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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.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 837.

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Length: 28 pages
Date of creation: Jul 2003
Date of revision:
Publication status: published in: Econometric Reviews, 2005, 24 (2), 195-217
Handle: RePEc:iza:izadps:dp837

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Keywords: generalised likelihood; censored variables; M-estimation; multivariate methods; random censoring;

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  1. Attanasio, Orazio P, 2000. "Consumer Durables and Inertial Behaviour: Estimation and Aggregation of (S, s) Rules for Automobile Purchases," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 67(4), pages 667-96, October.
  2. Newey, Whitney K. & McFadden, Daniel, 1986. "Large sample estimation and hypothesis testing," Handbook of Econometrics, Elsevier, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 36, pages 2111-2245 Elsevier.
  3. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, Econometric Society, vol. 41(6), pages 997-1016, November.
  4. Nelson, Forrest D., 1977. "Censored regression models with unobserved, stochastic censoring thresholds," Journal of Econometrics, Elsevier, Elsevier, vol. 6(3), pages 309-327, November.
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