Bias-corrected quantile regression estimation of censored regression models
P. Čížek () and
S. Sadikoglu ()
Additional contact information
P. Čížek: Tilburg University
S. Sadikoglu: Tilburg University
Statistical Papers, 2018, vol. 59, issue 1, 215-247
Abstract In this paper, an extension of the indirect inference methodology to semiparametric estimation is explored in the context of censored regression. Motivated by weak small-sample performance of the censored regression quantile estimator proposed by Powell (J Econom 32:143–155, 1986a), two- and three-step estimation methods were introduced for estimation of the censored regression model under conditional quantile restriction. While those stepwise estimators have been proven to be consistent and asymptotically normal, their finite sample performance greatly depends on the specification of an initial estimator that selects the subsample to be used in subsequent steps. In this paper, an alternative semiparametric estimator is introduced that does not involve a selection procedure in the first step. The proposed estimator is based on the indirect inference principle and is shown to be consistent and asymptotically normal under appropriate regularity conditions. Its performance is demonstrated and compared to existing methods by means of Monte Carlo simulations.
Keywords: Asymptotic normality; Censored regression; Indirect inference; Quantile regression; 62F10; 62F12; 62N01; 62N02 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s00362-016-0761-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.
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:spr:stpapr:v:59:y:2018:i:1:d:10.1007_s00362-016-0761-z
Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362
Access Statistics for this article
Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller
More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla ().