论文标题
未观察到异质性的样本选择模型的空间差异
Spatial Differencing for Sample Selection Models with Unobserved Heterogeneity
论文作者
论文摘要
本文在未观察到的异质性的样本选择模型中使用空间差异得出了识别，估计和推理结果。我们表明，在未观察到的亚线特定异质性和逆磨坊比的空间之间平稳变化的假设下，确定了样本选择模型的关键参数。亚线特定异质性的平滑度意味着结果的相关性。我们假设相关性受到位置或群集内的限制，并得出渐近结果表明，随着独立簇的数量增加，估计器的数量是一致的，并且渐近地正常。我们还提出了标准误差估计的公式。蒙特卡洛实验说明了我们估计量的较小样品特性。我们的程序在估计芬兰市政当局税率的决定因素表明，对未观察到的异质性的重要性。
This paper derives identification, estimation, and inference results using spatial differencing in sample selection models with unobserved heterogeneity. We show that under the assumption of smooth changes across space of the unobserved sub-location specific heterogeneities and inverse Mills ratio, key parameters of a sample selection model are identified. The smoothness of the sub-location specific heterogeneities implies a correlation in the outcomes. We assume that the correlation is restricted within a location or cluster and derive asymptotic results showing that as the number of independent clusters increases, the estimators are consistent and asymptotically normal. We also propose a formula for standard error estimation. A Monte-Carlo experiment illustrates the small sample properties of our estimator. The application of our procedure to estimate the determinants of the municipality tax rate in Finland shows the importance of accounting for unobserved heterogeneity.
