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Below results based on the criteria 'linear'
Total number of records returned: 25

1
Paper
Direction and Intensity of Russian Macroeconomic Evaluations
Jones, Bradford S.
Willerton, John P.
Sobel, Michael E.

Uploaded 08-30-1998
Keywords Russia
public opinion
log linear models
Abstract The Russian macroeconomy has exhibited volatility since the transformation from the Soviet Union to the Russian Federation. Much is known about the Russian public opinion climate during the end of the Soviet era and the beginning of the Russian Federation era; however, less well understood is the nature of Russians' macroeconomic evaluations during this on-going transformation. In this paper, we analyze Russians' assessments of the macroeconomy using Russian public opinion data asking respondents to assess the Russian national economy. We establish four testable hypotheses. First, we hypothesize that the direction of Russian opinion will be asymmetrically more negative than positive across all periods in the study. Second, we hypothesize that economic assessments will vary by residential region. Specifically, we contend the response distribution for respondents from Moscow and St. Petersburg (MSP) will differ from respondents from other residential regions. Third (and related to the second), we hypothesize that the response distributions for MSP respondents will be temporally heterogenous while the response distribution for respondents outside MSP will be temporally homogenous. Fourth, we hypothesize that despite the poor performance of the economy during the Russian Federation transition, Russian public opinion will not exhibit extreme negativity in macroeconomic evaluations. Using published survey data collected from the bi -monthly extsl{Russian Public Opinion Monitor} conducted by the Russian Center for Public Opinion Research (VCIOM), for the period January 1994 to July 1996, we examine both the direction and intensity of Russian opinion toward the state of the national economy by estimating the distribution on the response variable using an adjacent category logit model (Jones and Sobel 1998, Sobel 1995, 1997, 1998). From our analysis, we find first that the direction of Russians' evaluation of the macroeconomy is consistently negative rather than positive---a finding that corroborates extant research; however, the directional nature of economic assessments displays significant residential variation between MSP and the rest of the country. Second, we find significant residential variation in economic assessments. Specifically, the response distribution for MSP respondents can be distinguished from the response distribution from respondents in other residential regions, and also, the response distribution for MSP respondents displays considerable temporal heterogeneity. We argue this variability tends to follow changes in the macroeconomic and political environments. Third, we do not find support for the hypothesis of temporal homogeneity in the response distribution for respondents outside of MSP. Nevertheless, residents in other cities and in rural regions seem not to be as responsive to macroeconomic changes over the period, thus eliciting milder temporal variability than MSP respondents. Fourth, we find that in terms of the response distribution, the intensity of Russian pessimism (or optimism) is extsl{not} extreme.

2
Paper
How can soccer improve statistical learning?
Filho, Dalson
Rocha, Enivaldo
Paranhos, Ranulfo
Júnior, José

Uploaded 03-19-2014
Keywords quantitative methods
linear regression
soccer
Abstract This paper presents an active classroom exercise focusing on the interpretation of ordinary least squares regression coefficients. Methodologically, students analyze Brazilian soccer matches data, formulate and test classical hypothesis regarding home team advantage. Technically, our framework is simply adapted for others sports and has no implementation cost. In addition, the exercise is easily conducted by the instructor and highly enjoyable for the students. The intuitive approach also facilitates the understanding of linear regression practical application.

3
Paper
The Robustness of Normal-theory LISREL Models: Tests Using a New Optimizer, the Bootstrap, and Sampling Experiments, with Applications
Mebane, Walter R.
Sekhon, Jasjeet
Wells, Martin T.

Uploaded 01-01-1995
Keywords statistics
estimation
covariance structures
linear structural relations
LISREL
bootstrap
confidence intervals
BCa
specification tests
goodness-of-fit
hypothesis tests
optimization
evolutionary programming
genetic algorithms
monte carlo
sampling experiment
Abstract Asymptotic results from theoretical statistics show that the linear structural relations (LISREL) covariance structure model is robust to many kinds of departures from multivariate normality in the observed data. But close examination of the statistical theory suggests that the kinds of hypotheses about alternative models that are most often of interest in political science research are not covered by the nice robustness results. The typical size of political science data samples also raises questions about the applicability of the asymptotic normal theory. We present results from a Monte Carlo sampling experiment and from analysis of two real data sets both to illustrate the robustness results and to demonstrate how it is unwise to rely on them in substantive political science research. We propose new methods using the bootstrap to assess more accurately the distributions of parameter estimates and test statistics for the LISREL model. To implement the bootstrap we use optimization software two of us have developed, incorporating the quasi-Newton BFGS method in an evolutionary programming algorithm. We describe methods for drawing inferences about LISREL models that are much more reliable than the asymptotic normal-theory techniques. The methods we propose are implemented using the new software we have developed. Our bootstrap and optimization methods allow model assessment and model selection to use well understood statistical principles such as classical hypothesis testing.

