Models of ideal point estimation usually build on the assumption of spatial preferences. This ignores legislators' non-policy incentives and is thus likely to produce implausible results for many legislatures. We study this problem in parliamentary systems and develop a model of roll call voting that considers both the policy and the non-policy, tactical incentives of legislators. We go on to show how the relative weight of these policy and tactical incentives is influenced by the identity of the mover and characteristics of the motion. Analyses of two data sets of 2174 roll call votes in German state legislatures and 3295 roll call votes in the British House of Commons result in three main findings. First, we show that tactical incentives may be more important than policy incentives, and second, that the importance of tactical incentives varies with the importance of motions. Third, there are interesting twists: backbench private members' bills may reverse tactical incentives whereas proposals from anti-system parties are virtually always rejected by moderate parties, rendering these votes uninformative. Our findings have implications for ideal point estimation in parliamentary systems, as well as for research on separation of power systems.

Cointegration was introduced to our discipline by Renée Smith and Charles Ostrom Jr. and by Robert Durr more than two decades ago at political methodology meetings in Washington University�St. Louis and Florida State University. Their articles, along with comments by Neal Beck and John T. Williams, were published in a symposium like this one in the fourth volume of Political Analysis. Keele, Lin, and Webb (2016; hereafter KLW) and Grant and Lebo (2016; hereafter GL) show how, in the years that followed, cointegration was further evaluated by political scientists, and the related idea of error correction subsequently was applied.

Have the last twenty-plus years witnessed significant progress in modeling nonstationary political time series? In some respects, the answer is yes. The present symposium represents progress in understanding equation balance, analyzing bounded variables, and decomposing short- and longterm causal effects. In these respects KLW's and GL's articles deserve wide dissemination. But KLW and GL leave important methodological issues unresolved. They do not address some critical methodological challenges. From a historical perspective, the present symposium shows that we have made relatively little progress in modeling nonstationary political time series.

Measures of diversity and disparity within a population are used for investigating a range of developmental outcomes, but often by employing “off-the-shelf” indicators that may not be theoretically appropriate for the hypotheses under investigation. In this article, we proposed a general class of social distance measures that both enables us to see the conceptual relationship between different existing measures of heterogeneity more clearly and is sufficiently flexible to allow for the development of tailored hypothesis-specific measures. We show how a range of existing aggregate measures of diversity and disparity fit within the general class and demonstrate illustratively how the measure can be used to develop more precise hypothesis-specific measures.

Bowers, Fredrickson, and Panagopoulos (2013, Reasoning about interference between units: A general framework, Political Analysis 21(1):97–124; henceforth BFP) showed that one could use Fisher's randomization-based hypothesis testing framework to assess counterfactual causal models of treatment propagation and spillover across social networks. This research note improves the statistical inference presented in BFP (2013) by substituting a test statistic based on a sum of squared residuals and incorporating information about the fixed network for the simple Kolmogorov–Smirnov test statistic (Hollander 1999, section 5.4) they used. This note incrementally improves the application of BFP's “reasoning about interference” approach. We do not offer general results about test statistics for multi-parameter causal models on social networks here, but instead hope to stimulate further, and deeper, work on test statistics and sharp hypothesis testing.

Grant and Lebo (2016) and Keele, Linn, and Webb (2016) provide diverging recommendations to analysts working with short time series that are potentially fractionally integrated. While Grant and Lebo are quite positive about the prospects of fractionally differencing such data, Keele, Linn, and Webb argue that estimates of fractional integration will be highly uncertain in short time series. In this study, I simulate fractionally integrated data and compare estimates from the general error correction model (GECM), which disregards fractional integration, to models using fractional integration methods over thirty-two simulation conditions. I find that estimates of short-run effects are similar across the two models, but that models using fractionally differenced data produce superior predictions of long-run effects for all sample sizes when there are no short-run dynamics included. When short-run dynamics are included, the GECM outperforms the alternative model, but only in time series that consist of under 250 observations.

The appropriateness of experiments for studying causal mechanisms is well established. However, the ability of an experiment to isolate the effect of emotion has received less attention, and in this letter we lay out a guide to manipulating and tracing the impact of emotions. Some experimental manipulations are straightforward. Manipulating an emotion like anxiety is less obvious. There is no magic “political anxiety pill” and placebo that can be randomly assigned to participants. While the magic political anxiety pill is still elusive, we advocate using multiple manipulations, extensive pretesting, and mediation models. These approaches have allowed us to situate a discrete emotional experience in a complex political environment.

