Closed-form estimation and inference for panels with attrition and refreshment samples
针对面板数据存在样本流失时的问题，提出一个非参数识别假设，并给出无需调参与优化的闭式估计算法，证明估计量的一致性与渐近正态性，并用美国理解研究收入数据说明。
Summary It has long been established that if a panel dataset suffers from attrition, auxiliary (refreshment) sampling restores full identification under additional assumptions that still allow for nontrivial attrition mechanisms. Such identification results either rely on implausible assumptions about the attrition process or lead to theoretically and computationally challenging estimation procedures. We propose an alternative identifying assumption that, despite its nonparametric nature, suggests a simple estimation algorithm based on a transformation of the empirical cumulative distribution function of the data. This estimation procedure requires neither tuning parameters nor optimization in the first step, that is, it has a closed form. We prove that our estimator is consistent and asymptotically normal, demonstrate its good performance in simulations, and provide an empirical illustration with income data from the Understanding America Study.