A simple ordered data estimator for inverse density weighted expectations

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A Simple Estimator for Binary Choice Models with Endogenous Regressors (2015, methodology): “This is tail symmetry, which is described in more detail in Magnac and Maurin (2007). Even when tail symmetry does not hold exactly, the size of bias term e will equal the magnitude of the difference between these two integrals, and so the the bias resulting from applying special regressor methods w”
Optimal Data-Driven Regression Discontinuity Plots (2015, methodology): “We estimate the unknown constants using ideas related to spacings estimators (see, e.g., Ghosh and Jammalamadaka 2001; Lewbel and Schennach 2007; Baryshnikov, Penrose, and Yurich 2009, and references therein) and series estimators (see, e.g., Newey 1997; Chen 2007; Ruppert, Wand, and Carroll 2009;…”
Modeling Ordered Choices: A Primer and Recent Developments (2008, unknown): “The binary choice literature has two focal points, maximum score estimation [Manski (1975, 1985), Manski and Thompson (1985) and Horowitz (1992)] and the Klein and Spady (1993) kernel based semiparametric estimator for binary choice.”
Subsidies, financial constraints and firm innovative activities in emerging economies (2017, methodology): “Firstly, the estimator allows for two methods of estimation of the density: the standard kernel density (odd columns) and the sorted data density of Lewbel and Schennach (2007) in even columns.”