
1 
Paper

Did Illegally Counted Overseas Absentee Ballots Decide the 2000 U.S. Presidential Election?
Imai, Kosuke
King, Gary

Uploaded 
02132002

Keywords 
2000 U.S. Presidential Election Ecological Inference Bayesian Model Averaging

Abstract 
Although not widely known until much later, Al Gore received 202
more votes than George W. Bush on election day in Florida. George
W. Bush is president because he overcame his election day deficit
with overseas absentee ballots that arrived and were counted after
election day. In the final official tally, Bush received 537 more
votes than Gore. These numbers are taken from the official results
released by the Florida Secretary of State's office and so do not
reflect overvotes, undervotes, unsuccessful litigation, butterfly
ballot problems, recounts that might have been allowed but were not,
or any other hypothetical divergence between voter preferences and
counted votes. After the election, the New York Times conducted a
six month long investigation and found that 680 of the overseas
absentee ballots were illegally counted, and no partisan, pundit,
or academic has publicly disagreed with their assessment.
In this paper, we describe the statistical procedures we developed
and implemented for the Times to ascertain whether disqualifying
these 680 ballots would have changed the outcome of the election.
The methods involve adding formal Bayesian model averaging procedures
to King's (1997) ecological inference model. Formal Bayesian model
averaging has not been used in political science but is especially
useful when substantive conclusions depend heavily on apparently
minor but indefensible model choices, when model generalization is
not feasible, and when potential critics are more partisan than
academic. We show how we derived the results for the Times so that
other scholars can use these methods to make ecological inferences
for other purposes. We also present a variety of new empirical
results that delineate the precise conditions under which Al Gore
would have been elected president, and offer new evidence of the
striking effectiveness of the Republican effort to convince local
election officials to count invalid ballots in Bush counties and not
count them in Gore counties. 

2 
Paper

Testing Theories Involving Strategic Choice: The Example of Crisis Escalation
Smith, Alastair

Uploaded 
07231997

Keywords 
Strategic choice Bayesian model testing Markov chain Monte Carlo simulation multivariate probit crisis escalation war

Abstract 
If we believe that politics involves a significant
amount of strategic interaction then classical
statistical tests, such as Ordinary Least Squares,
Probit or Logit, cannot give us the right answers.
This is true for two reasons: The dependent variables
under observation are interdependent that is the
essence of game theoretic logic and the data is
censored  that is an inherent feature of off the
path expectations that leads to selection effects.
I explore the consequences of strategic decision making
on empirical estimation in the context of international
crisis escalation. I show how and why classical
estimation techniques fail in strategic settings.
I develop a simple strategic model of decision making
during crises. I ask what this explanation implies about
the distribution of the dependent variable: the level of
violence used by each nation. Counterfactuals play a key
role in this theoretical explanation. Yet, conventional
econometric techniques take no account of unrealized
opportunities. For example, suppose a weak nation (B) is
threatened by a powerful neighbor (A). If we believe that
power strongly influences the use of force then the weak
nation realizes that the aggressor's threats are probably
credible. Not wishing to fight a more powerful opponent,
nation B is likely to acquiesce to the aggressor's demands.
Empirically, we observe A threaten B. The actual level of
violence that A uses is low. However, the theoretical model
suggests that B acquiesced precisely because A would use force.
Although the theoretical model assumes a strong relationship
between strength and the use of force, traditional techniques
find a much weaker relationship. Our ability to observe whether
nation A is actually prepared to use force is censored when nation
B acquiesces. I develop a Strategically Censored Discrete Choice
(SCDC) model which accounts for the interdependent and censored
nature of strategic decision making. I use this model to test
existing theories of dispute escalation. Specifically, I analyze
Bueno de Mesquita and Lalman's (1992) dyadically coded version of
the Militarized Interstate Dispute data (Gochman and Moaz 1984).
I estimate this model using a Bayesian Markov chain Monte Carlo
simulation method. Using Bayesian model testing, I compare the
explanatory power of a variety of theories. I conclude that strategic
choice explanations of crisis escalation far outperform nonstrategic
ones. 

4 
Paper

Penalized Regression, Standard Errors, and Bayesian Lassos
Kyung, Minjung
Gill, Jeff
Ghosh, Malay
Casella, George

Uploaded 
02232010

Keywords 
model selection lassos Bayesian hierarchical models LARS algorithm EM/Gibbs sampler Geometric Ergodicity Gibbs Sampling

Abstract 
Penalized regression methods for simultaneous variable selection and coefficient estimation, especially those based on the lasso of Tibshirani (1996), have received a great deal of attention in recent years, mostly through frequentist models. Properties such as consistency have been studied, and are achieved by different lasso variations. Here we look at a fully Bayesian formulation of the problem, which is flexible enough to encompass most versions of the lasso that have been previously considered. The advantages of the hierarchical Bayesian formulations are many. In addition to the usual easeofinterpretation of hierarchical models, the Bayesian formulation produces valid standard errors (which can be problematic for the frequentist lasso), and is based on a geometrically ergodic Markov chain. We compare the performance of the Bayesian lassos to their frequentist counterparts using simulations and data sets that previous lasso papers have used, and see that in terms of prediction mean squared error, the Bayesian lasso performance is similar to and, in some cases, better than, the frequentist lasso. 

