Semiparametrically Efficient Inference Based on Signs and Ranks for Median Restricted Models
Marc Hallin (),
C. Vermandele and
Bas Werker ()
No 2004-11, Discussion Paper from Tilburg University, Center for Economic Research
Since the pioneering work of Koenker and Bassett (1978), econometric models involving median and quantile rather than the classical mean or conditional mean concepts have attracted much interest.Contrary to the traditional models where the noise is assumed to have mean zero, median-restricted models enjoy a rich group-invariance structure.In this paper, we exploit this invariance structure in order to obtain semiparametrically efficient inference procedures for these models.These procedures are based on residual signs and ranks, and therefore insensitive to possible misspecification of the underlying innovation density, yet semiparametrically efficient at correctly specified densities.This latter combination is a definite advantage of these procedures over classical quasi-likelihood methods.The techniques we propose can be applied, without additional technical difficulties, to both cross-sectional and time-series models.They do not require any explicit tangent space calculation nor any projections on these.
Keywords: models; regression analysis; econometrics (search for similar items in EconPapers)
JEL-codes: C14 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
Journal Article: Semiparametrically efficient inference based on signs and ranks for median‐restricted models (2008)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:tiu:tiucen:05757b2b-ad74-4583-b012-b417132f7675
Access Statistics for this paper
More papers in Discussion Paper from Tilburg University, Center for Economic Research
Bibliographic data for series maintained by Richard Broekman ().