A Nonparametric Maximum Rank Correlation Estimator
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.
|Date of creation:||Jul 1989|
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- Horowitz, Joel L., 1986. "A distribution-free least squares estimator for censored linear regression models," Journal of Econometrics, Elsevier, vol. 32(1), pages 59-84, June.
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- Rosa L. Matzkin, 1987. "Semiparametric Estimation of Monotonic and Concave Utility Functions: The Discrete Choice Case," Cowles Foundation Discussion Papers 830, Cowles Foundation for Research in Economics, Yale University.
- Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
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