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What You Always Wanted to Know About Censoring But Never Dared to Ask – Parameter Estimation for Censored Random Vectors

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  • Schnedler, Wendelin

    (University of Paderborn)

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 efficient and normally distributed.

Suggested Citation

  • Schnedler, Wendelin, 2003. "What You Always Wanted to Know About Censoring But Never Dared to Ask – Parameter Estimation for Censored Random Vectors," IZA Discussion Papers 837, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp837
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    References listed on IDEAS

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    1. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
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    3. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    4. Nelson, Forrest D., 1977. "Censored regression models with unobserved, stochastic censoring thresholds," Journal of Econometrics, Elsevier, vol. 6(3), pages 309-327, November.
    5. 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.
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    Keywords

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    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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