4
Paper
Aggregate Economic Conditions and Indivdual Forecasts: A Mulilevel Model of EconomicExpectations
Jones, Bradford S.
Haller, H. Brandon

Uploaded 00-00-0000
Keywords random coefficient modeling
multilevel analysis
hierarchical linear models
Abstract To what extent are individual economic expectations related to actual economic conditions? This is the central question examined in this paper. Surprisingly, little research exists examining how economic expectations are formed. Moreover, even less research has been done examining the interaction between the state of the national economy and individual forecasts. Most research addressing expectation formation has resided at the aggregate level. In this paper, we utilize the methodology of random coefficient models to explore the linkage between individuals and the macroeconomic environment. We conceptualize individuals as being "nested" within time periods. Individual forecasts are treated as contextually conditioned by the state of the economy. We find evidence that aggregate economic indicators do influence the parameters predicting economic expectations. Furthermore, the relationship between the macroeconomy and individual expectations provides strong support for Katona's (1972, 1975) notion of "psychological economics." We find that individual forecasts of the future are "brighter" when aggregate economic conditions are "darkest." Additionally, we find that individuals tend to rely less on retrospective evaluations of the economy when the economy is faring poorly.

5
Paper
Getting the Mean Right: Generalized Additive Models
Beck, Nathaniel
Jackman, Simon

Uploaded 00-00-0000
Keywords non-parametric regression
smoothing
loess
non-linear egression
Monte Carlo analysis
interaction effects
incumbency
cabinet duration
violence
Abstract We examine the utility of the generalized additive model as an alternative to the common linear model. Generalized additive models are flexible in that they allow the effect of each independent variable to be modelled non-parametrically while requiring that the effect of all the independent variables is additive. GAMs are common in the statistics literature but are conspicuously absent in political science. The paper presents the basic features of the generalized additive model. Through Monte Carlo experimentation we show that there is little danger of the generalized additive model finding spurious structures. We use GAMS to reanalyze several political science data sets. These applications show that generalized additive models can be used to improve standard analyses by guiding researchers as to the parametric shape of response functions. The technique also provides interesting insights about data, particularly in terms of modelling interactions.

6
Paper
Forecasting Time Series
Hinich, Melvin J.

Uploaded 07-08-1997
Keywords forecast
autoregressive
vector AR
state space
linear
Abstract The limits of forecasting a linear times series system are discussed.\r\nA stable autoregressive linear system can only be accurately predicted\r\nfor a few steps ahead of the last observation. If the time series is a\r\ndeterministic trend plus random fluctuations then the trend can be\r\npredicted as long as it is stable.

7
Paper
Generalized Substantively Reweighted Least Squares Regression
Gill, Jeff

Uploaded 01-29-1997
Keywords Linear Models
Robust Procedures
Data Analysis
Outlier Identification
Abstract Linear modeling often employs robust and resistant techniques to compensate for undesirable properties in the data. Conversely, Substantive Weighted Least Squares differs from these techniques since it seeks to analyze what makes the outliers distinguishable in their use of resources. SWLS does not see outliers as becoming potentially unbounded or even that they are necessarily undesirable elements of the data. SWLS runs consecutive weighted OLS models downweighting each case whose jacknifed residual is less than a specific threshold. Final iteration significant variables are identified as those which have a greater effect on higher performing cases and therefore provide prescriptive recommendations. GSRLS generalizes the SWLS technique by using transformations relating the jackknifed residuals to a common tabular distribution. This allows alpha-level positive outlier identification. Here, GSRLS is first placed in a theoretical context and further explored through monte-carlo simulation. In general, GSRLS can be seen as a data-analytic tool that exploits certain characteristics of the linear model to find variable influence on successful cases.

