Two-stage rank estimation of quantile index models

By Shakeeb Khan. 2001

71 citations recorded by the source corpus.

Abstract 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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/73f2f48a100cf6276edbd6ee8c752fdf6068b2ae

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Citations in the corpus

Two-step estimation of semiparametric censored regression models (2001, background): “Khan, J.L. Powell / Journal of Econometrics 103 (2001) 73–110”
Characterization of the asymptotic distribution of semiparametric M-estimators (2006, background): “variables) and than the estimator of Khan (2001) in the sense that monotonicity of f0 is”
Nonparametric Estimation of an Additive Quantile Regression Model (2004, methodology): “…1993; Powell, Stock, and Stoker 1989; Hristache, Juditsky, and Spokoiny 2001 for mean regression models; Chaudhuri, Doksum, and Samarov 1997; Khan 2001 for quantile regression models) and partially linear models (e.g., Robinson 1988 for mean regression models; He and Shi 1996; Lee 2003 for…”
SEMIPARAMETRIC ESTIMATION OF A HETEROSKEDASTIC SAMPLE SELECTION MODEL (2003, methodology): “For example, h2n may be selected by adopting data driven approaches that accommodate prespecified rate conditions, such as proposed in Chen and Khan (2000) and Khan (2001).”
Nonparametric Identification and Estimation of a Censored Location-Scale Regression Model (2005, methodology): “Gordon B. Dahl is Associate Professor of Economics (E-mail: dahl@troi.cc.rochester.edu) and Shakeeb Khan is Associate Professor of Economics (E-mail: skhan@troi.cc.rochester.edu), Department of Economics, University of Rochester, Rochester, NY 14627.”