Semiparametric Robust Estimation of Truncated and Censored Regression Models

Pavel Cizek

No 2008-34, Discussion Paper from Tilburg University, Center for Economic Research

Abstract: Many estimation methods of truncated and censored regression models such as the maximum likelihood and symmetrically censored least squares (SCLS) are sensitive to outliers and data contamination as we document. Therefore, we propose a semipara- metric general trimmed estimator (GTE) of truncated and censored regression, which is highly robust and relatively imprecise. To improve its performance, we also propose data-adaptive and one-step trimmed estimators. We derive the robust and asymptotic properties of all proposed estimators and show that the one-step estimators (e.g., one-step SCLS) are as robust as GTE and are asymptotically equivalent to the original estimator (e.g., SCLS). The infinite-sample properties of existing and proposed estimators are studied by means of Monte Carlo simulations.

JEL-codes: C13 C14 C21 C24 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: Written 2008
View list of references

Downloads: (external link)
http://arno.uvt.nl/show.cgi?fid=74840 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Access Statistics for this paper

More papers in Discussion Paper from Tilburg University, Center for Economic Research
Series data maintained by Corry Stuyts ().