355-370.

   • Michael A. Bailey. 2007. Comparable Preference Estimates across Time and Institutions for the Court, Congress, and Presidency. American Journal of Political Science 51 (3, Jul.): 433-448.

   • Jeffrey B. Lewis and Keith T. Poole. 2004. Measuring Bias and Uncertainty in Ideal Point Estimates via the Parametric Bootstrap. Political Analysis, 12: 105-127.

   • Royce Carroll, Jeffrey B. Lewis, James Lo, Keith T. Poole, and Howard Rosenthal. 2009. Measuring Bias and Uncertainty in DW-NOMINATE Ideal Point Estimates via the Parametric Bootstrap. Political Analysis 17: 261-227.

2. latent variables with text (Jan 24)
   • Justin Grimmer. 2010. A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases. Political Analysis 18 (No. 1, Winter): 1-35.

   • Sean M. Gerrish and David M. Blei. 2011. Predicting legislative roll calls from text. International Conference on Machine Learning.
     http://www.cs.princeton.edu/~blei/papers/GerrishBlei2011.pdf

   • David M. Blei and John D. Lafferty. 2007. A correlated topic model of Science. Annals of Applied Statistics 1 (1, Jun.): 17-35.
     http://www.cs.princeton.edu/~blei/papers/BleiLafferty2007.pdf

   • David M. Blei, Andrew Y. Ng and Michael I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3 (Jan.): 993-1022.
     http://www.cs.princeton.edu/~blei/papers/BleiNgJordan2003.pdf

   David M. Blei, Jon D. McAuliffe. 2007. Supervised topic models. Neural Information Processing Systems 21.
   http://www.cs.princeton.edu/~blei/papers/BleiMcAuliffe2007.pdf

3. Dirichlet and beyond (Jan 31)
   • Minjung Kyung, Jeff Gill and George Casella. 2010. Estimation in Dirichlet Random Effects Models. Annals of Statistics 38 (2, Apr.): 979-1009.

   • Samuel J. Gershman and David M. Blei. 2011. A tutorial on Bayesian nonparametric models. Journal of Mathematical Psychology, forthcoming.
     http://www.cs.princeton.edu/~blei/papers/GershmanBlei2011.pdf

   • Lauren A. Hannah, David M. Blei, and Warren B. Powell. 2011. Dirichlet process mixtures of generalized linear models. Journal of Machine Learning Research 12 (Jun.): 1923-1953.
     http://www.cs.princeton.edu/~blei/papers/HannahBleiPowell2011.pdf

   • C. Wang and D. Blei. 2009. Variational inference for the nested Chinese restaurant process. Neural Information Processing Systems.
     http://www.cs.princeton.edu/~blei/papers/WangBlei2009d.pdf

   • Justin Grimmer. 2011. An Introduction to Bayesian Inference via Variational Approximations. Political Analysis 19 (No. 1, Winter): 32-47.

   Brooks, S. P. 1998. Markov chain Monte Carlo method and its application. The Statistician 47: 69-100.

4. choice models (Feb 7)
   • 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. (in file jackson.mach.markowski.elecstud2010.pdf)

   • 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.

   • Walter R. Mebane, Jr. 2012. ``A mixed logit model with 1997 Polish survey data from John Jackson.'' Working paper (in file notes16jan2012.pdf).

   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

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

   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/

5. IV, weak instruments and randomization inference (Feb 14)
   • Donald W.K. Andrews, Marcelo J. Moreira, James H. Stock. 2007. Performance of conditional Wald tests in IV regression with weak instruments. Journal of Econometrics 139 (1, July): 116-132.

   • Guido W. Imbens and Donald B. Rubin. 1997. Estimating Outcome Distributions for Compliers in Instrumental Variables Models. The Review of Economic Studies 64 (4, Oct., Special Issue: Evaluation of Training and Other Social Programmes): 555-574.

   • Guido W. Imbens and Paul R. Rosenbaum. 2005. Robust, accurate confidence intervals with a weak instrument: quarter of birth and education. Journal of the Royal Statistical Society A 168 (1): 109-126.

   • Markus Brückner and Antonio Ciccone. 2011. Rain and the Democratic Window of Opportunity. Econometrica 79 (3, May): 923-947.

   • Timm Betz. 2011. Comments on ``Rain and the Democratic Window of Opportunity.'' Working paper (in file 111204_response.pdf).

   • Timm Betz. 2011. Robust Point Estimates with Non-Random Measurement Error and Instrumental Variables. Working paper (in file 111031_robustIV.pdf).

   Donald Andrews and James Stock. 2005. Inference with Weak Instruments.
   http://cowles.econ.yale.edu/P/cd/d15a/d1530.pdf

   Jinyong Hahn, John Ham, and Hyungsik Rorger Moon. 2011. The Hausman Test and Weak Instruments. Journal of Econometrics 160 (2, February): 289-299.

   Joshua D. Angrist, Guido W. Imbens and Donald B. Rubin. 1996. Identification of Causal Effects Using Instrumental Variables. Journal of the American Statistical Association 91 (434, Jun.): 444-455.

   Constantine E. Frangakis and Donald B. Rubin. 2002. Principal Stratification in Causal Inference. Biometrics 58 (1, Mar.): 21-29.

   Paul Rosenbaum. 2010. Observational Studies, 2d ed. Springer.

   Paul Rosenbaum. 2010. Design of Observational Studies. Springer.

6. causal identification norms (or dogmas) and diagrams (Feb 21)
   • Thomas J. Rothenberg. 1971. Identification in Parametric Models. Econometrica 39 (May): 577-591.

   • Judea Pearl. 1995. Causal Diagrams for Empirical Research. Biometrika 82 (4, Dec.): 669-688. (plus discussion, 688-710).