5 
Paper

Random Coefficient Models for TimeSeriesCrossSection Data: The 2001 Version
Beck, Nathaniel
Katz, Jonathan

Uploaded 
07172001

Keywords 
random coefficients generalized least squares empirical Bayesian Steinrule TCSC

Abstract 
This paper considers random coefficient models (RCMs) for
timeseriescrosssection data. These models allow for unit to unit
variation in the model parameters. After laying out the various
models, we assess several issues in specifying RCMs. We then consider
the finite sample properties of some standard RCM estimators, and show
that the most common one, associated with Hsaio, has very poor
properties. These analyses also show that a somewhat awkward
combination of estimators based on Swamy's work performs reasonably
well; this awkward estimator and a Bayes estimator with an
uninformative prior (due to Smith) seem to perform best. But we also
see that estimators which assume full pooling perform well unless
there is a large degree of unit to unit parameter heterogeneity. We
also argue that the various data driven methods (whether classical or
empirical Bayes or Bayes with gentle priors) tends to lead to much
more heterogeneity than most political scientists would like. We
speculate that fully Bayesian models, with a variety of informative
priors, may be the best way to approach RCMs. 

7 
Paper

The Spatial Probit Model of Interdependent Binary Outcomes: Estimation, Interpretation, and Presentation
Franzese, Robert
Hays, Jude

Uploaded 
07202007

Keywords 
Spatial Probit Bayesian GibbsSampler Estimator Recursive ImportanceSampling Estimator Interdependence Diffusion Contagion Emulation

Abstract 
We have argued and shown elsewhere the ubiquity and prominence of spatial interdependence in political science research and noted that much previous practice has neglected this interdependence or treated it solely as nuisance to the serious detriment of sound inference. Previously, we considered only linearregression models of spatial and/or spatiotemporal interdependence. In this paper, we turn to binaryoutcome models. We start by stressing the ubiquity and centrality of interdependence in binary outcomes of interest to political and social scientists and note that, again, this interdependence has been ignored in most contexts where it likely arises and that, in the few contexts where it has been acknowledged, the endogeneity of the spatial lag has not be recognized. Next, we explain some of the severe challenges for empirical analysis posed by spatial interdependence in binaryoutcome models, and then we follow recent advances in the spatialeconometric literature to suggest Bayesian or recursiveimportancesampling (RIS) approaches for tackling estimation. In brief and in general, the estimation complications arise because among the RHS variables is an endogenous weighted spatiallag of the unobserved latent outcome, y*, in the other units; Bayesian or RIS techniques facilitate the complicated nested optimization exercise that follows from that fact. We also advance that literature by showing how to calculate estimated spatial effects (as opposed to parameter estimates) in such models, how to construct confidence regions for those (adopting a simulation strategy for the purpose), and how to present such estimates effectively. 

8 
Paper

No News is News: NonIgnorable NonResponse in RollCall Data Analysis
Rosas, Guillermo
Shomer, Yael
Haptonstahl, Stephen

Uploaded 
07102010

Keywords 
rollcall voting abstention missing Bayesian IRT

Abstract 
Rollcall votes are widely employed to infer the ideological proclivities of legislators, even though inferences based on rollcall data are accurate reflections of underlying policy preferences only under stringent assumptions. We explore the consequences of violating one such assumption, namely, the ignorability of the process that generates nonresponse in roll calls. We offer a reminder of the inferential consequences of ignoring certain processes of nonresponse, a basic estimation framework to model nonresponse and vote choice concurrently, and models for two theoretically relevant processes of nonignorable missingness. We reconsider the "most liberal Senator" question that comes up during election times every four years in light of our arguments and show how we inferences about ideal points can improve by incorporating a priori information about the process that generates abstentions. 

9 
Paper

PreElection Polls in Nation and State: A Dynamic Bayesian Hierarchical Model
Franklin, Charles

Uploaded 
07172001

Keywords 
campaigns polling aggregation Bayesian hierarchical models

Abstract 
A vast number of national trial heat polls are conducted in the months
preceding a presidential election. But as was dramatically demonstrated
in 2000, candidates must win states to win the presidency, not just win
popular votes. The density of state level polling is much less than
that for the nation as a whole. This makes efforts to track candidate
support at the state level, and to estimate campaign effects in the
states, very difficult.
This paper develops a Bayesian hierarchical model of trial heat polls
which uses state and national polling data, plus measures of campaign
effort in each state, to estimate candidate support between observed
state polls.
At a technical level, the Bayesian approach provides not only estimates
of support but also easily understood estimates of the uncertainty of
those estimates.
At an applied level, this method can allow campaigns to target polling
in states that are most likely to be changing while being alerted to
potential shifts in states that are not as frequently polled. 

10 
Paper

Recent Developments in Econometric Modelling: A Personal Viewpoint
Maddala, G.S.

