The main textbook for the class is Andrew Gelman and Jennifer Hill (2007): Data Analysis Using Regression and Multilevel/Hierarchical Models. [GH]
We additionally use Greene (2003): Econometric Analysis, 5th edition [Greene] and Cameron and Trivedi (2005): Microeconometrics [CT].

• Bayesian data analysis :
• Andrew Gelman, John B. Carlin, Hal S. Stern, and Donald B. Rubin (2004): Bayesian Data Analysis. 2nd ed. CRC Press.
• Dalene K. Stangl and Donald A. Berry (eds.) (2000): Meta-analysis in medicine and health policy. New York : M. Dekker.
[electronic access available through NYU library]

Schedule
Date Topic Reading in text Handout (if any) Exercise (if any) Data, syntax files, anything else of use
Week 1: 9/5 Introductory discussion - what do we want to learn? Beginning discussion of hierarchical modeling Review of single-level regression models. GH, Part 1A "Single-level regression"
Week 2: 9/10 and 9/12 No classes. Install R and WinBUGS and learn how to use the software. To learn R: "An Introduction to R" available here. Look at Intro, Simple Manipulations, QUICKLY glance at (but do not master) Object and Factors, then do Arrays, Lists, Reading Data, Prob Dists, look at loops (you will do this later), skip writing own fns for now, then do some stats and graphs examples, then look at packages, make sure you can deal with a sample session and invoking R.

GH: Read and work the examples in 11 and 12. Once you are done with that, work the examples (and only work the examples) in 16 and 17 using Bugs.

Week 3: 9/17 and 9/19 Multilevel analysis: fixed effects and random effects models GH, Part 2A "Multilevel regression"

Jeffrey B. Lewis and Drew A. Linze (2005): Estimating Regression Models in Which the Dependent Variable Is Based on Estimates. Political Analysis 13:345-364

John D. Huber, Georgia Kernell, and Eduardo L. Leoni (2005): Institutional Context, Cognitive Resources and Party Attachments Across Democracies. Political Analysis 13:365-386

K. Robinson (1991): That BLUP is a Good Thing: The Estimation of Random Effects. Statistical Science, Vol. 6, No. 1. (Feb., 1991), pp. 15-32

Greene, Chapter 13 "Models for Panel Data" and/or CT, Chapter V "Models for Panel Data"

Hmwk 01

example solution (for Huber et al. data only)

data and codebook for Huber et al. (2005)
Week 4: 9/24 and 9/26 Multilevel analysis continued Nathaniel Beck and Jonathan N. Katz (2007): Random Coefficient Models for Time-Series-Cross-Section Data: Monte Carlo Experiments. Political Analysis 15(2):182-195

Nathaniel Beck and Jonathan N. Katz (2004): Random Coefficient Models for Time-Series-Cross-Section Data. California Institute of Technology, Social Science Working Paper 1205. See pp. 9-12 for GLS approach.

Greene, pp. 289-290, 295-296

John D. Hey (1983): Data in Doubt, pp. 27-29, 116-119, 137-152

Week 5: 10/1 and 10/3 Multilevel analysis: computational bayes - MCMC Simon Jackman's book draft "Bayesian Analysis for the Social Sciences". May 27, 2007. (distributed by email)

Simon Jackman (2000): Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo. American Journal of Political Science, Vol. 44, No. 2, pp. 375-404

Hmwk 01 (the real deal) WinBUGS:
- Simon Jackman's probit example (right from his web site)

data and codebook for Huber et al. (2005)

Week 6: 10/10

(note: no class on Monday 10/8)

Item Response Theory Models GH: Chapter 14.2 "Item-response and ideal-point models", pp. 314-21

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

Shawn Treier and Simon Jackman (2004): Democracy as a Latent Variable. American Journal of Political Science, forthcoming

Hmwk 02 WinBugs files from Simon Jackman"s website:
- legislators.odc to estimate legislative ideal points from roll call data
- judges.odc to estimate ideological locations of Supreme Court justices via analysis of decisions

- Neal's updater file (see hmwk for instructions)

- "WinBUGS - The Movie!", a "short Flash illustration of the basic steps of running WinBUGS."

Week 7: 10/15 and 10/17 Missing Data Imputation

Time Series Cross Section Issues

For Missing Data Imputation:
GH: Chapter 25 "Missing-data imputation", pp. 529-543

Gary King, James Honaker, Anne Joseph, and Kenneth Scheve (2001): Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation, American Political Science Review, Vol. 95, No. 1, pp. 49-69

James Honaker and Gary King (2007): What to do about Missing Values in Time Series Cross-Section Data. Unpublished Manuscript, September 21

For Time Series Cross Section Issues:
Greene: Chapters 12 "Serial Correlation", 19 "Models with Lagged Variables", 20 "Time-Series Models"

Nathaniel Beck and Jonathan N. Katz (2004): Time-Series-Cross-Section Issues: Dynamics, 2004. Draft of July 24, 2004

Lag polynomials handout (corrected version!)
Week 8: 10/22 and 10/24 Time Series Cross Section Issues continued same as previous week same as previous week

Models for non-stationary series - a very brief, albeit useful, intro (new!)

