Political Science 799: Multivariate Analysis

Fall 2026
Tuesday 4–6 (1512 NUB) Tuesday 6–7 (2462 MH)
Professor: Walter R. Mebane, Jr.
Office: 7735 Haven Hall (607/592-0546); email [email protected]
Office hours: Tue 1–3 or other times by appointment.
Course web page: in Canvass; syllabus also at http://www.umich.edu/~wmebane/ps799.html

Assignment Due Dates
due date	description	weight
TBA	problem sets	40%
Sep 22	one-page paper proposal	5%
Nov 24	paper abstract	5%
Dec 15 1:30–3:30	final paper presentations	15%
Dec 17	final paper	25%
—	participation	10%

See fpaper.pdf posted on the Canvas site for more information about the final paper and presentation.

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.

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

   • Bierlaire, M. 2018. BIOGEME: A free package for the estimation of discrete choice models, http://biogeme.epfl.ch/

   • OpenBUGS. http://www.openbugs.net/w/FrontPage

   • WinBUGS. https://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/

   • Bob Carpenter, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. Stan: A probabilistic programming language. Journal of Statistical Software 76(1). DOI 10.18637/jss.v076.i01 (in file v76i01.pdf)

   The Comprehensive R Archive Network. 2019. https://cran.r-project.org/

   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.

   Bierlaire, M. (2018). PandasBiogeme: a short introduction. Technical report TRANSP-OR 181219. Transport and Mobility Laboratory, ENAC, EPFL.
   http://transp-or.epfl.ch/documents/technicalReports/Bier18.pdf

   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/

   Pemstein, Daniel, Kevin M. Quinn and Andrew D. Martin. 2011. “The Scythe Statistical Library: An Open Source C++ Library for Statistical Computation.” Journal of Statistical Software. 42. DOI 10.18637/jss.v042.i12

   Martin, Andrew D., Kevin M. Quinn, and Jong Hee Park. 2011. “MCMCpack: Markov Chain Monte Carlo in R.” Journal of Statistical Software. 42. DOI 10.18637/jss.v042.i09

2. maximum likelihood and numerical optimization (Sep 8)
   • A. Colin Cameron and Pravin K. Trivedi. 2005. Microeconometrics: Methods and Applications. Cambridge. Chapter 5. (in file CT5.pdf)

   • Walter R. Mebane, Jr., and Jasjeet Sekhon. 2011. “Genetic Optimization Using Derivatives: The rgenoud Package for R.” Journal of Statistical Software 42(11). https://www.jstatsoft.org/article/view/v042i11

   • Walter R. Mebane, Jr. 1999. “Congressional Campaign Contributions, District Service and Electoral Outcomes in the United States: Statistical Tests of a Formal Game Model with Nonlinear Dynamics” in Diana Richards, ed., Political Complexity: Nonlinear Models of Politics. Ann Arbor: University of Michigan Press. (in file ghopf.pdf)

   • Mebane, Walter R., Jr. 2000. “Coordination, Moderation, and Institutional Balancing in American Presidential and House Elections.” American Political Science Review 94 (March): 37–57. MS version available at http://www.umich.edu/~wmebane/covote.pdf

   • 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.

   Philip E. Gill , Walter Murray and Margaret H. Wright. 1982. Practical Optimization. Classics in Applied Mathematics edition (2019). https://doi.org/10.1137/1.9781611975604

   Stefan Theussl, Florian Schwendinger, Hans W. Borchers. 2021. “CRAN Task View: Optimization and Mathematical Programming.” https://cran.r-project.org/web/views/Optimization.html

   John E. Jackson. 1983. “Election Night Reporting and Voter Turnout.” American Journal of Political Science 27(4): 615–635.

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

   Leonard A. Stefanski and Dennis D. Boos, 2002. The Calculus of M-Estimation. The American Statistician 56 (1, Feb.): 29–38.