Uploaded 
07171997

Keywords 
dynamic panel data models dynamic models with limited dependent variables unit roots cointegration VAR's Bayesian

Abstract 
The quotation above (more than three thousand years ago) essentially summarizes my perception of what is going on in econometrics.
Dynamic economic modelling is a comprehensive term. It covers everything except pure crosssection analysis. Hence, I have to narrow down the scope of my paper. I shall not cover duration models, event studies, count data and Markovian models. The areas covered are: dynamic panel data models, dynamic models with limited dependent variables, unit roots, cointegration, VAR’s and Bayesian approaches to all these problems. These are areas I am most familiar with. Also, the paper is not a survey of recent developments. Rather, it presents what I feel are important issues in these areas. Also, as far as possible, I shall relate the issues with those considered in the work on Political Methodology.
I have a rather different attitude towards econometric methods which my own colleagues in the profession may not share. In my opinion, there is too much technique and not enough discussion of why we are doing what we are doing. I am often reminded of the admonition of the queen to Pollonius in Shakespeare’s Hamlet, “More matter, less art.” 

11 
Paper

A default prior distribution for logistic and other regression models
Gelman, Andrew
Jakulin, Aleks
Pittau, Maria Grazia
Su, YuSung

Uploaded 
08032007

Keywords 
Bayesian inference generalized linear model least squares hierarchical model linear regression logistic regression multilevel model noninformative prior distribution

Abstract 
We propose a new prior distribution for classical (nonhierarchical) logistic regression models, constructed by first scaling all nonbinary variables to have mean 0 and standard deviation 0.5, and then placing independent Student$t$ prior distributions on the coefficients. As a default choice, we recommend the Cauchy distribution with center 0 and scale 2.5, which in the simplest setting is a longertailed version of the distribution attained by assuming onehalf additional success and onehalf additional failure in a logistic regression. We implement a procedure to fit generalized linear models in R with this prior distribution by incorporating an approximate EM algorithm into the usual iteratively weighted least squares. We illustrate with several examples, including a series of logistic regressions predicting voting preferences, an imputation model for a public health data set, and a hierarchical logistic regression in epidemiology.
We recommend this default prior distribution for routine applied use. It has the advantage of always giving answers, even when there is complete separation in logistic regression (a common problem, even when the sample size is large and the number of predictors is small) and also automatically applying more shrinkage to higherorder interactions. This can be useful in routine data analysis as well as in automated procedures such as chained equations for missingdata imputation. 

13 
Paper

Time Series CrossSectional Analyses with Different Explanatory Variables in Each CrossSection
Girosi, Federico
King, Gary

Uploaded 
07112001

Keywords 
Bayesian hierarchical model time series crosssection

Abstract 
The current animosity between quantitative crossnational
comparativists and area studies scholars originated in the expanding
geographic scope of data collection in the 1960s. As quantitative
scholars sought to include more countries in their regressions, the
measures they were able to find for all observations became less
comparable, and those which were available (or appropriate) for
fewer than the full set were excluded. Area studies scholars
appropriately complain about the violence these procedures do to the
political reality they find from their in depth analyses of
individual countries, but as quantitative comparativists continue to
seek systematic comparisons, the conflict continues. We attempt to
eliminate a small piece of the basis of this conflict by developing
models that enable comparativists to include different explanatory
variables, or the same variables with different meanings, in the
timeseries regression in each country. This should permit more
powerful statistical analyses and encourage more contextsensitive
data collection strategies. We demonstrate the advantages of this
approach in practice by showing how outofsample forecasts of
mortality rates in 25 countries, 17 age groups, and 17 causes of
death in males and 20 in females from this model outperform a
standard regression approach. 

14 
Paper

Multilevel (hierarchical) modeling: what it can and can't do
Gelman, Andrew

Uploaded 
01262005

Keywords 
Bayesian inference hierarchical model multilevel regression

Abstract 
Multilevel (hierarchical) modeling is a generalization of linear and generalized linear modeling in which regression coefficients are themselves given a model, whose parameters are also estimated from data. We illustrate the strengths and limitations of multilevel modeling through an example of the prediction of home radon levels in U.S. counties. The multilevel model is highly effective for predictions at both levels of the model but could easily be misinterpreted for
causal inference. 

15 
Paper

Bayesian Model Averaging: Theoretical developments and practical applications
Montgomery, Jacob
Nyhan, Brendan

Uploaded 
01222008

Keywords 
Bayesian model averaging BMA model robustness specification uncertainty

Abstract 
Political science researchers typically conduct an idiosyncratic search of possible model configurations and then present a single specification to readers. This approach systematically understates the uncertainty of our results, generates concern among readers and reviewers about fragile model specifications, and leads to the estimation of bloated models with huge numbers of controls. Bayesian model averaging (BMA) offers a systematic method for analyzing specification uncertainty and checking the robustness of one's results to alternative model specifications. In this paper, we summarize BMA, review important recent developments in BMA research, and argue for a different approach to using the technique in political science. We then illustrate the methodology by reanalyzing models of voting in U.S. Senate elections and international civil war onset using software that respects statistical conventions within political science. 

