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].
Further readings of interest:
Useful Links:
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 singlelevel regression models. GH, Part 1A "Singlelevel 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 13:345364 John D. Huber, Georgia Kernell, and Eduardo L. Leoni (2005): Institutional Context, Cognitive Resources and Party Attachments Across Democracies. Political Analysis 13:365386 K. Robinson (1991): That BLUP is a Good Thing: The Estimation of Random Effects. Statistical Science, Vol. 6, No. 1. (Feb., 1991), pp. 1532 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 TimeSeriesCrossSection Data: Monte Carlo Experiments. Political Analysis 15(2):182195
Nathaniel Beck and Jonathan N. Katz (2004): Random Coefficient Models for TimeSeriesCrossSection Data. California Institute of Technology, Social Science Working Paper 1205. See pp. 912 for GLS approach. 
Greene, pp. 289290, 295296
John D. Hey (1983): Data in Doubt, pp. 2729, 116119, 137152 

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. 375404 
Hmwk 01 (the real deal)  WinBUGS:
 Simon Jackman's probit example (right from his web site) data and codebook for Huber et al. (2005) radon data for Pennsylvania (instead of for Minnesota as in GH) 

Week 6: 10/10 (note: no class on Monday 10/8) 
Item Response Theory Models  GH: Chapter 14.2 "Itemresponse and idealpoint models", pp. 31421
Joshua Clinton, Simon Jackman and Douglas Rivers (2004): The Statistical Analysis of Roll Call Data. American Political Science Review. 98(2):355370 (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 "Missingdata imputation", pp. 529543 From Gary King's Missing Data website: 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. 4969 James Honaker and Gary King (2007): What to do about Missing Values in Time Series CrossSection Data. Unpublished Manuscript, September 21 For Time Series Cross Section Issues:
Nathaniel Beck and Jonathan N. Katz (2004): TimeSeriesCrossSection 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 nonstationary 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 TimeSeriesCrossSection Models: The Example of State Failure. Draft of July 12
Nathaniel Beck, Jonathan N. Katz, and Richard Tucker (1998): Taking Time Seriously: TimeSeriesCrossSection Analysis with a Binary Dependent Variable. American Journal of Political Science, Vol. 42, No. 4, pp. 12601288. Symposium on Research Design and Method in International Relations. International Organization, Vol. 55, No. 2. (Spring, 2001):

TimeSeries Issues  Hmwk 03.1  Clarke et al. (2000)
data for Clarke et al. (British approval data for the ThatcherMajor 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. 945960 (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. 281293 Additional readings:


Week 10: 11/5 and 11/7  Causality: Introduction continued  for Monday, November 5:
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. 113132 

Week 11: 11/12 and 11/14  Causality: matching, propensity and regression based methods  for Monday, November 12:

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. 725753 (here via NYU) 

Week 13.1: 11/26  Causality: Mechanisms  Morgan and Winship, Chapters 810
Adam Glynn and Kevin Quinn (2007): Nonparametric 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, 2744 
syntax file and data from lab session  
Week 15: 12/10 and 12/12  Bootstrapping  CT: Chapter 11 