What you always wanted to know about censoring but never dared to ask
AbstractThis article considers a wide class of censoring problems and presents a construction rule for an objective function. This objective function generalises the orginary 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 InfoPaper provided by Department of Economics, University of Bristol, UK in its series The Centre for Market and Public Organisation with number 03/082.
Length: 25 pages
Date of creation: Jul 2003
Date of revision:
censored variables; m-estimation; multivariate methods; random censoring; generalised likelihood;
Other versions of this item:
- Wendelin Schnedler, 2003. "What you always wanted to know about censoring but never dared to ask," Bonn Econ Discussion Papers bgse16_2003, University of Bonn, Germany.
- 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
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- 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, vol. 67(4), pages 667-96, October.
- Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
- 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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