Closed-form estimation and inference for panels with attrition and refreshment samples

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.