Guessing on closed-ended knowledge items is common. Under likely-to-hold assumptions, in the presence of guessing, the most common estimator of learning, difference between pre- and postprocess scores, is negatively biased. To account for guessing-related error, we develop a latent class model of how people respond to knowledge questions and identify the model with the mild assumption that people do not lose knowledge over short periods of time. A Monte Carlo simulation over a broad range of informative processes and knowledge items shows that the simple difference score is negatively biased and the method we develop here is unbiased. To demonstrate its use, we apply our model to data from Deliberative Polls. We find that estimates of learning, once adjusted for guessing, are about 13% higher. Adjusting for guessing also eliminates the gender gap in learning, and halves the pre-deliberation gender gap on political knowledge.

Legislative redistricting alters the political and electoral context for some voters but not others, thus offering a potentially promising research design to study many questions of interest in political science. We apply this design to study the effect that descriptive representation has on co-ethnic political engagement, focusing on Hispanic participation following California's 2000 redistricting cycle. We show that when redistrictors draw legislative boundaries in California's 1990, 2000, and 2010 apportionment cycles, they systematically sort higher-participating Hispanic voters into majority-Hispanic (MH) jurisdictions represented by co-ethnic candidates, biasing subsequent comparisons of Hispanic participation across districts. Similar sorting occurs during redistricting in Florida and Texas, though here the pattern is reversed, with less-participating Hispanic voters redistricted to MH districts. Our study highlights important heterogeneity in redistricting largely unknown or underappreciated in previous research. Ignoring this selection problem could significantly bias estimates of the effect of Hispanic representation, either positively or negatively. After we correct for these biases using a hierarchical genetic matching algorithm, we find that, in California, being moved to a district with an Hispanic incumbent has little impact on Hispanic participation in our data.

Political scientists frequently wish to test hypotheses about the effects of specific emotions on political behavior. However, commonly used experimental manipulations tend to have collateral effects on emotions other than the targeted emotion, making it difficult to ascribe outcomes to any single emotion. In this letter, we propose to address this problem using causal mediation analysis. We illustrate this approach using an experiment examining the effect of emotion on dyadic trust, as measured by the trust game. Our findings suggest that negative emotions can decrease trust, but only if those negative emotions make people feel less certain about their current situation. Our results suggest that only anxiety, a low-certainty emotion, has a negative impact on trust, whereas anger and guilt, two emotions that differ in their control appraisals but induce the same high level of certainty, appear to have no effect on trusting behavior. Importantly, we find that failing to use causal mediation analysis would ascribe a positive effect of anxiety on trust, demonstrating the value of this approach.

The papers in this symposium agree on several points. In this article, we sort through some remaining areas of disagreement and discuss some of the practical issues of time series modeling we think deserve further explanation. In particular, we have five points: (1) clarifying our stance on the general error correction model in light of the comments in this issue; (2) clarifying equation balance and discussing how bounded series affects our thinking about stationarity, balance, and modeling choices; (3) answering lingering questions about our Monte Carlo simulations and exploring potential problems in the inferences drawn from long-run multipliers; (4) reviewing and defending fractional integration methods in light of the questions raised in this symposium and elsewhere; and (5) providing a short practical guide to estimating a multivariate autoregressive fractionally integrated moving average model with or without an error correction term.

This issue began as an exchange between Grant and Lebo (2016) and ourselves (Keele, Linn, and Webb 2016) about the utility of the general error correction model (GECM) in political science. The exchange evolved into a debate about Grant and Lebo's proposed alternative to the GECM and the utility of fractional integration methods (FIM). Esarey (2016) and Helgason (2016) weigh in on this part of the debate. Freeman (2016) offers his views on the exchange as well. In the end, the issue leaves readers with a lot to consider. In his comment, Freeman (2016) argues that the exchange has produced little significant progress because of the contributors' failures to consider a wide array of topics not directly related to the GECM or FIM. We are less pessimistic. In what follows, we distill what we believe are the most important elements of the exchange–the importance of balance, the costs and benefits of FIM, and the vagaries of pre-testing.

