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Grigory Franguridi from USC will present "Estimation of panels with attrition and refreshment samples” joint work with Jinyong Hahn, Pierre Hoonhout, Arie Kapteyn, and Geert
Abstract
It has long been established that, if a panel dataset suffers from attrition, auxiliary (refreshment) sampling may restore full identification under weak nonparametric assumptions on attrition. Despite their generality, these identification strategies have not been employed in empirical research due to the intractability of induced estimation procedures.
We show that practical estimation is nevertheless possible without parametric approximations. Our two-step estimation algorithm utilizes the well-known iterative proportional fitting procedure, which does not require optimization and exhibits fast convergence even with continuous data. We show that our estimator is consistent and asymptotically normal under smoothness assumptions and appropriate choice of kernel and bandwidth. We also demonstrate its excellent performance in simulations and provide an empirical illustration using the Understanding of America survey panel.