Publication Type: Conference Paper/Unpublished Manuscript
Abstract: Quasi-instrumental variables are instruments that are not perfectly exogenous. I examine how different instrumental variable estimators are affected by using quasi-instruments instead of true instruments, with an application to campaign spending.

Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: Quasi-instrumental variables are instruments that are not perfectly exogenous (Bartels 1991). In this paper, I examine how different instrumental variable estimators are affected by using quasi-instruments instead of true instruments. Using Monte Carlo methods, I explore the properties of 2SLS, LIML, and Jackknife estimators. I find that all estimators are seriously biased and inconsistent. I then use these methods to estimate the effect of spending on electoral success in U.S. Senate elections, using data from Gerber (1998).
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Publication Type: Conference Paper/Unpublished Manuscript
Abstract: With a specific focus on the quality of instruments and parameter uncertainty, we provide practical tests of relevance and orthogonality for instrumental variables estimators in political research.

Publication Type: Conference Paper/Unpublished Manuscript
Review Method: Peer Reviewed
Abstract: Propensity score matching provides an estimate of the effect of a “treatment” variable on an outcome variable that is largely free of bias arising from an association between treatment status and observable variables. However, matching methods are not robust against “hidden bias” arising from unobserved variables that simultaneously affect assignment to treatment and the outcome variable. One strategy for addressing this problem is the Rosenbaum bounds approach, which allows the analyst to determine how strongly an unmeasured confounding variable must affect selection into treatment in order to undermine the conclusions about causal effects from a matching analysis. Instrumental variables (IV) estimation provides an alternative strategy for the estimation of causal effects, but the method typically reduces the precision of the estimate and has an additional source of uncertainty that derives from the untestable nature of the assumptions of the IV approach. A method of assessing this additional uncertainty is proposed so that the total uncertainty of the IV approach can be compared with the Rosenbaum bounds approach to uncertainty using matching methods. Because the approaches rely on different information and different assumptions, they provide complementary information about causal relationships. The approach is illustrated via an analysis of the impact of unemployment insurance on the timing of reemployment, the post-unemployment wage, and the probability of relocation, using data from several panels of the Survey of Income and Program Participation (SIPP).

Publication Type: Proceeding