A dominant trend in models with binary outcomes is to control for unmodeled duration dependence by including temporal dependence variables. A second, distinct trend is to interpret both the short- and long-term effects of explanatory variables in autoregressive models. While the first trend is nearly ubiquitous in models with binary outcomes, the second trend has yet to be applied consistently beyond models with continuous outcomes. While scholars use temporal splines and cubic polynomials to model the underlying hazard rate, they have neglected the fact that this causes the explanatory variables to have a long-term effect (LTE) by modifying the future values of the temporal dependence variables. In this article, I propose a simple technique that estimates a wide range of probabilistic LTEs in models with temporal dependence variables. These effects can range from simple LTEs for a one-time change in an explanatory variable to more complex scenarios where effects change in magnitude with time and compound across repeated events. I then replicate Clare's (2010, Ideological fractionalization and the international conflict behavior of parliamentary democracies. International Studies Quarterly 54:965–87) examination of the influence of government fractionalization on conflict behavior to show that failing to interpret the results within the context of temporal dependence underestimates the total impact of fractionalization by neglecting LTEs.

By adding an informational component to the judicial review, Rogers (2001) argued that an independent court can be created and maintained by a legislature. This influential article, however, has one important mistake in its game-theoretical model that changes the equilibrium results and ultimately undermines the theoretical contribution to the discipline. The legislature no longer enjoys informational benefits by having an independent court.

In both political behavior research and voting rights litigation, turnout and vote choice for different racial groups are often inferred using aggregate election results and racial composition. Over the past several decades, many statistical methods have been proposed to address this ecological inference problem. We propose an alternative method to reduce aggregation bias by predicting individual-level ethnicity from voter registration records. Building on the existing methodological literature, we use Bayes's rule to combine the Census Bureau's Surname List with various information from geocoded voter registration records. We evaluate the performance of the proposed methodology using approximately nine million voter registration records from Florida, where self-reported ethnicity is available. We find that it is possible to reduce the false positive rate among Black and Latino voters to 6% and 3%, respectively, while maintaining the true positive rate above 80%. Moreover, we use our predictions to estimate turnout by race and find that our estimates yields substantially less amounts of bias and root mean squared error than standard ecological inference estimates. We provide open-source software to implement the proposed methodology.

The most commonly used statistical models of civil war onset fail to correctly predict most occurrences of this rare event in out-of-sample data. Statistical methods for the analysis of binary data, such as logistic regression, even in their rare event and regularized forms, perform poorly at prediction. We compare the performance of Random Forests with three versions of logistic regression (classic logistic regression, Firth rare events logistic regression, and L1-regularized logistic regression), and find that the algorithmic approach provides significantly more accurate predictions of civil war onset in out-of-sample data than any of the logistic regression models. The article discusses these results and the ways in which algorithmic statistical methods like Random Forests can be useful to more accurately predict rare events in conflict data.

One of the most robust findings on political institutions is that compulsory voting (CV) reduces the participation gap between poorer and wealthier voters. We present evidence that in Brazil, the largest country to use such a rule, CV increases inequality in turnout. We use individual-level data on 140 million Brazilian citizens and two age-based discontinuities to estimate the heterogeneous effects of CV by educational achievement, a strong proxy for socioeconomic status. Evidence from both thresholds shows that the causal effect of CV on turnout among the more educated is at least twice the size of the effect among those with less education. To explain this result, which is the opposite of what is predicted by the existing literature, we argue that nonmonetary penalties for abstention primarily affect middle- and upper-class voters and thus increase their turnout disproportionately. Survey evidence from a national sample provides evidence for the mechanism. Our results show that studies of CV should consider nonmonetary sanctions, as their effects can reverse standard predictions.

Sensitivity diagnostics has recently been put high on the agenda of methodological research into Qualitative Comparative Analysis (QCA). Existing studies in this area rely on the technique of exhaustive enumeration, and the majority of works examine the reactivity of QCA either only to alterations in discretionary parameter values or only to data quality. In this article, we introduce the technique of combinatorial computation for evaluating the interaction effects between two problems afflicting data quality and two discretionary parameters on the stability of QCA reference solutions. In this connection, we challenge a hitherto unstated assumption intrinsic to exhaustive enumeration, show that combinatorial computation permits the formulation of general laws of sensitivity in QCA, and demonstrate that our technique is most efficient.

Much of the data used in social science is aggregated into spatial units, even if the analysis itself does not explicitly incorporate that information. A key concern with such aggregation, however, is that changes in the units of aggregation themselves cause difficulty in comparing data gathered on the old boundaries and the new boundaries. Such changes present serious concerns to researchers who may exclude observations or cases due to a lack of comparable units or omit certain key variables. While geographers have long examined this problem and created methods of projecting data from one spatial unit into another, known as areal interpolation, it is telling that a recent article notes the difficulty for researchers in implementing even the most basic solutions without relying heavily on programming skills or proprietary software.