   • Holland, Paul. 1986, Statistics and Causal Inference. Journal of the American Statistical Association 81: 945-961.

   Roger Bowden. 1973. The Theory of Parametric Identification. Econometrica 41 (Nov): 1069-1074.

   Franklin Fisher. 1976. The Identification Problem in Econometrics. Krieger.

   Roger Bowden and Darrell Turlington. 1984. Instrumental Variables. Cambridge UP.

   Judea Pearl. 2009. Causality: Models, Reasoning and Inference, 2d ed. Cambridge UP. Chapters 1-5.

   James M. Robins. 1999. Association, Causation, and Marginal Structural Models. Synthese 121: 151-179.

7. partial identification (Mar 6)
   • Charles F. Manski. 1990. Nonparametric Bounds on Treatment Effects. American Economic Review 80 (2, Papers and Proceedings): 319-323.

   • Charles F. Manski and Elie Tamer. 2002. Inference on Regressions with Interval Data on a Regressor or Outcome. Econometrica 70 (2, Mar): 519-546.

   • Charles F. Manski. 2007. Partial Identification of Counterfactual Choice Probabilities. International Economic Review 48 (4, Nov): 1429-1439.

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

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

   Charles F. Manski. 1975. Maximum Score Estimation of the Stochastic Utility Model of Choice. Journal of Econometrics 3: 205-228.

   Charles F. Manski. 1975. Semiparametric Analysis of Discrete Response: Asymptotic Properties of the Maximum Score Estimator. Journal of Econometrics 27: 313-333.

   Jeremy T. Fox. 2007. Semiparametric Estimation of Multinomial Discrete-Choice Models Using a Subset of Choices. The RAND Journal of Economics 38 (4, Winter): 1002-1019.

8. identification with missing covariates (Mar 13)
   • Joel L. Horowitz and Charles F. Manski. 2000. Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data. Journal of the American Statistical Association 95 (449, Mar): 77-84.

   • Rosa L. Matzkin. 2007. Nonparametric Survey Response Errors. International Economic Review 48 (4): 1411-1427.

   • Francesca Molinari. 2010. Missing Treatments. Journal of Business and Economic Statistics 28 (1): 82-95.

   • Walter R. Mebane, Jr. and Paul Poast. 2012. ``Causal Inference without Ignorability: Identification with Nonrandom Assignment and Missing Treatment Data.'' Working paper (in file CIwI.pdf).

   Beresteanu, Arie and Francesca Molinari. 2008. Asymptotic Properties for a Class of Partially Identified Models. Econometrica 76(4): 763-814.

   Charles F. Manski. 2011. Identification of Treatment Response with Social Interactions. Working paper in file treatment_with_interactions.pdf

   Rosa L. Matzkin. 2007. Nonparametric Identification. In James J. Heckman and Edward E. Leamer, eds., Handbook of Econometrics volume 6B. North-Holland. Pp. 5307-5368.

9. regression discontinuity (Mar 20)
   • Imbens, Guido W., and Thomas Lemieux. 2008. Regression discontinuity designs: A guide to practice. Journal of Econometrics 142 (2): 615-635.

   • David S. Lee. 2008. Randomized experiments from non-random selection in U.S. House elections. Journal of Econometrics 142 (2): 675-697..

   • Devin Caughey and Jasjeet S. Sekhon. 2011. Elections and the Regression-Discontinuity Design: Lessons from Close U.S. House Races, 1942-2008. Political Analysis 19 (4): 385-408.

10. matching (Mar 27)
    • Jasjeet S. Sekhon. 2009. Opiates for the Matches: Matching Methods for Causal Inference. Annual Review of Political Science 12: 487-508. (in file opiates.pdf).

    • Keisuke Hirano, Guido W. Imbens, and Geert Ridder. 2003. Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica 71 (4, July): 1161-1189.

    • Alexis Biamond and Jasjeet S. Sekhon. 2010. Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies. Review of Economics and Statistics forthcoming. (in file GenMatch.pdf).

    • Ben B. Hansen and Jake Bowers. 2009. Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign. Journal of the American Statistical Association 104 (Sep): 873-885.

    Arceneaux, Kevin, Alan S. Gerber, and Donald P. Green. 2006. Comparing Experimental and Matching Methods using a Large-Scale Voter Mobilization Experiment. Political Analysis 14: 1-36.

    Alberto Abadie, Guido W. Imbens. 2008. On the Failure of the Bootstrap for Matching Estimators. Econometrica 76 (6): 1537-1557.

    Alberto Abadie, Guido W. Imbens. 2006. Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica 74 (1): 235-267.

11. structural dynamic programming models (Apr 3)
    • William D. MacMillan. 2012. Stochastic Control of Appropriations by Antitrust Enforcers. Working paper (in file MacMillan antitrust 02feb2012.pdf).

    • Rust, J. 1987. Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher. Econometrica 55: 999-1033.

    • V. Joseph Hotz and Robert A. Miller. 1993. Conditional Choice Probabilities and the Estimation of Dynamic Models The Review of Economic Studies 60 (3, Jul): 497-529.

    • Hiroyuki Kasahara and Katsumi Shimotsu. 2009. Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices. Econometrica 77 (1, Jan): 135-175.

    Victor Aguirregabiria and Pedro Mira. 2007. Sequential Estimation of Dynamic Discrete Games. Econometrica 75 (1, Jan.): 1-53.

    Hiroyuki Kasahara and Katsumi Shimotsu. 2008. Pseudo-likelihood estimation and bootstrap inference for structural discrete Markov decision models. Journal of Econometrics 146 (1): 92-106.

12. presentations (Apr 10, 17)