16 
Paper

Measuring Political Support and Issue
Ownership Using Endorsement Experiments,
with Application to the Militant Groups
in Pakistan
Bullock, Will
Imai, Kosuke
Shapiro, Jacob

Uploaded 
07182010

Keywords 
endorsement experiment survey experiment bayesian pakistan militant groups issue ownership social desirability

Abstract 
To measure the levels of support for political actors (e.g., candidates and parties) and the strength of their issue ownership, survey experiments are often conducted in which respondents are asked to express their opinion about a particular policy endorsed by a randomly selected political actor. These responses are contrasted with those from a control group that receives no endorsement. This survey methodology is particularly useful for studying sensitive political attitudes. We develop a Bayesian hierarchical measurement model for such endorsement experiments, demonstrate its statistical properties through simulations, and use it to measure support for Islamist militant groups in Pakistan. Our model uses item response theory to estimate support levels on the same scale as the ideal points of respondents. The model also estimates the strength of political actors' issue ownership for specic policies as well as the relationship between respondents' characteristics and support levels. Our analysis of a recent survey experiment in Pakistan reveals three key patterns. First, citizens' attitudes towards militant groups are geographically clustered. Second, once these regional differences are taken into account, respondents' characteristics have little predictive power for their support levels. Finally, militant groups tend to receive less support in the areas where they operate. 

17 
Paper

Flexible Prior Specifications for Factor Analytic Models with an Application to the Measurement of American Political Ideology
Quinn, Kevin M.

Uploaded 
04202000

Keywords 
factor analysis intrinsic autoregression hierarchical modeling Bayesian inference political ideology

Abstract 
Factor analytic measurement models are widely used in the social
sciences to measure latent variables and functions thereof. Examples
include the measurement of: political preferences, liberal democracy,
latent determinants of exchange rates, and latent factors in arbitrage
pricing theory models and the corresponding pricing deviations.
Oftentimes, the results of these measurement models are sensitive to
distributional assumptions that are made regarding the latent factors.
In this paper I demonstrate how prior distributions commonly used in
image processing and spatial statistics provide a flexible means to
model dependencies among the latent factor scores that cannot be
easily captured with standard prior distributions that treat the
factor scores as (conditionally) exchangeable. Markov chain Monte
Carlo techniques are used to fit the resulting models. These modeling
techniques are illustrated with a simulated data example and an
analysis of American political attitudes drawn from the 1996 American
National Election Study. 

20 
Paper

Seven Deadly Sins of Contemporary Quantitative Political Analysis
Schrodt, Philip

Uploaded 
08232010

Keywords 
collinearity prediction explanation Bayesian frequentist control variables pedagogy philosophy of science logical positivists significance test Hempel Thor

Abstract 
A combination of technological change, methodological drift and a certain degree of
intellectual sloth and sloppiness, particularly with respect to philosophy of science,has allowed contemporary quantitative political analysis to accumulate a series of dysfunctional habits that have rendered a great deal of contemporary research more or less scientifically useless. The cure for this is not to reject quantitative methods  and the cure is most certainly not a postmodernist nihilistic rejection of all systematic method  but rather to return to some fundamentals, and take on some
hard problems rather than expecting to advance knowledge solely through the everincreasing
application of fasttwitch muscle fibers to computer mice.
In this paper, these "seven deadly sins" are identified as
1. Kitchen sink models that ignore the effects of collinearity;
2. Prescientific explanation in the absence of prediction;
3. Reanalyzing the same data sets until they scream;
4. Using complex methods without understanding the underlying assumptions;
5. Interpreting frequentist statistics as if they were Bayesian;
6. Linear statistical monoculture at the expense of alternative structures;
7. Confusing statistical controls and experimental controls.
The answer to these problems is solid, thoughtful, original work driven by an appreciation of both theory and data. Not postmodernism. The paper closes with a review
of how we got to this point from the perspective of 17th through 20th century philosophy of science, and provides suggestions for changes in philosophical and pedagogical approaches that might serve to correct some of these problems. 

23 
Paper

Making Inferences from 2x2 Tables: The Inadequacy of the Fisher Exact\r\nTest for Observational Data and a Principled Bayesian Alternative
Sekhon, Jasjeet

Uploaded 
08172005

Keywords 
Fisher exact test randomization inference permutation
tests Bayesian tests difference of proportions observational data

Abstract 
The Fisher exact test is the dominant method of making inferences from
2x2 tables where the number of observations is small. Although the
Fisher test and approximations to it are used in a large number of
studies, these tests rest on a data generating process which is
inappropriate for most applications for which they are used. The
canonical Fisher test assumes that both of the margins in a 2x2 table
are fixed by constructioni.e., both the treatment and outcome
margins are fixed a priori. If the data were generated by an
alternative process, such as binomial, negative binomial or Poisson
binomial sampling, the Fisher exact test and approximations to it do
not have correct coverage. A Bayesian method is offered which has
correct coverage, is powerful, is consistent with a binomial process
and can be extended easily to other distributions. A prominent 2x2
table which has been used in the literature by Geddes (1990) and
Sekhon (2004) to explore the relationship between foreign threat and
social revolution (Skocpol, 1979) is reanalyzed. The Bayesian method
finds a significant relationship even though the Fisher and related
tests do not. A Monte Carlo sampling experiment is provided which
shows that the Bayesian method dominates the usual alternatives in
terms of both test coverage and power when the data are generated by a
binomial process. 

