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This article investigates the role of voter turnout in school bond election outcomes. It is widely believed that turnout is negatively related to bond approval rates. Conclusions from previous empirical research, however, may be misleading because many sociodemographic factors and election parameters that influence bond support are also likely to influence voter turnout decisions. To account for the endogeneity of turnout, we employ an instrumental variable approach. We find that the persistent part of voter turnout plays a negligible role in explaining bond approval shares conditioned on election timing, past voting behavior, and district characteristics. Using first-difference models, change in turnout has a negative and significant influence on change in approval share and probability of bond success. Our results support previous research and suggest that targeted voter mobilization strategies have the potential to influence school bond outcomes.

This note considers a three-step non-Gaussian quasi-maximum likelihood estimation (TS-NGQMLE) of the double autoregressive model with its asymptotics, which improves efficiency of the GQMLE and circumvents inconsistency of the NGQMLE when the innovation is heavy-tailed. Under mild conditions, the estimator not only can achieve consistency and asymptotic normality regardless of density misspecification of the innovation, but also outperforms the existing estimators, such as the GQMLE and the (weighted) least absolute deviation estimator, when the innovation is indeed heavy-tailed.

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Knowing the error distribution is important in many multivariate time series applications. To alleviate the risk of error distribution mis-specification, testing methodologies are needed to detect whether the chosen error distribution is correct. However, the majority of the existing tests only deal with the multivariate normal distribution for some special multivariate time series models, and they thus can not be used to testing for the often observed heavy-tailed and skewed error distributions in applications. In this paper, we construct a new consistent test for general multivariate time series models, based on the kernelized Stein discrepancy. To account for the estimation uncertainty and unobserved initial values, a bootstrap method is provided to calculate the critical values. Our new test is easy-to-implement for a large scope of multivariate error distributions, and its importance is illustrated by simulated and real data.