8
Paper
Presidential Approval: the case of George W. Bush
Beck, Nathaniel
Jackman, Simon
Rosenthal, Howard

Uploaded 07-19-2006
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 macro-economic 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.

9
Paper
Splitting a predictor at the upper quarter or third and the lower quarter or third
Gelman, Andrew
Park, David

Uploaded 07-06-2007
Keywords discretization
linear regression
statistical communication
trichotomizing
Abstract A linear regression of $y$ on $x$ can be approximated by a simple difference: the average values of $y$ corresponding to the highest quarter or third of $x$, minus the average values of $y$ corresponding to the lowest quarter or third of $x$. A simple theoretical analysis shows this comparison performs reasonably well, with 80%--90% efficiency compared to the linear regression if the predictor is uniformly or normally distributed. Discretizing $x$ into three categories claws back about half the efficiency lost by the commonly-used strategy of dichotomizing the predictor. We illustrate with the example that motivated this research: an analysis of income and voting which we had originally performed for a scholarly journal but then wanted to communicate to a general audience.

10
Paper
Degeneracy and Inference for Social Networks
Handcock, Mark S.

Uploaded 07-15-2002
Keywords Random graph models
log-linear network model
Markov fields
Markov Chain Monte Carlo
Abstract Networks are a form of "relational data". Relational data arise in many social science fields and graph models are a natural approach to representing the structure of these relations. This framework has many applications including, for example, the structure of social networks, the behavior of epidemics, the interconnectedness of the WWW, and long-distance telephone calling patterns. We review stochastic models for such graphs, with particular focus on sexual and drug use networks. Commonly used Markov models were introduced by Frank and Strauss (1986) and were derived from developments in spatial statistics (Besag 1974). These models recognize the complex dependencies within relational data structures. To date, the use of graph models for networks has been limited by three interrelated factors: the complexity of realistic models, lack of use of simulation studies, and a poor understanding of the properties of inferential methods. In this talk we discuss these factors and the degeneracy of commonly promoted models. We also review the role of Markov Chain Monte Carlo (MCMC) algorithms for simulation and likelihood-based inference. These ideas are applied to a sexual relations network from Colorado Springs with the objective of understanding the social determinants of HIV spread. In this talk we focus on stochastic models for such graphs that can be used to represent the structural characteristics of the networks. In our applications, the nodes usually represent people, and the edges represent a specified relationship between the people.

11
Paper
Models of Path Dependence with an Empirical Application
Jackson, John
Kollman, Ken

Uploaded 07-17-2007
Keywords Path dependence
partisanship
non-linear least squares
Abstract It is now commonplace in the social sciences to describe an outcome or process as path dependent. By path dependence, researchers generally mean that the sequence of events prior to the observation of the outcome has explanatory power. The paper develops models that have both path dependent and non-path dependent properties, depending upon the value of a particular parameter. The paper then uses non-linear least squares and a Monte Carlo simulation to explore how well this parameter can be estimated, meaning how well scholars can discriminate betwen the two processes. The methodology is applied to the evolution of attitudes on aid to minorities and partisanship between 1956 and 2000. The results are consistent with the path dependent model.

12
Paper
What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation
Gill, Jeff
King, Gary

Uploaded 05-14-2002
Keywords Hessian
Cholesky
generalized inverse
maximum likelihood
statistical computing
importance sampling
pseudo-variance
generalized linear model
singular normal
Abstract What should a researcher do when statistical analysis software terminates before completion with a message that the Hessian is not invertable? The standard textbook advice is to respecify the model, but this is another way of saying that the researcher should change the question being asked. Obviously, however, computer programs should not be in the business of deciding what questions are worthy of study. Although noninvertable Hessians are sometimes signals of poorly posed questions, nonsensical models, or inappropriate estimators, they also frequently occur when information about the quantities of interest does exist in the data, through the likelihood function. We explain the problem in some detail and lay out two preliminary proposals for ways of dealing with noninvertable Hessians without changing the question asked.

13
Paper
A default prior distribution for logistic and other regression models
Gelman, Andrew
Jakulin, Aleks
Pittau, Maria Grazia
Su, Yu-Sung

Uploaded 08-03-2007
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 (non-hierarchical) 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 longer-tailed version of the distribution attained by assuming one-half additional success and one-half 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 higher-order interactions. This can be useful in routine data analysis as well as in automated procedures such as chained equations for missing-data imputation.

