A semiparametric technique that has been gaining considerable popularity in economics, the quantile regression model has a number of attractive features. For example, it can be used to characterize the entire conditional distribution of a dependent variable given a set of regressors; it has a linear programming representation which makes estimation easy; and it gives a robust measure of location. Concentrating on cross-section applications, this article presents the basic structure of the quantile regression model, highlights the most important features, and provides the elementary tools for using quantile regressions in empirical applications.
Asymptotic covariance matrix Boostrap Censored quantile regression model Design matrix bootstrapping Equivariance Generalized method of moments Heteroskedasticity Kernel estimators in econometrics Least absolute deviation Linear programming Optimal minimum distance estimator Quantile regression Semiparametric estimation Tobit model
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