A Nonparametric Maximum Rank Correlation Estimator

Rosa L. Matzkin
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Rosa L. Matzkin: Cowles Foundation, Yale University, http://cowles.econ.yale.edu/

No 918, Cowles Foundation Discussion Papers from Cowles Foundation, Yale University

Abstract: This paper presents a nonparametric and distribution-free estimator for the function h*, of observable exogenous variables, x, in the generalized regression model, y-G(h*(x), mu). The method does not require a parametric specification for either the function h* or for the distribution of the random term mu. The estimation proceeds by maximizing a rank correlation criterion (Han (1987) over a set of functions that are monotone increasing, concave, and homogeneous degree one; the function h* is assumed to belong to this set of functions. The estimator is shown to be strongly consistent.

Keywords: Nonparametric; rank correlation; estimators; consistency; regression model (search for similar items in EconPapers)
Date: 1989-07
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