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Simultaneous selection of optimal bandwidths for the sharp regression discontinuity estimator

机译:同时为锐利回归不连续估计器选择最佳带宽

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A new bandwidth selection method that uses different bandwidths for the local linear regression estimators on the left and the right of the cut‐off point is proposed for the sharp regression discontinuity design estimator of the average treatment effect at the cut‐off point. The asymptotic mean squared error of the estimator using the proposed bandwidth selection method is shown to be smaller than other bandwidth selection methods proposed in the literature. The approach that the bandwidth selection method is based on is also applied to an estimator that exploits the sharp regression kink design. Reliable confidence intervals compatible with both of the proposed bandwidth selection methods are also proposed as in the work of Calonico, Cattaneo, and Titiunik (2014a). An extensive simulation study shows that the proposed method's performances for the samples sizes 500 and 2000 closely match the theoretical predictions. Our simulation study also shows that the common practice of halving and doubling an optimal bandwidth for sensitivity check can be unreliable.
机译:提出了一种新的带宽选择方法,该方法对截止点左右两侧的局部线性回归估计器使用不同的带宽,以针对截止点处的平均处理效果进行尖锐的回归间断设计估计器。结果表明,使用所提出的带宽选择方法的估计器的渐进均方误差小于文献中提出的其他带宽选择方法。带宽选择方法所基于的方法也被应用于利用锐利回归扭结设计的估计器。与Calonico,Cattaneo和Titiunik(2014a)的工作一样,也提出了与两种建议的带宽选择方法兼容的可靠置信区间。广泛的仿真研究表明,该方法对500和2000样本量的性能与理论预测非常吻合。我们的仿真研究还表明,将灵敏度检查的最佳带宽减半和加倍的常规做法可能不可靠。

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