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Functionals of order statistics and their multivariate concomitants with application to semiparametric estimation by nearest neighbours

Ba Chu, Kim Huynh () and David Jacho-Chávez ()

MPRA Paper from University Library of Munich, Germany

Abstract: This paper studies the limiting behavior of general functionals of order statistics and their multivariate concomitants for weakly dependent data. The asymptotic analysis is performed under a conditional moment-based notion of dependence for vector-valued time series. It is argued, through analysis of various examples, that the dependence conditions of this type can be effectively implied by other dependence formations recently proposed in time-series analysis, thus it may cover many existing linear and nonlinear processes. The utility of this result is then illustrated in deriving the asymptotic properties of a semiparametric estimator that uses the k-Nearest Neighbour estimator of the inverse of a multivariate unknown density. This estimator is then used to calculate consumer surpluses for electricity demand in Ontario for the period 1971 to 1994. A Monte Carlo experiment also assesses the effi- cacy of the derived limiting behavior in finite samples for both these general functionals and the proposed estimator.

Keywords: Order statistics; multivariate concomitant; k-nearest neighbour; semiparametric estimation; consumer surplus. (search for similar items in EconPapers)
JEL-codes: C14 C4 (search for similar items in EconPapers)
Date: 2013, Revised 2012
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Published in Sankhya B 2.75(2013): pp. 238-292

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