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

   O. E. Barndorff-Nielsen. 1995. “Quasi Profile and Directed Likelihoods From Estimating Functions.” Ann. Inst. Statist. Math. 47(3), 461–464. https://www.ism.ac.jp/editsec/aism/pdf/047_3_0461.pdf

   S. A. Murphy and A. W. van der Vaart. 2000. “On Profile Likelihood.” Journal of the American Statistical Association 95 (450 Jun)), 449–465. (in file Murphy.Vaart.JASA2000.2669386.pdf)

3. asymptotics, bootstrap and refinements (Sep 15)
   • Bradley Efron. 1987. “Better Bootstrap Confidence Intervals (and discussion)” Journal of the American Statistical Association 82 (397): 171–200. (in file efron.jasa1987.pdf and nine other similarly named files)

   • Gary W. Oehlert. 1992. A Note on the Delta Method. The American Statistician 46 (1, Feb.): 27–29. (in file oehlert.amstat1992.pdf)

   • 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. (in file fears.benichou.gail.amstat1996.pdf)

   • Yudi Pawitan. 2000. A Reminder of the Fallibility of the Wald Statistic: Likelihood Explanation. The American Statistician 54 (1, Feb.): 54–56. (in file pawitan.amstat2000.pdf)

   • 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. (in file boos.hughesoliver.amstat2000.pdf)

   • 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. (in file long.ervin.amstat2000.pdf)

   • John E. Jackson. 2019. “Corrected Standard Errors with Clustered Data.” Political Analysis, 28 (3, July): 318–339. (in file cese_PA_final.pdf)

   Bradley Efron. 1979. “Bootstrap Methods: Another Look at the Jackknife,” Annals of Statistics 7 (1): 1–26. (in file efron.aos1979.pdf)

   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.

   Steven J. Sepanski. 1994. “Asymptotic for Multivariate $t$-Statistic and Hotelling's $T^2$-Statistic under Infinite Second Moments via Bootstrapping,” Journal of Multivariate Analysis 49 (1): 41–54.

   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.

   Diogo Ferrari and John E. Jackson. 2019. “ceser R Package: Cluster Estimated Standard Error in R.” Journal of Statistical Software. (in file ceser.pdf)

4. text as data (Sep 22)
   • 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.
     (in file bayesian_hierarchical_topic_model_for_political_texts_measuring
     _expressed_agendas_in_senate_press_releases.pdf)

   • Justin Grimmer and Brandon M. Stewart. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21: 267–297. (in file text_as_data_the_promise_and_pitfalls_.pdf)

   • Margaret E. Roberts, Brandon M. Stewart, Dustin Tingley, Christopher Lucas, Jetson Leder-Luis, Shana Kushner Gadarian, Bethany Albertson and David G. Rand. 2014. “Structural Topic Models for Open-Ended Survey Responses.” American Journal of Political Science, 58 (4):1064–1082. (in file Roberts-StructuralTopicModels-2014.pdf)

   • Margaret E. Roberts, Brandon M. Stewart, Dustin Tingley and Edoardo M. Airoldi. 2013. “The Structural Topic Model and Applied Social Science.” NIPS 2013 Workshop on Topic Models: Computation, Application, and Evaluation. (in file stmnips2013.pdf)

   • Fong, Christian and Justin Grimmer (2016). “Discovery of Treatments from Text Corpora.” Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics 1: 1600-1609. (in file Discovery of Treatments from Text Corpora (2016, ACL).pdf)

   • Fong, Christian and Justin Grimmer (2023). “Causal Inference with Latent Treatments.” American Journal of Political Science. 67(2): 374–389. (in file Causal Inference with Latent Treatments (2023, AJPS).pdf)

   Burt L. Monroe, Michael P. Colaresi and Kevin M. Quinn. 2008. “Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict.” Political Analysis 16: 372–403. (in file fightin_words_lexical_feature_selection_and_.pdf)

   Matthew J. Denny and Arthur Spirling. 2018. “Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It.” Political Analysis 26: 168–189. (in file text_preprocessing_for_unsupervised_learning.pdf)

   Mitchel Bosley, Saki Kuzushima, Ted Enamorado and Yuki Shiraito. 2024. “Improving Probabilistic Models in Text Classification via Active Learning.” American Political Science Review. DOI: https://doi.org/10.1017/S0003055424000716 (in file improving-probabilistic-models-in-text-classification-via-active-learning.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.jmlr.org/papers/volume3/blei03a/blei03a.pdf

