
1 
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.


2 
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. 

3 
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. 

5 
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. 

7 
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. 

8 
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. 

10 
Paper

The Problem with Quantitative Studies of International Conflict
Beck, Nathaniel
King, Gary
Zeng, Langche

Uploaded 
07151998

Keywords 
Conflict logit neural networks forecasting Bayesian analysis

Abstract 
Despite immense data collections, prestigious journals, and
sophisticated analyses, empirical findings in the literature on
international conflict are frequently unsatisfying. Statistical
results appear to change from article to article and specification
to specification. Very few relationships hold up to replication
with even minor respecification. Accurate forecasts are
nonexistent. We provide a simple conjecture about what accounts for
this problem, and offer a statistical framework that better matches
the substantive issues and types of data in this field. Our model,
a version of a ``neural network'' model, forecasts substantially
better than any previous effort, and appears to uncover some
structural features of international conflict. 

11 
Paper

Expressive Bayesian Voters, their Turnout Decisions, and Double Probit
Achen, Christopher

Uploaded 
07172006

Keywords 
turnout expressive Bayesian probit scobit EITM

Abstract 
Voting is an expressive act. Since people are not born wanting to express themselves politically, the desire to vote must be acquired, either by learning about the candidates, by using party identification as a cognitive shortcut, or by contact from a trusted source. Modeled as Bayesian updating, this simple explanatory framework has dramatic implications for the understanding of voter turnout. It mathematically implies the main empirical generalizations familiar from the literature, it predicts hitherto unnoticed patterns that appear in turnout data, it provides a better fitting statistical model (double probit) for sample surveys of turnout, and it allows researchers to forecast turnout patterns in new elections when circumstances change. Thus the case is strengthened for the Bayesian voter model as a central organizing principle for public opinion and voting behavior. 

12 
Paper

Joint Modeling of Dynamic and CrossSectional Heterogeneity: Introducing Hidden Markov Panel Models
Park, Jong Hee

Uploaded 
07142009

Keywords 
Bayesian statistics Fixedeffects Hidden Markov models Markov chain Monte Carlo methods Randomeffects Reversible jump Markov chain Monte Carlo

Abstract 
Researchers working with panel data sets often face situations where changes in unobserved factors have produced changes in the crosssectional heterogeneity across time periods. Unfortunately, conventional statistical methods for panel data are based on the assumption that the unobserved crosssectional heterogeneity is time constant. In this paper, I introduce statistical methods to diagnose and model changes in the unobserved heterogeneity. First, I develop three combinations of a hidden Markov model with panel data models using the Bayesian framework; (1) a baseline hidden Markov panel model with varying fixed effects and varying random effects; (2) a hidden Markov panel model with varying fixed effects; and (3) a hidden Markov panel model with varying intercepts. Second, I present model selection methods to diagnose the dynamic heterogeneity using the marginal likelihood method and the reversible jump Markov chain Monte Carlo method. I illustrate the utility of these methods using two important ongoing political economy debates; the relationship between income inequality and economic growth and the effect of institutions on income inequality. 

13 
Paper

Unresponsive, Unpersuaded: The Unintended Consequences of Voter Persuasion Efforts
Bailey, Michael
Hopkins, Daniel
Rogers, Todd

Uploaded 
08092013

Keywords 
causal inference field experiments persuasion attrition multiple imputation Approximate Bayesian Bootstrap

Abstract 
Can randomized experiments at the individual level help assess the persuasive effects of campaign tactics? In the contemporary U.S., vote choice is not observable, so one promising research design to assess persuasion involves randomizing appeals and then using a survey to measure vote intentions. Here, we analyze one such field experiment conducted during the 2008 presidential election in which 56,000 registered voters were assigned to persuasion in person, by phone, and/or by mail. Persuasive appeals by canvassers had two unintended consequences. First, they reduced responsiveness to the followup survey, lowering the response rate sharply among infrequent voters. Second, various statistical methods to address the resulting biases converge on a counterintuitive conclusion: the persuasive canvassing reduced candidate support. Our results allow us to rule out even small effects in the intended direction, and illustrate the backlash that persuasion can engender. 

