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Linearization of randomly weighted empiricals under long range dependence with application to nonlinear regression quantiles

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>06/2000
<mark>Journal</mark>Econometric Theory
Issue number3
Number of pages23
Pages (from-to)301-323
<mark>Original language</mark>English


This paper discusses some asymptotic uniform linearity results of randomly
weighted empirical processes based on long range dependent random variables+
These results are subsequently used to linearize nonlinear regression quantiles in
a nonlinear regression model with long range dependent errors, where the design
variables can be either random or nonrandom+ These, in turn, yield the limiting
behavior of the nonlinear regression quantiles+ As a corollary, we obtain the limiting
behavior of the least absolute deviation estimator and the trimmed mean
estimator of the parameters of the nonlinear regression model+ Some of the limiting
properties are in striking contrast with the corresponding properties of a
nonlinear regression model under independent and identically distributed error
random variables+ The paper also discusses an extension of rank score statistic in
a nonlinear regression model+

Bibliographic note

http://journals.cambridge.org/action/displayJournal?jid=ECT The final, definitive version of this article has been published in the Journal, Econometric Theory, 16 (3), pp 301-323 2000, © 2000 Cambridge University