   David M. Blei, Jon D. McAuliffe. 2007. Supervised topic models. Neural Information Processing Systems 21.
   https://papers.nips.cc/paper/3328-supervised-topic-models.pdf

   Patrick Y. Wu, Walter R. Mebane, Jr., Joseph Klaver, Logan Woods and Preston Due. 2019. “Partisan Associations of Twitter Users Based on Their Self-descriptions and Word Embeddings.” Presented at APSA 2019. (updated version in file wepa.pdf)

   Blake Miller, Fridolin Linder and Walter R. Mebane, Jr. 2020. “Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning Approaches.” Political Analysis, 28 (4, October): 532–551. DOI: https://doi.org/10.1017/pan.2020.4 (in file
   active_learning_approaches_for_labeling_text_review_and_assessment_of_the_performance_of_active_learning_approaches.pdf)

   Walter R. Mebane, Jr., Patrick Y. Wu, Logan Woods, Alejandro Pineda, Blake Miller, Joseph Klaver, Preston Due and Adam Rauh. 2020. “Diverse Election Experiences Reported without Bias: Observing Election Incidents in the United States via Twitter.” (in file TEO.pdf)

5. prediction measurement errors and LLMs (Sep 29)
   • Fong, Christian and Matthew Tyler (2021). “Machine Learning Predictions as Regression Covariates.” Political Analysis. 29(4): 467–484. (in file Machine Learning Predictions as Regression Covariates (2021, PA).pdf)

   • Naoki Egami, Musashi Hinck, Brandon M. Stewart and Hanying Wei. 2023. “Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models.” 37th Conference on Neural Information Processing Systems (NeurIPS 2023). (in file dsl.pdf).

   • Naoki Egami, Musashi Hinck, Brandon M. Stewart and Hanying Wei. 2023. “Using Large Language Model Annotations for the Social Sciences: A General Framework of Using Predicted Variables in Downstream Analyses.” 2024. (in file dsl_ss.pdf).

   • Alessandra Rister Portinari Maranca, Adam D. Wolsky, Jihoon Chung, Naoki Egamim Musashi Hinck and Brandon M. Stewart. 2025. “Correcting the Measurement Errors of AI-assisted Labeling in Image Analysis using Design-based Supervised Learning.” Sociological Methods and Research (Forthcoming). (in file dsl_image.pdf).

   • Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser and Illia Polosukhin. 2017. “Attention Is All You Need.” 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. (in file NIPS-2017-attention-is-all-you-need-Paper.pdf).

   • Andreas Liesenfeld and Mark Dingemanse. 2024. “Rethinking open source generative AI: open-washing and the EU AI Act.” 2024. FAccT ’24, June 03–06, 2024, Rio de Janeiro, Brazil. (in file 3630106.3659005.pdf).

6. prediction, machine learning, LASSO, regularization (Oct 6)
   • Trevor Hastie, Robert Tibshirani and Jerome Friedman. 2009. The Elements of Statistical Learning. Springer. Chapters 2, 5, 7 and 11. (in files ESL2.pdf, ESL5.pdf, ESL7.pdf and ESL11.pdf)

   • Trevor Hastie, Robert Tibshirani and Martin Wainwright. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press. Chapters 2, 3 and 11. (in files SLS2.pdf, SLS3.pdf and SLS11.pdf)

7. choice models (Oct 13)
   • Daniel McFadden and Kenneth Train. 2000. Mixed MNL Models for Discrete Response. Journal of Applied Econometrics 15 (5, Sep–Oct): 447–470.
     (in file mcfadden.train.japplecon2000.pdf)

   • 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. (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)

   • Walter R. Mebane, Jr., John E. Jackson and Jonathan Wall. 2015. “Choice Function Heterogeneities in Models of Electoral Behavior.” Working paper (in file mw14.pdf)

   • Garrett Glasgow. 2001. Mixed Logit Models for Multiparty Elections. Political Analysis 9 (1): 116–136. (in file glasgow.pa2001.pdf)

