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Data Mining for Theorists Kenkel, Brenton Signorino, Curtis Uploaded 07-26-2011 Keywords empirical implications of theoretical models basis regression adaptive lasso bootstrap functional form misspecification Abstract Among those interested in statistically testing formal models,two approaches dominate. The structural estimation approach derives a structural probability model based on the formal model and then estimates parameters associated with that model. The reduced-form approach generally applies off-the-shelf techniques---such as OLS, logit, or probit---to test whether the independent variables are related to a decision variable according to the comparative statics predictions. We provide a new statistical method for the comparative statics approach. The decision variable of interest is modeled as a polynomial function of the available covariates, which allows for the nonmonotonic and interactive relationships commonly found in strategic choice data. We use the adaptive lasso to reduce the number of parameters and prevent overfitting, and we obtain measures of uncertainty via the nonparametric bootstrap. The method is "data mining" because the aim is to discover complex relationships in data without imposing a particular structure,but "for theorists" in that it was developed specifically to deal with the peculiar features of data on strategic choice. Using a Monte Carlo simulation, we show that the method handily outperforms other non-structural techniques in estimating a nonmonotonic relationship from strategic choice data.

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Copula Functions for Approval Ratings and Endogenous Political Events Quiroz Flores, Alejandro Uploaded 07-27-2011 Keywords copula functions approval ratings endogenous bivariate distribution Abstract Empirical investigations of US presidential approval ratings often control for the exogenous materialization and duration of particular events such as wars or political scandals. Although the materialization of some of these events might be exogenous, their duration is not. Once an event takes place, a President has sufficient power to reduce its duration if the event reduces approval ratings, or increase its duration if the event is politically beneficial. In other words, the duration of these events is endogenous. In order to address this potential problem, this paper uses a copula function to jointly estimate presidential approval ratings (with a Normal distribution) and the duration of significant political events (with a Weibull distribution). The estimation of this bivariate Normal-Weibull distribution will help us test whether and to what extent American presidents manipulate political events in order to increase their approval ratings. Estimation results from the bivariate Normal-Weibull distribution suggest that there is a positive, two-way association between presidential approval ratings and the duration of political events.

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Comparative Effectiveness of Matching Methods for Causal Inference King, Gary Nielsen, Richard Uploaded 07-27-2011 Keywords Causal Inference Matching Propensity Scores Abstract Matching is an increasingly popular method of causal inference in observational data, but applications of it are often poorly executed. We address this problem by providing a graphical approach for choosing among the numerous possible matching solutions generated by three methods: the venerable "Mahalanobis Distance Matching" (MDM), the commonly used "Propensity Score Matching" (PSM), and a newer approach called "Coarsened Exact Matching" (CEM). In the process of using our approach, we also discover that PSM often approximates random matching, both in real applications and in data simulated by the processes for which PSM theory was designed. Moreover, contrary to conventional wisdom, random matching is not benign: it (and thus PSM) can degrade inferences relative to not matching at all. We find that MDM and CEM do not have this problem, and in practice CEM usually outperforms the other two approaches. However, with our comparative graphical approach, focus is on choosing a matching solution for a particular application, which is what may improve inferences, rather than the particular method used to generate it. The easyto- follow procedures we describe thus enable researchers to improve the application of any one of these methods, to choose among them and from the various matching solutions generated by any one method, and ultimately to increase the validity and extent of causal information extracted from their data. Link to paper: http://gking.harvard.edu/files/psparadox.pdf

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Racing Horses: Constructing and Evaluating Forecasts in Political Science Brandt, Patrick Freeman, John R. Schrodt, Philip Uploaded 07-27-2011 Keywords forecasting political conflict scoring rules model training forecast density verification rank histogram probability integral transform Abstract We review methods for forecast evaluations and how they can be used in political sciences. We examine how forecast densities are more useful summaries of forecasted variables than point metrics. We also cover how continuous rank probability scores, probability integral transforms, and verification rank histograms can be used to calibrate and evaluate forecast performance. Finally, we present two illustrations, one a simulation and the other a comparison of forecasting models for the China-Taiwan (cross-straits) conflict.

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Agnostic Notes on Regression Adjustments to Experimental Data: Reexamining Freedman's Critique Lin, Winston Uploaded 09-02-2011 Keywords Covariate adjustment Randomization inference Neyman's repeated sampling approach Sandwich estimator Social experiments Abstract Freedman [Adv. in Appl. Math. 40 (2008a) 180–193; Ann. Appl. Stat. (2008b) 2 176–196] critiqued OLS regression adjustment of estimated treatment effects in randomized experiments, using Neyman’s model for randomization inference. This paper argues that in sufficiently large samples, the statistical problems he raised are either minor or easily fixed. OLS adjustment improves or does not hurt asymptotic precision when the regression includes a full set of treatment-covariate interactions. Asymptotically valid confidence intervals can be constructed with the Huber-White sandwich standard error estimator. Even the traditional OLS adjustment has benign large-sample properties when subjects are randomly assigned to two groups of equal size. The strongest reasons to support Freedman’s preference for unadjusted estimates are transparency and the dangers of specification search.