14 
Paper

Connecting Interest Groups and Congress: A New Approach to Understanding Interest Group Success
Victor, Jennifer Nicoll

Uploaded 
07162002

Keywords 
Interest Groups Congress Multiple Imputation Bayesian Information Criterion Ordinal Probit Nonnested Models Legislative Context

Abstract 
The primary challenge in explaining interest group legislative success in
Congress has been methodological. The discipline requires at least two
critical elements to make progress on this important question. First, we
need a theory that accounts for the highly interactive spatial game
between interest groups and legislators. Second, the discipline needs an
empirical model that associates interest groups and their activities with
specific congressional bills.
In this project I begin to contribute to our understanding of the complex
relationship between interest groups and Congress. I develop a theory of
group success that is based upon the strategies in which groups engage,
the groups' organizational capacity, and the strategic context of
legislation. I predict that groups will tailor their activities (and
strategically spend their resources) in Congress based upon two critical
factors: whether the group supports or opposes the legislation, and the
legislative environment for the bill.
To test this model I develop a unique sampling procedure and survey
design. I use legislative hearings to generate a sample of groups that
are associated with specific issues and survey them about their activities
on those issues. Then, I associate each group's issue with a specific
bill in Congress. I then track the bill to discern its final status. I
create a dependent variable of interest group success that is based on the
group's position (favor or oppose) and the final status of the bill. This
sampling procedure and dependent variable allow me to make inferences
about group behavior over specific legislative proposals. I develop
independent variables of group activity, group organizational capacity,
and legislative context from the survey instrument and objective
information about the bills.
To fill in gaps in the survey data set, I use a multiple imputation method
that generates plausible values based on given distributions of data. I
estimate two modelsone for groups in favor of legislation, and one for
opposition groups. The ordinal probit models generally support the
theoretical expectations. In sum, I find that groups can best expend
their resources in pursuit of rules that advantage their position rather
than fighting for bill content. 

15 
Paper

Pooling Disparate Observations
Bartels, Larry M.

Uploaded 
01011995

Keywords 
induction statistical inference Bayesian statistics econometrics observations Ftest pooling fractional pooling

Abstract 
Data analysts frequently face difficult choices about whether
to pool disparate observations in their statistical analyses.
I explore the inferential ramifications of such choices, and
propose a new technique, dubbed "fractional pooling," which
provides a simple way to incorporate prior beliefs about the
theoretical relevance of disparate observations. The technique
is easy to implement and has a plausible rationale in Bayesian
statistical theory. I illustrate the potential utility of
fractional pooling by applying the technique to political data
originally analyzed by Ashenfelter (1994), Powell (1982), and
Alesina et al. (1993). These examples demonstrate that
conventional approaches to analyzing disparate observations can
be seriously misleading, and that the approach proposed here can
enrich our understanding of the inferential implications of
unavoidably subjective judgments about the theoretical relevance
of available data. 

16 
Paper

Designing and Analyzing Randomized Experiments
Horiuchi, Yusaku
Imai, Kosuke
Taniguchi, Naoko

Uploaded 
07052005

Keywords 
Bayesian inference causal inference noncompliance nonresponse randomized block design

Abstract 
In this paper, we demonstrate how to effectively design and analyze randomized experiments, which are becoming increasingly common in political science research. Randomized experiments provide researchers with an opportunity to obtain unbiased estimates of causal effects because the randomization of treatment guarantees that the treatment and control groups are on average equal in both observed and unobserved characteristics. Even in randomized experiments, however, complications can arise. In political science experiments, researchers often cannot force subjects to comply with treatment assignment or to provide the information necessary for the estimation of causal effects. Building on the recent statistical literature, we show how to make statistical adjustments for these noncompliance and nonresponse problems when analyzing randomized experiments. We also demonstrate how to design randomized experiments so that the potential impact of such complications is minimized. 

17 
Paper

Balancing Competing Demands: PositionTaking and Election Proximity in the European Parliament
Lindstaedt, Rene
Slapin, Jonathan
Vander Wielen, Ryan

Uploaded 
07312009

Keywords 
Legislative Politics European Parliament Comparative Politics Bayesian IRT Parties Formal Theory

Abstract 
Parties value unity, yet, members of parliament face competing demands, giving them incentives to deviate from the party. For members of the European Parliament (MEPs), these competing demands are national party and European party group pressures. Here, we look at how MEPs respond to those competing demands. We examine ideological shifts within a single parliamentary term to assess how European Parliament (EP) election proximity affects party group cohesion. Our formal model of legislative behavior with multiple principals yields the following hypothesis: When EP elections are proximate, national party delegations shift toward national party positions, thus weakening EP party group cohesion. For our empirical test, we analyze roll call data from the fifth EP (19992004) using Bayesian item response models. We find significant movement among national party delegations as EP elections approach, which is consistent with our theoretical model, but surprising given the existing literature on EP elections as secondorder contests. 