14
Paper
Detection of Multinomial Voting Irregularities
Mebane, Walter R.
Sekhon, Jasjeet
Wand, Jonathan

Uploaded 07-17-2001
Keywords outlier detection
robust estimation
overdispersed multinomial
generalized linear model
2000 presidential election
voting irregularities
Abstract We develop a robust estimator for an overdispersed multinomial regression model that we use to detect vote count outliers in the 2000 presidential election. The count vector we model contains vote totals for five candidate categories: Buchanan, Bush, Gore, Nader and ``other.'' We estimate the multinomial model using county-level data from Florida. In Florida, the model produces results for Buchanan that are essentially the same as in a binomial model: Palm Beach County has the largest positive residual for Buchanan. The multinomial model shows additional large discrepancies that almost always hurt Gore or Nader and help Bush or Buchanan.

15
Paper
Nonparametric Priors For Ordinal Bayesian Social Science Models: Specification and Estimation
Gill, Jeff
Casella, George

Uploaded 08-21-2008
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.

16
Paper
Bayesian Learning about Ideal Points of U.S. Supreme Court Justices, 1953-1999
Martin, Andrew D.
Quinn, Kevin M.

Uploaded 07-09-2001
Keywords item response models
dynamic linear models
Markov chain Monte Carlo
Abstract At the heart of attitudinal and strategic explanations of judicial behavior is the assumption that justices have policy preferences. These preferences have been measured in a handful of ways, including using factor analysis and multidimensional scaling techniques (Schubert, 1965, 1974), looking at past votes in a single policy area (Epstein et al., 1989), content-analyzing newspaper editorials at the time of appointment to the Court (Segal and Cover, 1989), and recording the background characteristics of the justices (Tate and Handberg, 1991). In this manuscript we employ Markov chain Monte Carlo (MCMC) methods to t Bayesian measurement models of judicial preferences for all justices serving on the U.S. Supreme Court from 1953 to 1999. We are particularly interested in considering to what extent ideal points of justices change throughout their tenure on the Court, and how the proposals over which they are voting also change across time. To do so, we t four longitudinal item response models that include dynamic specications for the ideal points and the case-specic parameters. Our results suggest that justices do not have constant ideal points, even after controlling for the types of cases that come before the Court.

17
Paper
Sampling Schemes for Generalized Linear Dirichlet Random Effects Models
Kyung, Minjung
Gill, Jeff
Casella, George

Uploaded 02-18-2009
Keywords generalized linear mixed Dirchlet model
Markov chain Monte Carlo
Dirichlet process priors for random effects
precision parameters
Scottish Social Attitudes Survey
terrorism targeting
Abstract We evaluate MCMC sampling schemes for a variety of link functions in generalized linear models with Dirichlet random effects. We find that models using Dirichlet process priors for the random effects tend to capture information in the data in a nonparametric fashion. In fitting the the Dirichlet process, dealing with the precision parameter has troubled model specifications in the past. Here we find that incorporating this parameter into the MCMC sampling scheme is not only computationally feasible, but also results in a more robust set of estimates, in that they are marginalized-over rather than conditioned-upon. Applications are provided with social science problems in areas where the data can be difficult to model. In all, we find that these models provide superior Bayesian posterior results in theory, simulation, and application.

18
Paper
Learning in Campaigns: A Policy Moderating Model of Individual Contributions to House Candidates
Wand, Jonathan
Mebane, Walter R.

Uploaded 04-18-1999
Keywords FEC
campaign contributions
campaign finance
policy moderation
GLM
generalized linear model
negative binomial
time series
bootstrap
U.S. House of Representatives
1984 election
Abstract We propose a policy moderating model of individual campaign contributions to House campaigns. Based on a model that implies moderating behavior by voters, we hypothesize that individuals use expectations about the Presidential election outcome when deciding whether to donate money to a House candidate. Using daily campaign contributions data drawn from the FEC Itemized Contributions files for 1984, we estimate a generalized linear model for count data with serially correlated errors. We expand on previous empirical applications of this type of model by comparing standard errors derived from a sandwich estimator to confidence intervals produced by a nonparametric bootstrap.

