1

Paper

Endogeneity in Probit Response Models Freedman, David Sekhon, Jasjeet Uploaded 05-28-2008 Keywords Bivariate probit sample selection identification indefinite Hessian optimization Abstract In this paper, we look at conventional methods for removing endogeneity bias in regression models, including the linear model and the probit model. The usual Heckman two-step procedure should not be used in the probit model: from a theoretical perspective, this procedure is unsatisfactory, and likelihood methods are superior. However, serious numerical problems occur when standard software packages try to maximize the biprobit likelihood function, even if the number of covariates is small. The log likelihood surface may be nearly flat, or may have saddle points with one small positive eigenvalue and several large negative eigenvalues. We draw conclusions for statistical practice. Finally, we describe the conditions under which parameters in the model are identifable; these results appear to be new.

2

Paper

The Trouble with Tobit: A District-Level Sample Selection Model of Voting for Extreme Right Parties in Europe, 1980-2004 Bowyer, Benjamin Uploaded 07-07-2008 Keywords Tobit Heckman sample selection censored data aggregate data extreme right parties Abstract The growing electoral success of extreme right parties (ERPs) in many European countries has sparked academic interest in explaining variation in extreme right success. However, much of the extant research on the electoral success of extreme right parties suffers from at least two types of selection bias. The first involves the selection of cases and occurs when only those national elections that were contested by extreme right parties are included in the cross-national analysis. To address this problem, a growing number of scholars of ERP electoral support employ Tobit models to analyze national-level election results pooled across countries and election years. However, this approach conceals a second source of selection bias: ERPs are extremely selective about which election districts within a country they choose to contest. The correct specification of this process of self-selection requires the recognition of two fundamental points. First, the causal factors that determine whether an extreme right party contests an election are not identical to those that influence its share of the vote if it does appear on the ballot. Second, this decision about when and where to field candidates is one that is observable at the level of the election district. This paper argues that the appropriate way to model is as a Heckman sample selection model estimated at the level of electoral district. I present a preliminary analysis of a dataset that pools district-level election results for eighteen European countries from 1980-2004 (N=12,050), the results of which demonstrate the value of this approach.

3

Poster

Weighted Estimation for Analyses with Missing Data Samii, Cyrus Uploaded 07-21-2010 Keywords missing data doubly robust inverse probability weighting semi-parametric post-treatment regression sample selection Abstract Missing data plague data analyses in political science. The recent applied statistics literature reflects renewed interest in weighting methods for missing data problems. Three properties are stressed in this literature: (i) robustness, (ii) the ability to use post-treatment information in causal analysis, and (iii) methods to gain efficiency. I present these results, hoping to show the potential in using refashioned weighting methods for political science research.