18 
Paper

StateLevel Opinions from National Surveys: Poststratification using Hierarchical Logistic Regression
Park, David K.
Gelman, Andrew
Bafumi, Joseph

Uploaded 
07122002

Keywords 
Bayesian Inference Hierarchical Logit Poststratification Public Opinion States Elections

Abstract 
Previous researchers have pooled national surveys in order to construct
statelevel opinions. However, in order to overcome the small n problem
for less populous states, they have aggregated a decade or more of
national surveys to construct their measures. For example, Erikson,
Wright and McIver (1993) pooled 122 national surveys conducted over 13
years to produce statelevel partisan and ideology estimates. Brace,
SimsButler, Arceneaux, and Johnson (2002) pooled 22 surveys over a
25year period to produce statelevel opinions on a number of specific
issues. We construct a hierarchical logistic regression model for the
mean of a binary response variable conditional on poststratification
cells. This approach combines the modeling approach often used in
smallarea estimation with the population information used in
poststratification (see Gelman and Little 1997). We produce statelevel
estimates pooling seven national surveys conducted over a nineday
period. We first apply the method to a set of U.S preelection polls,
poststratified by state, region, as well as the usual demographic
variables and evaluate the model by comparing it to statelevel election
outcomes. We then produce statelevel partisan and ideology estimates by
comparing it to Erikson, Wright and McIver's estimates. 

19 
Paper

A Bayesian Method for the Analysis of Dyadic Crisis Data
Smith, Alastair

Uploaded 
11041996

Keywords 
Bayesian model testing Censored data Crisis data Gibbs sampling Markov chain Monte Carlo Ordered discrete choice model Strategic choice

Abstract 
his paper examines the level of force that nations use during
disputes. Suppose that two nations, A and B, are involved in a
dispute. Each nation chooses the level of violence that it is prepared
to use in order to achieve its objectives. Since there are two
opponents making decisions, the outcome of the crisis is determined by
a bivariate rather than univariate process. I propose a bivariate
ordered discrete choice model to examine the relationship between
nation A's decision to use force, nation B's decision to use force,
and a series of explanatory variables. The model is estimated in the
Bayesian context using a Markov chain Monte Carlo simulation
technique. I analyze Bueno de Mesquita and Lalman's (1992) dyadically
coded version of the Militarized Interstate Dispute data (Gochman and
Moaz 1984). Various models are compared using Bayes Factors. The
results indicate that nation A's and nation B's decisions to use force
can not be regarded as independent. Bayesian model comparison show
that variables derived from Bueno de Mesquita's expected utility
theory (1982, 1985; Bueno de Mesquita and Lalman 1986, 1992) provide
the best explanatory variables for decision making in crises. 

20 
Paper

Bayesian and Likelihood Inference for 2 x 2 Ecological Tables: An Incomplete Data Approach
Imai, Kosuke
Lu, Ying
Strauss, Aaron

Uploaded 
12162006

Keywords 
Coarse data Contextual effects
Data augmentation EM algorithm Missing information principle
Nonparametric Bayesian Modeling.

Abstract 
Ecological inference is a statistical problem where aggregatelevel data are used to make inferences about individuallevel behavior. Recent years have witnessed resurgent interest in ecological inference among political methodologists and statisticians. In this paper, we conduct a theoretical and empirical study of Bayesian and likelihood inference for 2 x 2 ecological tables by applying the general statistical framework of incomplete data. We first show that the ecological inference problem can be decomposed into three factors: distributional effects which address the possible misspecification of parametric modeling assumptions about the unknown distribution of missing data, contextual effects which represent the possible correlation between missing data and observed variables, and aggregation effects which are directly related to the loss of information caused by data aggregation. We then examine how these three factors affect inference and offer new statistical methods to address each of them. To deal with distributional effects, we propose a nonparametric Bayesian model based on a Dirichlet process prior which relaxes common parametric assumptions. We also specify the statistical adjustments necessary to account for contextual effects. Finally, while little can be done to cope with aggregation effects, we offer a method to quantify the magnitude of such effects in order to formally assess its severity. We use simulated and real data sets to empirically investigate the consequences of these three factors and to evaluate the performance of our proposed methods. C code, along with an easytouse R interface, is publicly available for implementing our proposed methods. 