   • Kirill Zhirkov. 2021. “Estimating relative prevalence of competing issue voting rules in multiparty elections using finite mixture modeling.” Electoral Studies 69 (2021) 102249. (in file 1-s2.0-S0261379420301281-main.pdf)

   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

   Mauricio Sarrias and Ricardo A. Daziano. 2017. “Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package.” Journal of Statistical Software 79 (2). doi: 10.18637/jss.v079.i02

8. observational studies, RD and causal inference (Oct 27)
   • Angrist, Joshua D., Guido Imbens and Donald B. Rubin. 1996. “Identification of Causal Effects Using Instrumental Variables.” Journal of the American Statistical Association 91(June): 444–455. (in file angrist.imbens.rubin.jasa1996.pdf)

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

   • Lee, D. S. 2008. “Randomized Experiments from Non-random Selection in U.S. House Elections.” Journal of Econometrics 142:675–-697. (in file lee.jeconometrics2008.pdf)

   • Jasjeet Sekhon and Rocio Titiunik. 2017. “On Interpreting the Regression Discontinuity Design as a Local Experiment.” Regression Discontinuity Designs (Advances in Econometrics, Vol. 38). Emerald Publishing Limited, pp. 1-28. https://doi.org/10.1108/S0731-905320170000038001 . (in file SekhonTitiunik-RD-2016.pdf)

   • Sebastian Calonico, Matias D. Cattaneo and Rocio Titiunik. 2014. “Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs.” Econometrica 82(6):2295–2326. (in file Calonico-Cattaneo-Titiunik_2014_ECMA.pdf)

   • Naoki Egami and Erin Hartman. 2023. “Elements of External Validity: Framework, Design, and Analysis.” American Political Science Review 117(3): 1070-–1088. (in file external_full.pdf).

   Holland, Paul. 1986, “Statistics and Causal Inference.” Journal of the American Statistical Association 81: 945–961. (in file holland.jasa1986.pdf)

   Paul R. Rosenbaum. 2002. Observational Studies. Springer.

   Paul R. Rosenbaum. 2009. Design of Observational Studies. Springer.

   Judea Pearl. 2009. Causality: Models, Reasoning, and Inference, 2d ed. Cambridge.

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

   Dunning, Thad. 2012. Natural Experiments in the Social Sciences: A Design-Based Approach (Strategies for Social Inquiry). Cambridge.

   Gerber, Alan S., and Donald P. Green. 2012. Field Experiments: Design, Analysis, and Interpretation. Norton.

   Guido W. Imbens and Donald B. Rubin. 2015. Causal Inference for Statistics, Social and Biomedical Sciences: An Introduction. Cambridge.

   Matias D. Cattaneo, Nicolás Idrobo and Rocío Titiunik. 2019. A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge Elements, Quantitative and Computational Methods for the Social Sciences, Cambridge.

   Matias D. Cattaneo, Nicolás Idrobo and Rocío Titiunik. 2024. A Practical Introduction to Regression Discontinuity Designs: Extensions. Cambridge Elements, Quantitative and Computational Methods for the Social Sciences, Cambridge.

9. time-series cross-sectional (TSCS) data (Nov 3)
   • Stephen Nickell. 1991. “Biases in Dynamic Models with Fixed Effects.” Econometrica 49 (6):1417–1426. (in file Nickell-BiasesDynamicModels-1981.pdf)

   • Blackwell, Matthew, and Adam N. Glynn. 2018. “How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables.” American Political Science Review 112 (4):1067–-1082. (in file BLACKWELL-MakeCausalInferences-2018.pdf)

   • Imai, Kosuke, and In Song Kim. 2019. “When Should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data.” Journal of Political Science 63 (2):467–-490. (in file Imai-UseUnitFixed-2019.pdf)

   • Imai, Kosuke and In Song Kim. 2021. “On the Use of Two-way Fixed Effects Regression Models for Causal Inference with Panel Data.” Political Analysis, 29 (3):405–415. (in file FEmatch-twoway.pdf)

   • Imai, Kosuke, In Song Kim, and Erik H. Wang. 2023. “Matching Methods for Causal Inference with Time-Series Cross-Sectional Data.” American Journal of Political Science 67 (3):587–605. (in file Imai-tscs2023.pdf)

