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What you always wanted to know about censoring but never dared to ask

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  • Schnedler, Wendelin

Abstract

This article considers a wide class of censoring problems and presents a construction rule for an objective function. This objective function generalises the ordinary likelihood as well as particular "likelihoods" used for estimation in several censoring models. Under regularity conditions the maximiser of this generalised likelihood has all the properties of a maximum likelihood estimator: it is consistent and the respective root-n estimator is asymptotically e±cient and normally distributed.

Suggested Citation

  • Schnedler, Wendelin, 2003. "What you always wanted to know about censoring but never dared to ask," Bonn Econ Discussion Papers 16/2003, University of Bonn, Bonn Graduate School of Economics (BGSE).
    • Handle: RePEc:zbw:bonedp:162003
    as

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    References listed on IDEAS

    as
    1. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    2. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119, Decembrie.
    3. Orazio P. Attanasio, 2000. "Consumer Durables and Inertial Behaviour: Estimation and Aggregation of (S, s) Rules for Automobile Purchases," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(4), pages 667-696.
    4. Nelson, Forrest D., 1977. "Censored regression models with unobserved, stochastic censoring thresholds," Journal of Econometrics, Elsevier, vol. 6(3), pages 309-327, November.
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    More about this item

    Keywords

    Censored variables; M-estimation; Multivariate methods; Random censoring; generalised likelihood;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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