We propose the use of Kernel Regularized Least Squares (KRLS) for social science modeling and inference problems. KRLS borrows from machine learning methods designed to solve regression and classification problems without relying on linearity or additivity assumptions. The method constructs a flexible hypothesis space that uses kernels as radial basis functions and finds the best-fitting surface in this space by minimizing a complexity-penalized least squares problem. We argue that the method is well-suited for social science inquiry because it avoids strong parametric assumptions, yet allows interpretation in ways analogous to generalized linear models while also permitting more complex interpretation to examine nonlinearities, interactions, and heterogeneous effects. We also extend the method in several directions to make it more effective for social inquiry, by (1) deriving estimators for the pointwise marginal effects and their variances, (2) establishing unbiasedness, consistency, and asymptotic normality of the KRLS estimator under fairly general conditions, (3) proposing a simple automated rule for choosing the kernel bandwidth, and (4) providing companion software. We illustrate the use of the method through simulations and empirical examples.

The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alter their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.

Estimators within the Cox family are often used to estimate models for repeated events. Yet, there is much we still do not know about the performance of these estimators. In particular, we do not know how they perform given time dependence, different censoring rates, and a varying number of events and sample sizes. We use Monte Carlo simulations to demonstrate the performance of a variety of popular semi-parametric estimators as these data aspects change and under conditions of event dependence and heterogeneity, both, or neither. We conclude that the conditional frailty model outperforms other standard estimators under a wide array of data-generating processes, and data limitations rarely alter its performance.

This article presents a scaling approach to jointly estimate the locations of voters, parties, and European political groups on a common left-right scale. Although most comparative research assumes that cross-national comparisons of voters and parties are possible, few correct for systematic biases commonly known to exist in surveys or examine whether survey data are comparable across countries. Our scaling method addresses scale perception in surveys and links cross-national surveys through new bridging observations. We apply our approach to the 2009 European Election Survey and demonstrate that the improvement in party estimates that one gains from fixing various survey bias issues is significant. Our scaling strategy provides left-right positions of voters and of 162 political parties, and we demonstrate that variables based on rescaled voter and party positions on the left-right dimension significantly improve the fit of a cross-national vote choice model.

Due in large part to the proliferation of digitized text, much of it available for little or no cost from the Internet, political science research has experienced a substantial increase in the number of data sets and large-n research initiatives. As the ability to collect detailed information on events of interest expands, so does the need to efficiently sort through the volumes of available information. Automated document classification presents a particularly attractive methodology for accomplishing this task. It is efficient, widely applicable to a variety of data collection efforts, and considerably flexible in tailoring its application for specific research needs. This article offers a holistic review of the application of automated document classification for data collection in political science research by discussing the process in its entirety. We argue that the application of a two-stage support vector machine (SVM) classification process offers advantages over other well-known alternatives, due to the nature of SVMs being a discriminative classifier and having the ability to effectively address two primary attributes of textual data: high dimensionality and extreme sparseness. Evidence for this claim is presented through a discussion of the efficiency gains derived from using automated document classification on the Militarized Interstate Dispute 4 (MID4) data collection project.

The random utility model (RUM) of voting behavior can account for strategic voting by making use of proxy indicators that measure voter incentives to vote strategically. The contribution of this article is to propose a new method to estimate the RUM in the presence of strategic voters, without having to construct proxy measures of strategic voting incentives. Our method can be used to infer the counterfactual sincere vote of those who vote strategically and provides an estimate of the size of strategic voting. We illustrate the procedure using post-electoral survey data from Spain. Our calculations indicate that strategic voting in Spain is about 2.19%.

This article investigates inconsistency and invalid statistical inference that often characterize dynamic panel analysis in international political economy. These econometric concerns are tied to Nickell bias and cross-sectional dependence. First, we discuss how to avoid Nickell bias in dynamic panels. Second, we put forward factor-augmented dynamic panel regression as a means for addressing cross-sectional dependence. As a specific application, we use our methods for an analysis of the impact of terrorism on economic growth. Different terrorism variables are shown to have no influence on economic growth for five regional samples when Nickell bias and cross-dependence are taken into account. Our finding about terrorism and growth is contrary to the extant literature.