21 
Paper

Bayesian statistical decision theory and a critical test for substantive significance
Esarey, Justin

Uploaded 
09092009

Keywords 
inference ttest substantive significance Bayesian

Abstract 
I introduce a new critical test statistic, c*, that uses Bayesian statistical decision theory to help an analyst determine whether quantitative evidence supports the existence of a substantively meaningful relationship. Bayesian statistical decision theory takes a rational choice perspective toward evidence, allowing researchers to ask whether it makes sense to believe in the existence of a statistical relationship given how they value the consequences of correct and incorrect decisions. If a relationship of size c* is not important enough to influence future research and policy advice, then the evidence does not support the existence of a substantively significant effect. A replication of findings from the American Journal of Political Science and Journal of Politics illustrates that statistical significance at conventional levels is neither necessary nor sufficient to accept a hypothesis of substantive significance using c*. I also make software packages available for Stata and R that allow political scientists to easily use c* for inference in their own research. 

22 
Paper

Moving Mountains: Bayesian Forecasting As Policy Evaluation
Brandt, Patrick T.
Freeman, John R.

Uploaded 
04242002

Keywords 
Bayesian vector autoregression VAR policy evaluation conditional forecasting

Abstract 
Many policy analysts fail to appreciate the dynamic, complex causal
nature of political processes. We advocate a vector autoregression
(VAR) based approach to policy analysis that accounts for various
multivariate and dynamic elements in policy formulation and
for both dynamic and specification uncertainty of parameters. The
model we present is based on recent developments in Bayesian
VAR modeling and forecasting. We present an example based on work in
Goldstein et al. (2001) that illustrates how a full accounting of the
dynamics and uncertainty in multivariate data can lead to more
precise and instructive results about international mediation in
Middle Eastern conflict. 

23 
Paper

Not Asked and Not Answered: Multiple Imputation for Multiple Surveys
Gelman, Andrew
King, Gary
Liu, Chuanhai

Uploaded 
10271997

Keywords 
Bayesian inference cluster sampling diagnostics hierarchical models ignorable nonresponse missing data political science sample surveys stratified sampling multiple imputation

Abstract 
We present a method of analyzing a series of independent
crosssectional surveys in which some questions are not answered in
some surveys and some respondents do not answer some of the questions
posed. The method is also applicable to a single survey in which
different questions are asked, or different sampling methods used, in
different strata or clusters. Our method involves multiplyimputing
the missing items and questions by adding to existing methods of
imputation designed for single surveys a hierarchical regression model
that allows covariates at the individual and survey levels.
Information from survey weights is exploited by including in the
analysis the variables on which the weights were based, and then
reweighting individual responses (observed and imputed) to estimate
population quantities. We also develop diagnostics for checking the
fit of the imputation model based on comparing imputed to nonimputed
data. We illustrate with the example that motivated this project 
a study of preelection public opinion polls, in which not all the
questions of interest are asked in all the surveys, so that it is
infeasible to impute each survey separately. 

24 
Paper

Modeling Foreign Direct Investment as a Longitudinal Social Network
Jensen, Nathan
Martin, Andrew
Westveld, Anton

Uploaded 
07132007

Keywords 
foreign direct investment social network data longitudinal data hierarchical modeling mixture modeling Bayesian inference.

Abstract 
An extensive literature in international and comparative political economy has focused on the how the mobility of capital affects the ability of governments to tax and regulate firms. The conventional wisdom holds that governments are in competition with each other to attract foreign direct investment (FDI). Nationstates observe the fiscal and regulatory decisions of competitor governments, and are forced to either respond with policy changes or risk losing foreign direct investment, along with the politically salient jobs that come with these investments. The political economy of FDI suggests a network of investments with complicated dependencies.
We propose an empirical strategy for modeling investment patterns in 24 advanced industrialized countries from 19852000. Using bilateral FDI data we estimate how increases in flows of FDI affect the flows of FDI in other countries. Our statistical model is based on the methodology developed by Westveld & Hoff (2007). The model allows the temporal examination of each notion's activity level in investing, attractiveness to investors, and reciprocity between pairs of nations. We extend the model by treating the reported inflow and outflow data as independent replicates of the true value and allowing for a mixture model for the fixed effects portion of the network model. Using a fully Bayesian approach, we also impute missing data within the MCMC algorithm used to fit the model. 

26 
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. 

27 
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. 

29 
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. 

30 
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. 

32 
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. 

33 
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. 

34 
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. 

35 
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.” 

36 
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. 

38 
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. 

39 
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. 

40 
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. 

41 
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. 

42 
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. 

45 
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. 

47 
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. 

48 
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. 

49 
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. 

50 
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. 