   • Licheng Liu, Ye Wang and Yiqing Xu. 2024. “A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data.” American Journal of Political Science, 68 (1):160–176. (in file Liu_Wang_Xu_AJPS2024.pdf)

10. causal identification norms, DAGs, interference (Nov 10)
    • Thomas J. Rothenberg. 1971. Identification in Parametric Models. Econometrica 39 (May), 577–591. (in file rothenberg.econometrica1971.pdf)

    • Aronow, Peter M. and Samii, Cyrus, 2017. “Estimating average causal effects under general interference, with application to a social network experiment.” The Annals of Applied Statistics 11 (4), 1912-1947. (in file aronow.samii.aapplstat2017.pdf)

    • Naoki Egami. 2021. “Spillover Effects in the Presence of Unobserved Networks.” Political Analysis 29: 287-–316. (in file netspill.pdf).

    • Hainmueller, Jens, Hopkins, Daniel J. and Yamamoto, Teppei, 2014. “Causal inference in conjoint analysis: Understanding multidimensional choices via stated preference experiments.” Political Analysis 22 (1), 1-30. (in file hainmueller.etal.pa2014.pdf)

    • Egami, Naoki and Imai, Kosuke, 2019. “Causal interaction in factorial experiments: Application to conjoint analysis.” Journal of the American Statistical Association 114 (526), 529-540. (in file egami.imai.jasa2019.pdf)

    • P. L. Cohen and C. B. Fogarty. 2024. No-harm calibration for generalized Oaxaca-Blinder estimators. Biometrika 111 (1), 331–338. (in file P17 2024 No Harm Calibration.pdf).

    • Colin B. Fogarty and Raiden B. Hasegawa. 2019. Extended sensitivity analysis for heterogeneous unmeasured confounding with an application to sibling studies of returns to education. Annals of Applied Statistics 13 (2), 767–796. (in file P9 2019 Extended Sensitivity Analysis.pdf)

    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.

    Colin B. Fogarty. 2020. Studentized Sensitivity Analysis for the Sample Average Treatment Effect in Paired Observational Studies. Journal of the American Statistical Association 115 (531), 1518–1530. (in file P10 2020 Studentized Sensitivity Analysis.pdf)

    Graeme Blair, Jasper Cooper, Alexander Coppock and Macartan Humphreys. 2019. “Declaring and Diagnosing Research Designs.” American Political Science Review 113 (3): 838–859.

    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.

    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.

11. Bayesian estimation via MCMC (Nov 17)
    • Brooks, S. P. 1998. Markov chain Monte Carlo method and its application. The Statistician 47: 69–100. (in file brooks.statistician1998.pdf)

    • 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. (in file spiegelhalter.jrssb2002.pdf)

    • Bettina Grün and Friedrich Leisch.. 2009. Dealing with label switching in mixture models under genuine multimodality. Journal of Multivariate Analysis 100, 851–861. (in file grun-leisch-JMultAnala2009.pdf)

    • Lei Gong and James M. Flegal. 2016. A practical sequential stopping rule for high-dimensional Markov chain Monte Carlo. Journal of Computational and Graphical Statistics 25, 684–700. (in file Gong-Flegal-JCGS2016.pdf)

    • Dootika Vats, James M. Flegal and Galin L. Jones. 2016. Multivariate output analysis for Markov chain Monte Carlo. Preprint. Available at arXiv:1512.07713. Published in Biometrika in 2019: 106, 321–337. (preprint in file Vats-Flegal-Jones-1512.07713v2.pdf)

    Simon Jackman. 2009. Bayesian Analysis for the Social Sciences. Wiley. Chapter 7.

    Andrew Gelman, John B. Carlin, Hal S. Stern and Donald B. Rubin. 2004. Bayesian Data Analysis, 2d ed. Chapman & Hall. Chapter 5. (Chapters 1–4 are probably necessary preparation.)

    Andrew Gelman and Jennifer Hill. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge. Pages 109–117 251–265, 345–359, 366–371, 419–421.

