Superconsistent estimation and inference in structural econometric models using extreme order statistics

By Stephen G. Donald, Harry J. Paarsch. 2002

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Extremal quantile regression: An overview (2016, background): “Donald and Paarsch (2002) modelled the winning bid, Y , as Y = c ( Z ) β ( N ) + ε , where c ( Z ) is the eﬃcient cost function that depends on the bid characteristics, Z , β ( N ) ≥ 1 is a mark-up that approaches 1 as the number of bidders N approaches inﬁnity, and the disturbance ε captures small…”
Inference for Extremal Conditional Quantile Models, with an Application to Market and Birthweight Risks (2009, unknown): “We refer the reader to Donald and Paarsch (2002) for a detailed example of this approach in the context of using k-sample extreme quantiles.3 1Caballero and Engel (1999) study approximate adjustment barriers using distribution models; obviously quantile models can also be used.”