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 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.
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Bibliographic InfoPaper provided by University of Bonn, Germany in its series Bonn Econ Discussion Papers with number bgse16_2003.
Date of creation: Jul 2003
Date of revision:
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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," The Centre for Market and Public Organisation 03/082, Department of Economics, University of Bristol, UK.
- 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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- Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
- 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.
- 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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