Semiparametric robust estimation of truncated and censored regression models

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)


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 semiparametric general trimmed estimator (GTE) of truncated and censored regression, which is highly robust but 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 finite-sample properties of existing and proposed estimators are studied by means of Monte Carlo simulations.
Original languageEnglish
Pages (from-to)347-366
JournalJournal of Econometrics
Issue number2
Publication statusPublished - 2012


Dive into the research topics of 'Semiparametric robust estimation of truncated and censored regression models'. Together they form a unique fingerprint.

Cite this