Two-stage rank estimation of quantile index models
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This paper estimates a class of models which satisfy a monotonicity condition on the conditional quantile function of the response variable. This class includes as a special case the monotonic transformation model with the error term satisfying a conditional quantile restriction, thus allowing for very general forms of conditional heteroscedasticity. A two-stage approach is adopted to estimate the relevant parameters. In the first stage the conditional quantile function is estimated nonparametrically by the local polynomial estimator discussed in Chaudhuri (Journal of Multivariate Analysis 39 (1991a) 246-269; Annals of Statistics 19 (1991b) 760-777) and Cavanagh (1996, Preprint). In the second stage, the monotonicity of the quantile function is exploited to estimate the parameters of interest by maximizing a rank-based objective function. The proposed estimator is shown to have desirable asymptotic properties and can then also be used for dimensionality reduction or to estimate the unknown structural function in the context of a transformation model. © 2001 Elsevier Science S.A. All rights reserved.
Published Version (Please cite this version)10.1016/S0304-4076(00)00040-3
Publication InfoKhan, S (2001). Two-stage rank estimation of quantile index models. Journal of Econometrics, 100(2). pp. 319-355. 10.1016/S0304-4076(00)00040-3. Retrieved from https://hdl.handle.net/10161/1917.
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Professor of Economics
Professor Khan is on leave at Boston College for the 2016-17 academic year.Professor Khan specializes in the fields of mathematical economics, statistics, and applied econometrics. His studies have explored a variety of subjects from covariate dependent censoring and non-stationary panel data, to causal effects of education on wage inequality and the variables affecting infant mortality rates in Brazil. He was awarded funding by National Science Foundation grants for his projects ent
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