On multivariate quantile regression analysis
Jean-Paul Chavas ()
Statistical Methods & Applications, 2018, vol. 27, issue 3, 365-384
Abstract This paper investigates the estimation of parameters in a multivariate quantile regression model when the investigator wants to evaluate the associated distribution function. It proposes a new directional quantile estimator with the following properties: (1) it applies to an arbitrary number of random variables; (2) it is equivalent to estimating the distribution function allowing for non-convex distribution contours; (3) it satisfies nice equivariance properties; (4) it has desirable statistical properties (i.e., consistency and asymptotic normality); and (5) its implementation involves a modest computational burden: our proposed estimator can be obtained by solving parametric linear programming problems. As such, this paper expands the range of applications of quantile estimation for multivariate regression models.
Keywords: Multivariate; Quantile; Regression; Estimator; Nonconvex (search for similar items in EconPapers)
JEL-codes: C10 C30 C50 (search for similar items in EconPapers)
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