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Sharpness in randomly censored linear models

Author

Listed:
  • Khan, Shakeeb
  • Ponomareva, Maria
  • Tamer, Elie

Abstract

This work proves that inferences on parameter vectors based on moment inequalities typically used in linear models with outcome censoring are sharp, i.e., they exhaust all the information in the data and the model. This holds for fixed and randomly censored linear models under median independence where the censoring can be endogenous.

Suggested Citation

  • Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Sharpness in randomly censored linear models," Economics Letters, Elsevier, vol. 113(1), pages 23-25, October.
  • Handle: RePEc:eee:ecolet:v:113:y:2011:i:1:p:23-25
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    References listed on IDEAS

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    1. Han Hong & Elie Tamer, 2003. "Inference in Censored Models with Endogenous Regressors," Econometrica, Econometric Society, vol. 71(3), pages 905-932, May.
    2. Khan, Shakeeb & Tamer, Elie, 2009. "Inference on endogenously censored regression models using conditional moment inequalities," Journal of Econometrics, Elsevier, vol. 152(2), pages 104-119, October.
    3. Honore, Bo & Khan, Shakeeb & Powell, James L., 2002. "Quantile regression under random censoring," Journal of Econometrics, Elsevier, vol. 109(1), pages 67-105, July.
    4. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
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    Cited by:

    1. Andrew Chesher & Adam Rosen, 2015. "Characterizations of identified sets delivered by structural econometric models," CeMMAP working papers CWP63/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Arkadiusz Szyd?owski, 2017. "Stochastic processes of limited frequency and the effects of oversampling," Discussion Papers in Economics 17/04, Department of Economics, University of Leicester.
    3. repec:eee:ecolet:v:159:y:2017:i:c:p:42-45 is not listed on IDEAS

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