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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 & Ponomareva, Maria & Tamer, Elie, 2016. "Identification of panel data models with endogenous censoring," Journal of Econometrics, Elsevier, vol. 194(1), pages 57-75.
    3. 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.
    4. Honore, Bo & Khan, Shakeeb & Powell, James L., 2002. "Quantile regression under random censoring," Journal of Econometrics, Elsevier, vol. 109(1), pages 67-105, July.
    5. 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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    Citations

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    Cited by:

    1. Shosei Sakaguchi, 2020. "Partial Identification and Inference in Duration Models with Endogenous Censoring," CeMMAP working papers CWP8/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Shosei Sakaguchi, 2021. "Partial Identification and Inference in Duration Models with Endogenous Censoring," Papers 2107.00928, arXiv.org.
    3. 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.
    4. Arkadiusz Szydlowski, 2017. "Stochastic processes of limited frequency and the effects of oversampling," Discussion Papers in Economics 17/04, Division of Economics, School of Business, University of Leicester.
    5. Shosei Sakaguchi, 2024. "Partial identification and inference in duration models with endogenous censoring," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 308-326, March.
    6. Fan, Yanqin & Liu, Ruixuan, 2018. "Partial identification and inference in censored quantile regression," Journal of Econometrics, Elsevier, vol. 206(1), pages 1-38.
    7. Chesher, Andrew & Kim, Dongwoo & Rosen, Adam M., 2023. "IV methods for Tobit models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1700-1724.
    8. Andrew Chesher & Adam Rosen, 2018. "Generalized instrumental variable models, methods, and applications," CeMMAP working papers CWP43/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Khan, S. & Ponomareva, M. & Tamer, E., 2023. "Identification of dynamic binary response models," Journal of Econometrics, Elsevier, vol. 237(1).
    10. Szydłowski, Arkadiusz, 2017. "Endogenously censored median regression with an application to benefit elasticity of US unemployment duration," Economics Letters, Elsevier, vol. 159(C), pages 42-45.
    11. Irene Botosaru & Chris Muris, 2022. "Identification of time-varying counterfactual parameters in nonlinear panel models," Papers 2212.09193, arXiv.org, revised Nov 2023.

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