24 
Paper

Why we (usually) don't have to worry about multiple comparisons
Gelman, Andrew
Hill, Jennifer
Yajima, Masanao

Uploaded 
06012008

Keywords 
Bayesian inference hierarchical modeling multiple comparisons type S error statistical significance

Abstract 
The problem of multiple comparisons can disappear when viewed from a Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise. These address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low grouplevel variation, which is where multiple comparisons are a particular concern.
Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting
for multiple comparisons by making the intervals wider (or, equivalently, adjusting the pvalues corresponding to intervals of fixed width). Multilevel estimates make comparisons more conservative, in the sense that intervals for comparisons are more likely to include zero; as a result, those comparisons that are made with confidence are more likely to be valid. 

25 
Paper

A Statistical Method for Empirical Testing of Competing Theories
Imai, Kosuke
Tingley, Dustin

Uploaded 
08242010

Keywords 
EITM finite mixture model Bayesian statistics multiple testing false discovery rate EM algorithm

Abstract 
Empirical testing of competing theories lies at the heart of social science research. We demonstrate that a very general and wellknown class of statistical models, called finite mixture models, provides an effective way of rival theory testing. In the proposed framework, each observation is assumed to be generated from a statistical model implied by one of the theories under consideration. Researchers can then estimate the probability that a specific observation is consistent with either of the competing theories. By directly modeling this probability with the characteristics of observations, one can also determine the conditions under which a particular theory applies. We discuss a principled way to identify a list of observations that are statistically significantly consistent with each theory. Finally, we propose several measures of the overall performance of a particular theory. We illustrate the advantages of our method by applying it to an influential study on trade policy preferences. 

26 
Paper

The Insignificance of Null Hypothesis Significance Testing
Gill, Jeff

Uploaded 
02061999

Keywords 
hypothesis testing inverse probability Fisher NeymanPearson Bayesian approaches confidence sets metaanalysis

Abstract 
The current method of hypothesis testing in the social sciences is under intense criticism
yet most political scientists are unaware of the important issues being
raised. Criticisms focus on the construction and interpretation of a
procedure that has dominated the reporting of empirical results for over fifty years.
There is evidence that null hypothesis significance testing as practiced in political
science is deeply flawed and widely misunderstood. This is important since most
empirical work in political science argues the value of findings through the use of
the null hypothesis significance test.
In this article I review the history of the null hypothesis significance
testing paradigm in the social sciences and discuss major problems, some of which
are logical inconsistencies while others are more interpretive in nature. I suggest
alternative techniques to convey effectively the importance of dataanalytic findings.
These recommendations are illustrated with examples using empirical
political science publications. 

27 
Paper

Identifying IntraParty Voting Blocs in the UK House of Commons
Quinn, Kevin
Spirling, Arthur

Uploaded 
07192005

Keywords 
rollcall analysis UK House of Commons Bayesian nonparametrics Dirichlet process mixtures

Abstract 
Legislative voting records are an important source of information about
legislator preferences, intraparty cohesiveness, and the divisiveness
of various policy issues. Standard methods of analyzing a legislative
voting record tend to have serious drawbacks when applied to
legislatures, such as the UK House of Commons, that feature highly
disciplined parties, strategic voting, and large amounts of missing
data. We present a method (based on a Dirichlet process mixture model)
for analyzing such voting records that does not suffer from these same
problems. We apply the method to the voting records of Labour and
Conservative Party MPs in the 19972001 session of the UK House of
Commons. Our method has a number of advantages over existing approaches.
It is modelbased and thus allows one to make probability statements
about quantities of interest. It allows one to estimate the number of
voting blocs within a party or any other group of MPs. It handles
missing data in a principled fashion and does not rely on an ad hoc
distance metric between voting profiles. Finally, it can be used as both
a predictive model and an exploratory model. We illustrate these points
in our analysis of the UK data. 

30 
Paper

Estimation and Inference by Bayesian Simulation: an online resource for social scientists
Jackman, Simon

Uploaded 
08301999

Keywords 
Markov chain Monte Carlo Bayesian statistics howto BUGS ordinal probit time series

Abstract 
http://tamarama.stanford.edu/mcmc
a Webbased online resource for Markov chain Monte Carlo,
specifically tailored for social scientists.
MCMC is probably the most exciting development in statistics in the
last ten years. But to date, most applications of MCMC methods are in
biostatistics, making it difficult for social scientists to fully
grasp the power of MCMC methods. In providing this online resource I
aim to overcome this deficiency, helping to put MCMC in the reach of
social scientists.
The resource comprises:
(*) a set of worked examples
(*) data and programs
(*) links to other relevant web sites
(*) notes and papers
At the meetings in Atlanta, I will present two of the worked examples,
which are part of this document:
(*) Cosponsor: computing auxiliary quantities from MCMC output (e.g.,
percent correctly predicted in a logit/probit model of legislative
behavior; cf Herron 1999).
(*) Delegation: estimating a timeseries model for ordinal data
(e.g., changes to the U.S. president's discretionary power in trade
policy, 18901990; cf Epstein and O'Halloran 1996). 