Week 9.1: 10/29 Binary TSCS and Transition Models Nathaniel Beck, David Epstein, Simon Jackman, and Sharyn O'Halloran (2002): Alternative Models of Dynamics in Binary Time-Series-Cross-Section Models: The Example of State Failure. Draft of July 12

Nathaniel Beck, 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, Vol. 42, No. 4, pp. 1260-1288.

Symposium on Research Design and Method in International Relations. International Organization, Vol. 55, No. 2. (Spring, 2001):

• Donald P. Green, Soo Yeon Kim, and David H. Yoon: Dirty Pool. pp. 441-468 (here via Jstor)

• Nathaniel Beck and Jonathan N. Katz: Throwing out the Baby with the Bath Water: A Comment on Green, Kim, and Yoon. pp. 487-495 (here via Jstor)
Daron Acemoglu, Simon Johnson, James A. Robinson, and Pierre Yared (2007): Reevaluating the Modernization Hypothesis. Unpublished Manuscript, August
Time-Series Issues Hmwk 03.1 Clarke et al. (2000)

data for Clarke et al. (British approval data for the Thatcher-Major period)

data for MacKuen et al.

Week 9.2 (10/31) Causality: Introduction Paul W. Holland (1986): Statistics and Causal Inference. Journal of the American Statistical Association, Vol. 81, No. 396, pp. 945-960 (here via Jstor)
(copies provided)

Jasjeet S. Sekhon (2004): Quality Meets Quantity: Case Studies, Conditional Probability, and Counterfactuals. Perspectives on Politics, Vol. 2, No. 2, pp. 281-293
(copies provided)

Week 10: 11/5 and 11/7 Causality: Introduction continued for Monday, November 5:
• GH: Chapter 9 "Causal inference using regression on the treatment variable" and Chapter 10 "Causal inference using more advanced models"

• CT: Chapter 25 "Treatment Evaluation"
for Wednesday, November 7:
• Stephen L. Morgan and Christopher Winship (2007): Counterfactuals and Causal Inference: Methods and Principles for Social Research. Cambridge. Part 1 & 2

Alberto Abadie and Javier Gardeazabal (2003): The Economic Costs of Conflict: A Case Study of the Basque Country. The American Economic Review, Vol. 93, No. 1., pp. 113-132

Week 11: 11/12 and 11/14 Causality: matching, propensity and regression based methods for Monday, November 12:
• Morgan and Winship, Part 1 & 2
for Wednesday, November 14:
• Morgan and Winship, Chapters 4 & 5
• Gary King and Langche Zeng (2005): The Dangers of Extreme Counterfactuals. Political Analysis 14: 131-159
• (you only need to start on the bottom of p.151 and continue until the top of p.153, with the proof in footnote 16)
Hmwk 4 R code and dataset from section

replication material from Michael J. Gilligan and Ernest J. Sergenti (2007): Do UN Interventions Cause Peace? Using Matching to Improve Causal Inference. Unpublished Manuscirpt. October 31.

Week 12: 11/19 Causality: Instrumental Variables Morgan and Winship, Chapters 6 & 7

Edward Miguel, Shanker Satyanath, and Ernest Sergenti (2004): Economic Shocks and Civil Conflict: An Instrumental Variables Approach. Journal of Political Economy, Vol. 112, No. 4, pp. 725-753 (here via NYU)
(copies provided)

Week 13.1: 11/26 Causality: Mechanisms Morgan and Winship, Chapters 8-10

Adam Glynn and Kevin Quinn (2007): Non-parametric Mechanisms and Causal Modeling. Unpublished Manuscript, July 15

Week 13.2: 11/28 Measurement Error CT: Chapter 26
Week 14: 12/3 and 12/5 Spatial Econometrics Michael D. Ward and Kristian Skrede Gleditsch (2007): An Introduction to Spatial Regression Models in the Social Sciences. Book draft, June 9
(copies provided)

Nathaniel Beck, Kristian Skrede Gleditsch, and Kyle Beardsley (2006): Space Is More than Geography: Using Spatial Econometrics in the Study of Political Economy. International Studies Quarterly 50, 27-44
(copies provided)

syntax file and data from lab session
Week 15: 12/10 and 12/12 Bootstrapping CT: Chapter 11