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Semiparametric Robust Estimation of Truncated and Censored Regression Models

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Author Info
Cizek, P. (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.

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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2008-34.

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Date of creation: 2008
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Handle: RePEc:dgr:kubcen:200834

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Find related papers by JEL classification:
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models

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