31 
Paper

Democracy as a Latent Variable
Treier, Shawn
Jackman, Simon

Uploaded 
07162003

Keywords 
democracy Polity measurement latent variables Bayesian statistics itemresponse model ordinal data latent class analysis democratic peace Markov chain Monte Carlo

Abstract 
Measurement is critical to the social scientific enterprise. Many key concepts in socialscientific theories are not observed
directly, and researchers rely on assumptions (tacitly or explicitly, via formal measurement models) to operationalize these concepts in empirical work. In this paper we apply formal, statistical measurement models to the Polity indicators of democracy and autocracy, used widely in studies of international relations. In so doing, we make explicit the hitherto implicit assumptions underlying scales built
using the Polity indicators. We discuss two models: one in which democracy is operationalized as a latent continuous variable, and another in which democracy is operationalized as a latent class. Our modeling approaches allow us to assess the measurement error in the resulting measure of democracy. We show that this measurement error is considerable, and has substantive
consequences when using a measure of democracy as an independent variable in crossnational statistical analysis. Our analysis suggests that skepticism as to the precision of the Polity democracy scale is wellfounded, and that many researchers have been overly sanguine about the properties of the Polity democracy scale in applied statistical work. 

32 
Paper

Nonparametric Priors For Ordinal Bayesian Social Science Models: Specification and Estimation
Gill, Jeff
Casella, George

Uploaded 
08212008

Keywords 
generalized linear mixed model ordered probit Bayesian approaches nonparametric priors Dirichlet process mixture models nonparametric Bayesian inference

Abstract 
A generalized linear mixed model, ordered probit, is used to estimate levels of stress in presidential political appointees as a means of understanding their surprisingly short tenures. A Bayesian approach is developed, where the random effects are modeled with a Dirichlet process mixture prior, allowing for useful incorporation of prior information, but retaining some vagueness in the form of the prior. Applications of Bayesian models in the social sciences are typically done with ``noninformative'' priors, although some use of informed versions exists. There has been disagreement over this, and our approach may be a step in the direction of satisfying both camps. We give a detailed description of the data, show how to implement the model, and describe some interesting conclusions. The model utilizing a nonparametric prior fits better and reveals more information in the data than standard approaches. 

33 
Paper

Modeling Electoral Coordination: Voters, Parties and Legislative Lists in Uruguay
Levin, Ines
Katz, Gabriel

Uploaded 
04202011

Keywords 
electoral coordination number of parties Bayesian estimation multilevel modeling strategic voting

Abstract 
During each electoral period, the strategic interaction between voters and political elites determines the number of viable candidates in a district. In this paper, we implement a hierarchical seemingly unrelated regression model to explain electoral coordination at the district level in Uruguay as a function of district magnitude, previous electoral outcomes and electoral regime. Elections in this country are particularly useful to test for institutional effects on the coordination process due to the large variations in district magnitude, to the simultaneity of presidential and legislative races held under different rules, and to the reforms implemented during the period under consideration. We find that district magnitude and electoral history heuristics have substantial effects on the number of competing and votedfor parties and lists. Our modeling approach uncovers important interactioneffects between the demand and supply side of the political market that were often overlooked in previous research. 

35 
Paper

Candidate Viability and Voter Learning in the Presidential Nomination Process
Paolino, Philip

Uploaded 
08301999

Keywords 
beta distribution maximum likelihood heterogeneity Bayesian

Abstract 
Candidates' viability and momentum are important features of the
presidential nomination process in the United States, and much work
has examined how both influence the outcome of the nomination campaign
(e.g. Aldrich 1980a, Aldrich 1980b, Bartels 1988, Brady and Johnston
1987) Previous treatments, however, have focused upon candidates'
expectations of winning or losing the nomination. A critical feature
that has been mentioned, but not addressed directly is the volatility
of these expectations. In this paper, I use a view of viability and
momentum that considers both expectations and the variance of the
public's perceptions about candidates' viability which allows us to
examine how voters use new information to update their beliefs about
both elements of candidates' viability and provides a basis for
assessing candidates' potential momentum. 

38 
Paper

Bayesian Measures of Explained Variance and Pooling in Multilevel (Hierarchical) Models
Gelman, Andrew
Pardoe, Iain

Uploaded 
04162004

Keywords 
adjusted Rsquared Bayesian inference hierarchical model multilevel regression partial pooling shrinkage

Abstract 
Explained variance (R2) is a familiar summary of the fit of a linear
regression and has been generalized in various ways to multilevel
(hierarchical) models. The multilevel models we consider in this paper
are characterized by hierarchical data structures in which individuals
are grouped into units (which themselves might be further grouped into
larger units), and there are variables measured on individuals and each
grouping unit. The models are based on regression relationships at
different levels, with the first level corresponding to the individual
data, and subsequent levels corresponding to betweengroup regressions
of individual predictor effects on grouping unit variables. We present
an approach to defining R2 at each level of the multilevel model, rather
than attempting to create a single summary measure of fit. Our method is
based on comparing variances in a single fitted model rather than
comparing to a null model. In simple regression, our measure generalizes
the classical adjusted R2. We also discuss a related variance comparison
to summarize the degree to which estimates at each level of the model
are pooled together based on the levelspecific regression relationship,
rather than estimated separately. This pooling factor is related to the
concept of shrinkage in simple hierarchical models. We illustrate the
methods on a dataset of radon in houses within counties using a series
of models ranging from a simple linear regression model to a multilevel
varyingintercept, varyingslope model.