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Multivariate Matching Methods That are Monotonic Imbalance Bounding King, Gary Iacus, Stefano Porro, Giuseppe Uploaded 01-03-2011 Keywords Matching CEM Causal Inference Abstract We introduce a new ``Monotonic Imbalance Bounding'' (MIB) class of matching methods for causal inference with a surprisingly large number of attractive statistical properties. MIB generalizes and extends in several new directions the only existing class, ``Equal Percent Bias Reducing'' (EPBR), which is designed to satisfy weaker properties and only in expectation. We also offer strategies to obtain specific members of the MIB class, and analyze in more detail a member of this class, called Coarsened Exact Matching, whose properties we analyze from this new perspective. We offer a variety of analytical results and numerical simulations that demonstrate how members of the MIB class can dramatically improve inferences relative to EPBR-based matching methods.

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The Split Population Logit (SPopLogit): Modeling Measurement Bias in Binary Data Beger, Andreas DeMeritt, Jacqueline Hwang, Wonjae Moore, Will Uploaded 02-28-2011 Keywords split populations binary data measurement error bias zero inflation substantive inference Abstract Researchers frequently face applied situations where their measurement of a binary outcome suffers from bias. Social desirability bias in survey work is the most widely appreciated circumstance, but the strategic incentives of human beings similarly induce bias in many measures outside of survey research (e.g., whether the absence of an armed attack indicates a country's satisfaction with the status quo or a calculation that the likely costs of war outweigh the likely benefits). In these circumstances the data we are able to observe do not reflect the distribution we wish to observe. This study introduces a statistical model that permits researchers to model the process that produces the bias, the split population logit (SPopLogit) model. It further presents a Monte Carlo simulation that demonstrates the effectiveness of the SPopLogit model, and then re-analyzes a study of sexual infidelity to illustrate the richness of the quantities of (empirical and theoretical) interest that can be estimated with the model. {\tt Stata ado} files that can be used to invoke the SPopLogit, as well as batch files that illustrate how to simulate commonly reported quantities of interest, are available for download from the WWW. The authors close by briefly identifying just a few of the many types of research projects that will benefit from abandoning logit and probit models in favor of the SPopLogit. NOTE: Files to implement SPopLogit and generate quantities of interest, as well as replication files for our Monte Carlo simulations and substantive application, are available at http://andybeger.wordpress.com/2011/02/03/split-population-logit/

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Beyond LATE: A Simple Method for Recovering Sample Average Treatment Effects Aronow, Peter Sovey, Allison Uploaded 03-24-2011 Keywords compliance score instrumental variables LATE average treatment effect causal inference Abstract Political scientists frequently use instrumental variables estimators to estimate the Local Average Treatment Effect (LATE), or the average treatment effect among those who comply with treatment assignment. However, the LATE is often not the causal estimand of interest; researchers may instead be interested in the Sample Average Treatment Effect (SATE), or the average treatment effect for the entire sample. We first introduce the compliance score, a pre-treatment covariate that reflects a unit's probability of treatment compliance, to researchers in political science. We posit a maximum likelihood estimation technique for predicting compliance scores even in the presence of two-sided non-compliance. We then develop a new technique, inverse compliance score weighting, that, in conjunction with a standard IV estimator, will allow researchers to easily estimate the SATE. Finally, we estimate both the LATE and SATE for a randomized experiment designed to measure the effects of media exposure and reach striking substantive conclusions.

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Modeling Electoral Coordination: Voters, Parties and Legislative Lists in Uruguay Levin, Ines Katz, Gabriel Uploaded 04-20-2011 Keywords electoral coordination number of parties Bayesian estimation multilevel modeling strategic voting Abstract During each electoral period, the strategic interaction between voters and political elites determines the number of viable candidates in a district. In this paper, we implement a hierarchical seemingly unrelated regression model to explain electoral coordination at the district level in Uruguay as a function of district magnitude, previous electoral outcomes and electoral regime. Elections in this country are particularly useful to test for institutional effects on the coordination process due to the large variations in district magnitude, to the simultaneity of presidential and legislative races held under different rules, and to the reforms implemented during the period under consideration. We find that district magnitude and electoral history heuristics have substantial effects on the number of competing and voted-for parties and lists. Our modeling approach uncovers important interaction-effects between the demand and supply side of the political market that were often overlooked in previous research.