    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.

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

    Brooks, S. P. and A. Gelman. 1998. Alternative methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7: 434–455. (in file brooks.gelman.jcgs1998.pdf)

12. measurement: latent variable models (Nov 24, Dec 1)
    • Simon Jackman and Shawn Treier. 2008. Democracy as a Latent Variable. American Journal of Political Science 52 (1): 201–217. (in file jackman.treir.ajps2008.pdf)

    • Joshua Clinton, Simon Jackman and Douglas Rivers. 2004. The Statistical Analysis of Roll Call Data. American Political Science Review 98 (2, May): 355–370. (in file clinton.jackman.rivers.apsr2004.pdf)

    • Kevin McAlister. 2020. Chapter II of “Essays on Latent Variable Models and Roll Call Scaling” (2020 Ph.D. dissertation)
      (in file KevinMcAlisterUMDissFinal.pdf)

    • Quinn, Kevin M. 2004. “Bayesian Factor Analysis for Mixed Ordinal and Continuous Responses.” Political Analysis 12: 338–353. (in file quinn.pa2004.pdf)

    • Karl G. Jöreskog. 1974. “Analyzing Psychological Data by Structural Analysis of Covariance Matrices.” In David H. Krantz, Richard C. Atkinson, R. Duncan Luce and Patrick Suppes, Contemporary Developments in Mathematical Psychology, Vol. II. W. H. Freeman and Company. (in file Joreskog1974c.pdf)

    • Fariss, Christopher J. 2014. “Respect for Human Rights has Improved Over Time: Modeling the Changing Standard of Accountability.” American Political Science Review 108 (2, May): 297–318. (in file Fariss2014APSR.pdf)

    • Walter R. Mebane, Jr. 2024. Election Forensics: A Finite Mixture Model that Estimates Realized Election Frauds. Sections. (in file EFbook_23aug2025.pdf)

    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. (in file shi.lee.jrssb2000.pdf)

    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.

    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.

13. hypothesis tests and model selection (Dec 8)
    • Cameron and Trivedi. Chapter 7. (in file CT7.pdf)

    • Guido W. Imbens and Donald B. Rubin. 2015. Causal Inference for Statistics, Social and Biomedical Sciences: An Introduction. Cambridge. Chapters 5 and 6.

    • Cohen, Peter L. and Fogarty, Colin B. (2022). Gaussian prepivoting for finite population causal inference. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 84 (2): 295–320. (in file Gaussian_Prepivoting.pdf)

    • Benjamin, Daniel J, James O. Berger, et al. 2018. Redefine Statistical Significance. Nature Human Behavior 2, 6–10. https://doi.org/10.1038/s41562-017-0189-z (in file BenjaminEtAlRedefineStatisticalSignificance.pdf)

    • 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.

    • Imai, Kosuke and Tingley, Dustin, 2012. “A statistical method for empirical testing of competing theories.” American Journal of Political Science 56 (1) 218-236.

    Kass, Robert E., and Adrian E. Raftery. 1995. “Bayes factors” Journal of the American Statistical Association 90 (430) : 773–795.

    Chib, S., 2001. “Markov chain Monte Carlo methods: Computation and Inference.” In Handbook of Econometrics (Vol. 5, pp. 3569–3649). Elsevier.
    Section 10: MCMC methods in model choice problems. (in file chib2001.pdf)

14. partial identification and identification with missing covariates (—)
    • Kei Kawai and Yasutora Watanabe. 2013. “Inferring Strategic Voting.” American Economic Review 103 (2): 624–662. (in file kawai2013inferring.pdf)

    • 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.

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

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

    • Walter R. Mebane, Jr. and Paul Poast. 2013. “Causal Inference without Ignorability: Identification with Nonrandom Assignment and Missing Treatment Data.” Political Analysis 21 (2): 233–251.

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

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

    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.

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

15. bounded influence estimation (—)
    • 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.

    • Western, Bruce. 1995. Concepts and Suggestions for Robust Regression Analysis. American Journal of Political Science 39 (3): 786–817.

    • 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.

16. paper presentations (Dec 10 1:30pm–3:30pm)