39 
Paper

Operationalizing and Testing Spatial Theories of Voting
Quinn, Kevin M.
Martin, Andrew D.

Uploaded 
04151998

Keywords 
spatial voting factor analysis multinomial probit multinomial logit Bayesian inference model comparison Bayes factors MCMC Dutch politics Danish politics

Abstract 
Spatial models of voting behavior provide the foundation for a
substantial number of theoretical results. Nonetheless, empirical
work involving the spatial model faces a number of potential
difficulties. First, measures of the latent voter and candidate issue
positions must be obtained. Second, evaluating the fit of competing
statistical models of voter choice is often more complicated than
previously realized. In this paper, we discuss precisely these
issues. We argue that confirmatory factor analysis applied to
masslevel issue preference questions is an attractive means of
measuring voter ideal points. We also show how party issue positions
can be recovered using a variation of this strategy. We go on to
discuss the problems of assessing the fit of competing statistical
models (multinomial logit vs. multinomial probit) and competing
explanations (those based on spatial theory vs. those derived from
other theories of voting such as sociological theories). We
demonstrate how the Bayesian perspective not only provides
computational advantages in the case of fitting the multinomial probit
model, but also how it facilitates both types of comparison mentioned
above. Results from the Netherlands and Denmark suggest that even
when the computational cost of multinomial probit is disregarded, the
decision whether to use multinomial probit (MNP) or multinomial logit
(MNL) is not clearcut. 

40 
Paper

Estimating Incumbency Advantage and Campaign Spending Effect without the Simultaneity Bias
Fukumoto, Kentaro

Uploaded 
07162006

Keywords 
Incumbency Advantage Campaign Spending Simultaneity Bias Bayesian Nash equilibria normal vote

Abstract 
In estimating incumbency advantage and campaign spending effect, simultaneity problem is composed of stochastic dependence and parametric dependence. Scholars have tried to solve the former, while the present paper intends to tackle the latter. Its core idea is to estimate parameters by maximizing likelihood of all endogenous variables (vote, both parties' candidate qualities and campaign spending) simultaneously. In order to do it, I take advantage of theories of electoral politics rigorously, model each endogenous variables by the others (or their expectation), derive Bayesian Nash equilibria, and plug them into my estimator. I show superiority of my model compared to the conventional estimators by Monte Carlo simulation. Empirical application of this model to the recent U.S. House election data demonstrates that incumbency advantage is smaller than previously shown and that entry of incumbent and strong challenger is motivated by electoral prospect. 

41 
Paper

Bayesian Combination of State Polls and Election Forecasts
Lock, Kari
Gelman, Andrew

Uploaded 
09212008

Keywords 
election prediction preelection polls Bayesian updating shrinkage estimation

Abstract 
In February of 2008, SurveyUSA polled 600 people in each state and asked who they would vote for in either headtohead matchup: Obama vs. McCain, and Clinton vs. McCain. Here we integrate these polls with prior information; how each state voted in comparison to the national outcome in the 2004 election. We use Bayesian methods to merge prior and poll data, weighting each by its respective information. The variance for our poll data incorporates both sampling variability and variability due to time before the election, estimated using preelection poll data from the 2000 and 2004 elections. The variance for our prior data is estimated using the results of the past nine presidential elections. The union of prior and poll data results in a posterior distribution predicting how each state will vote, in turn giving us posterior intervals for both the popular and electoral vote outcomes of the 2008 presidential election. Lastly, these posterior distributions are updated with the most recent poll data as of August, 2008. 

42 
Paper

Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation
Bafumi, Joseph
Gelman, Andrew
Park, David K.
Kaplan, Noah

Uploaded 
06112004

Keywords 
Ideal points Bayesian Logistic regression Rasch model

Abstract 
In recent years, logistic regression (Rasch) models have been
used in political science for estimating ideal points of
legislators and Supreme Court justices. These models present
estimation and identifiability challenges, such as improper
variance estimates, scale and translation invariance, reflection
invariance, and issues with outliers. We resolve these issues
using Bayesian hierarchical modeling, linear transformations,
informative regression predictors, and explicit modeling for
outliers. In addition, we explore new ways to usefully display
inferences and check model fit.


43 
Paper

A Random Effects Approach to Legislative Ideal Point Estimation
Bailey, Michael

Uploaded 
04211998

Keywords 
ideal points random effects models Bayesian estimation em algorithm

Abstract 
Conventionally, scholars use either standard probit/logit techniques or
fixedeffect methods to estimate legislative ideal points. However,
these methods are unsatisfactory when a limited number of votes are
available: standard probit/logit methods are poorly equipped to handle
multiple votes and fixedeffect models disregard serious ``incidental
parameter'' problems. In this paper I present an alternative approach
that moves beyond singlevote probit/logit analysis without requiring the
large number of votes needed for fixedeffects models. The method is
based on a random effects, panel logit framework that models ideal points
as stochastic functions of legislator characteristics. Monte Carlo
results and an application to trade politics demonstrate the practical
usefulness of the method. 