19
Paper
Estimation in Dirichlet Random Effects Models
Kyung, Minjung
Gill, Jeff
Casella, George

Uploaded 04-28-2009
Keywords generalized linear mixed model
Dirichlet process random effects model
precision parameter likelihood
Gibbs sampling
importance sampling
probit mixed Dirichlet random effects model
Abstract We develop a new Gibbs sampler for a linear mixed model with a Dirichlet process random effect term, which is easily extended to a generalized linear mixed model with a probit link function. Our Gibbs sampler exploits the properties of the multinomial and Dirichlet distribution, and is shown to be an improvement, in terms of operator norm and efficiency, over other commonly used MCMC algorithms. We also investigate methods for the estimation of the precision parameter of the Dirichlet process, finding that maximum likelihood may not be desirable, but a posterior mode is a reasonable approach. Examples are given to show how these models perform on real data. Our results complement both the theoretical basis of the Dirichlet process nonparametric prior and the computational work that has been done to date. Forthcoming: Annals of Statistics.

20
Paper
Coordination, Moderation and Institutional Balancing in American House Elections at Midterm
Mebane, Walter R.
Wand, Jonathan

Uploaded 09-02-1999
Keywords campaign finance
itemized contributions
congressional elections
generalized linear mixed model
Monte Carlo EM
random effects
conditional compound Poisson process
Abstract We use Federal Election Commission itemized contributions data from 1984 to estimate a model of campaign contributions in U.S. House elections. The model is a dynamic system of conditional compound Poisson processes in which there are contributions from both individuals and political action committees (PACs). The model includes random effects to allow for unobserved heterogeneity among districts and candidates. The dynamic effects measure how contributions to one candidate react to contributions to other candidates, as well as how contributions from individuals interact with contributions from PACs. We test the hypothesis that some candidates received higher contributions because of PAC endorsements. We also test whether national expectations about presidential election outcomes affect contributions to House candidates, as predicted by a policy moderating model. We use a Monte Carlo EM algorithm to optimize the likelihood of the model in specifications that include more than one random effect.

21
Paper
Spike and Slab Prior Distributions for Simultaneous Bayesian Hypothesis Testing, Model Selection, and Prediction, of Nonlinear Outcomes
Pang, Xun
Gill, Jeff

Uploaded 07-13-2009
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 two-component 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.

22
Paper
Modeling Direction and Intensity in Ordinal Scales with Midpoints
Jones, Bradford S.
Sobel, Michael E.

Uploaded 07-21-1998
Keywords adjacent category logit
log-linear models
public opinion
Congress
Abstract Political opinion analysts are frequently work with semantically balanced ordinal scales. Such survey items are frequently used to measure candidate evaluations, public spending preferences, positions on social issues, and candidate and party placement. Because of the special nature of these survey items (semantically balanced about a midpoint), researchers may be interested in understanding how both the response direction and response intensity varies over time and/or across covariate classes. That is, trends may be found in the tendency for respondents to choose categories above vs. below the midpoint (the response direction) and trends may be found in the tendency for respondents to choose between or among category labels above or below the midpoint. And while political analysts are commonly interested in response intensity and direction, traditional methods used to model distributions on semantically balanced ordinal scales are problematic. In this paper, we discuss a class of models originally developed by Sobel (1995, 1997, 1998) that allows researchers to simultaneously model direction and intensity in ordinal scales with midpoints. Specifically, we parameterize the model as an adjacent category logit model. Numerous parsimonious models may be arrived at that describe trends in the response direction and response intensity. Because the adjacent category logit model is linear in the logits, we estimate the model using log-linear models. We present an application of the models to data on approval ratings of House incumbents. We find that the trends in response directions (the tendency for respondents to evaluate the incumbent favorably or not favorably) increase through the 1980s, peaking in the late Eighties, and are now declining over the 1990s. With regard to response intensity, (that is, the tendency to respond in the extreme categories vs. the moderate categories), we find that intensity increases during most presidential election cycles and vanishes during midterm election years. We argue this finding is related to the different levels of political information citizens are exposed to in presidential vs. midterm election cycles.

