JSM 2004 - Toronto

Abstract #300087

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Activity Number: 137
Type: Invited
Date/Time: Monday, August 9, 2004 : 2:00 PM to 3:50 PM
Sponsor: Business and Economics Statistics Section
Abstract - #300087
Title: Quantile Regression and Identification of Structural Features
Author(s): Andrew Chesher*+
Companies: University College London
Address: Dept. of Economics, London, International, WC1E 6BT, United Kingdom
Keywords: identification ; structural modeling ; quantile regression
Abstract:

This paper studies the identifying power of models with nonparametrically specified structural equations from which latent variables may not be additively separable. An example is a model for the joint determination of wages and schooling in which the focus is on the identification of the value of the returns to schooling at quantiles of the distribution of "ability." Adding local conditional quantile independence conditions, together with some monotonicity and local exclusion restrictions, produces models that locally identify partial differences of a structural function, and partial derivatives when there is sufficient smoothness and continuous variation. Application of the analog principle points to estimators of these structural features, which are functionals of estimated quantile regression functions. When arguments of structural functions have discrete variation there may be interval, but not point, identification and the analog principle points to quantile-regression-based interval estimators.

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