44 
Paper

An Automated Method of TopicCoding Legislative Speech Over Time with Application to the 105th108th U.S. Senate
Quinn, Kevin
Monroe, Burt
Colaresi, Michael
Crespin, Michael
Radev, Dragomir

Uploaded 
07182006

Keywords 
legislatures agendas content analysis Bayesian time series cluster analysis unsupervised learning

Abstract 
We describe a method for statistical learning from speech documents that we apply to the Congressional Record in order to gain new insight into the dynamics of the political agenda. Prior efforts to evaluate the attention of elected representatives across topic areas have largely been expensive manual coding exercises and are generally circumscribed along one or more features of detail: limited time periods, high levels of temporal aggregation, and coarse topical categories. Conversely, the Congressional Record has scarcely been used for such analyses, largely because it contains too much information to absorb manually. We describe here a method for
inferring, through the patterns of word choice in each speech and the dynamics of word choice patterns across time, (a) what the topics of speeches are, and (b) the probability that attention will be paid to any given topic or set of topics over time. We use the model to examine the agenda in the United States Senate from 19972004, based on a new database of over 70 thousand speech documents containing over 70 million words. We estimate the model for 42 topics and provide evidence that we can reveal speech topics that are both distinctive and interrelated in substantively meaningful ways. We demonstrate further that the dynamics our model gives us leverage into important questions about the dynamics of the political agenda. 

46 
Paper

Estimating incumbency advantage and its variation, as an example of a before/after study
Gelman, Andrew
Huang, Zaiying

Uploaded 
02072003

Keywords 
Bayesian inference beforeafter study Congressional elections Gibbs

Abstract 
Incumbency advantage is one of the most studied features in American
legislative elections. In this paper, we construct and implement an
estimate that allows incumbency advantage to vary between individual
incumbents. This model predicts that openseat elections will be less
variable than those with incumbents running, an observed empirical
pattern that is not explained by previous models. We apply our method
to the U.S. House of Representatives in the twentieth century: our
estimate of the overall pattern of incumbency advantage over time is
similar to previous estimates (although slightly lower), and we also
find a pattern of increasing variation. In addition to the
application to incumbency advantage, our approach represents a new
method, using multilevel modeling, for estimating effects in
before/after studies. 

48 
Paper

Presidential Approval: the case of George W. Bush
Beck, Nathaniel
Jackman, Simon
Rosenthal, Howard

Uploaded 
07192006

Keywords 
presidential approval public opinion polls house effects dynamic linear model Bayesian statistics Markov chain Monte Carlo state space pages of killer graphs

Abstract 
We use a Bayesian dynamic linear model to track approval for George W. Bush over time. Our analysis deals with several issues that have been usually addressed separately in the extant literature. First, our analysis uses polling data collected at a higher frequency than is typical, using over 1,100 published national polls, and data on macroeconomic conditions collected at the weekly level. By combining this much poll information, we are much better poised to examine the public's reactions to events over shorter time scales than can the typical analysis of approval that utilizes monthly or quarterly approval. Second, our statistical modeling explicitly deals with the sampling error of these polls, as well as the possibility of bias in the polls due to house effects. Indeed, quite aside from the question of ``what drives approval?'', there is considerable interest in the extent to which polling organizations systematically diverge from one another in assessing approval for the president. These bias parameters are not only necessary parts of any realistic model of approval that utilizes data from multiple polling organizations, but easily estimated via the Bayesian dynamics linear model. 

49 
Paper

Spike and Slab Prior Distributions for Simultaneous Bayesian Hypothesis Testing, Model Selection, and Prediction, of Nonlinear Outcomes
Pang, Xun
Gill, Jeff

Uploaded 
07132009

Keywords 
Spike and Slab Prior Hypothesis Testing Bayesian Model Selection Bayesian Model Averaging Adaptive Rejection Sampling Generalized Linear Model

Abstract 
A small body of literature has used the spike and slab prior specification for model selection with strictly linear outcomes. In this setup a twocomponent mixture distribution is stipulated for coefficients of interest with one part centered at zero with very high precision (the spike) and the other as a distribution diffusely centered at the research hypothesis (the slab). With the selective shrinkage, this setup incorporates the zero coefficient contingency directly into the modeling process to produce posterior probabilities for hypothesized outcomes. We extend the model to qualitative responses by designing a hierarchy of forms over both the parameter and model spaces to achieve variable selection, model averaging, and individual coefficient hypothesis testing. To overcome the technical challenges in estimating the marginal posterior distributions possibly with a dramatic ratio of density heights of the spike to the slab, we develop a hybrid Gibbs sampling algorithm using an adaptive rejection approach for various discrete outcome models, including dichotomous, polychotomous, and count responses. The performance of the models and methods are assessed with both Monte Carlo experiments and empirical applications in political science. 