23
Paper
Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects
Imai, Kosuke
Keele, Luke
Yamamoto, Teppei

Uploaded 07-20-2009
Keywords causal inference
causal mediation analysis
direct and indirect e ects
linear structural equation models
sequential ignorability
unmeasured confounders
Abstract Causal mediation analysis is routinely conducted by applied researchers in a variety of disciplines including epidemiology, political science, psychology, and sociology. The goal of such an analysis is to investigate alternative causal mechanisms by examining the roles of intermediate variables that lie in the causal path between the treatment and outcome variables. In this paper, we first prove that under a particular version of sequential ignorability assumption, the average causal mediation effect (ACME) is nonparametrically identified. We compare our identifying assumption with those proposed in the literature. Some practical implications of our identification result are also discussed. In particular, the popular estimator based on the linear structural equation model (LSEM) can be interpreted as an ACME estimator if the linearity and no-interaction assumptions are satisfied in addition to the proposed assumption. We show that this assumption can easily be relaxed within the framework of LSEM. Second, we consider a simple nonparametric estimator of the ACME in order to relax distributional and functional form assumptions. We also discuss a more general nonparametric approach. Third, we propose a new sensitivity analysis that can be easily implemented by applied researchers within the standard LSEM framework. Like the existing identifying assumptions, the proposed assumption may be too strong in many applied settings. Thus, sensitivity analysis is essential in order to examine the robustness of empirical findings to the possible existence of an unmeasured confounder. Finally, we apply the proposed methods to a randomized experiment from political psychology.

24
Paper
Direction and Intensity of Russian Macroeconomic Evaluations
Jones, Bradford S.
Willerton, John P.
Sobel, Michael E.

Uploaded 08-30-1998
Keywords Russia
public opinion
log linear models
Abstract The Russian macroeconomy has exhibited volatility since the transformation from the Soviet Union to the Russian Federation. Much is known about the Russian public opinion climate during the end of the Soviet era and the beginning of the Russian Federation era; however, less well understood is the nature of Russians' macroeconomic evaluations during this on-going transformation. In this paper, we analyze Russians' assessments of the macroeconomy using Russian public opinion data asking respondents to assess the Russian national economy. We establish four testable hypotheses. First, we hypothesize that the direction of Russian opinion will be asymmetrically more negative than positive across all periods in the study. Second, we hypothesize that economic assessments will vary by residential region. Specifically, we contend the response distribution for respondents from Moscow and St. Petersburg (MSP) will differ from respondents from other residential regions. Third (and related to the second), we hypothesize that the response distributions for MSP respondents will be temporally heterogenous while the response distribution for respondents outside MSP will be temporally homogenous. Fourth, we hypothesize that despite the poor performance of the economy during the Russian Federation transition, Russian public opinion will not exhibit extreme negativity in macroeconomic evaluations. Using published survey data collected from the bi -monthly extsl{Russian Public Opinion Monitor} conducted by the Russian Center for Public Opinion Research (VCIOM), for the period January 1994 to July 1996, we examine both the direction and intensity of Russian opinion toward the state of the national economy by estimating the distribution on the response variable using an adjacent category logit model (Jones and Sobel 1998, Sobel 1995, 1997, 1998). From our analysis, we find first that the direction of Russians' evaluation of the macroeconomy is consistently negative rather than positive---a finding that corroborates extant research; however, the directional nature of economic assessments displays significant residential variation between MSP and the rest of the country. Second, we find significant residential variation in economic assessments. Specifically, the response distribution for MSP respondents can be distinguished from the response distribution from respondents in other residential regions, and also, the response distribution for MSP respondents displays considerable temporal heterogeneity. We argue this variability tends to follow changes in the macroeconomic and political environments. Third, we do not find support for the hypothesis of temporal homogeneity in the response distribution for respondents outside of MSP. Nevertheless, residents in other cities and in rural regions seem not to be as responsive to macroeconomic changes over the period, thus eliciting milder temporal variability than MSP respondents. Fourth, we find that in terms of the response distribution, the intensity of Russian pessimism (or optimism) is extsl{not} extreme.

25
Paper
Bayesian Methods: A Social and Behavioral Sciences Approach, ANSWER KEY TO THE SECOND EDITION. Odd Numbers.
Park, Hong Min
Gill, Jeff

Uploaded 09-14-2010
Keywords Bayes
modeling
simulation
Bayesian inference
MCMC
prior
posterior
Bayes Factor
DIC
GLM
Markov chain
Monte Carlo
hierarchical models
linear
nonlinear
Abstract This is the odd-numbered exercise answers to the second edition of Bayesian Methods: A Social and Behavioral Sciences Approach (minus Chapter 13). Course Instructors can get the full set from Chapman & Hall/CRC upon request.


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