Semiparametric robust estimation of truncated and censored regression models

Research output: Contribution to journalArticleScientificpeer-review

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 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
Volume168
Issue number2
Publication statusPublished - 2012

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Truncated regression
Estimator
Robust estimation
Censored regression model
Least squares
Censored regression
Outliers
Maximum likelihood
Asymptotic properties
Monte Carlo simulation
Finite sample properties
Contamination

Cite this

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title = "Semiparametric robust estimation of truncated and censored regression models",
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 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.",
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journal = "Journal of Econometrics",
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Semiparametric robust estimation of truncated and censored regression models. / Cizek, P.

In: Journal of Econometrics, Vol. 168, No. 2, 2012, p. 347-366.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Semiparametric robust estimation of truncated and censored regression models

AU - Cizek, P.

N1 - Appeared earlier as CentER Discussion Paper 2008-034

PY - 2012

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AB - 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.

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VL - 168

SP - 347

EP - 366

JO - Journal of Econometrics

JF - Journal of Econometrics

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