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Inference in Censored Models with Endogenous Regressors

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  • Elie Tamer

    (Princeton University)

Abstract

This paper analyzes the linear regression model y = xb+e with a conditional median assumption Med( e | z)=0 where z is a vector of instruments. Added complication arises due to the censoring of the outcome y. We treat the censored model as a model with interval-observed outcome thus obtaining interval restrictions on conditional median regressions. This allows us to use the framework introduced by Manski and Tamer (2000) to analyze the information contained in these inequality restrictions. We first show identification of the parameter b in the absence of censoring and introduce a consistent estimator based on the minimum distance method. We then give conditions for global identification of b in the model above with censored y and endogenous x. We provide a consistent estimator that is based on a modified minimum distance method.

Suggested Citation

  • Elie Tamer, 2000. "Inference in Censored Models with Endogenous Regressors," Econometric Society World Congress 2000 Contributed Papers 1815, Econometric Society.
  • Handle: RePEc:ecm:wc2000:1815
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    References listed on IDEAS

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    1. Powell, James L, 1986. "Symmetrically Trimmed Least Squares Estimation for Tobit Models," Econometrica, Econometric Society, vol. 54(6), pages 1435-1460, November.
    2. Charles F. Manski & Elie Tamer, 2002. "Inference on Regressions with Interval Data on a Regressor or Outcome," Econometrica, Econometric Society, vol. 70(2), pages 519-546, March.
    3. Arabmazar, Abbas & Schmidt, Peter, 1982. "An Investigation of the Robustness of the Tobit Estimator to Non-Normality," Econometrica, Econometric Society, vol. 50(4), pages 1055-1063, July.
    4. Powell, James L & Stock, James H & Stoker, Thomas M, 1989. "Semiparametric Estimation of Index Coefficients," Econometrica, Econometric Society, vol. 57(6), pages 1403-1430, November.
    5. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    6. Honore, Bo E, 1992. "Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects," Econometrica, Econometric Society, vol. 60(3), pages 533-565, May.
    7. Vella, Francis & Verbeek, Marno, 1999. "Two-step estimation of panel data models with censored endogenous variables and selection bias," Journal of Econometrics, Elsevier, vol. 90(2), pages 239-263, June.
    8. Richard W. Blundell & Richard J. Smith, 1989. "Estimation in a Class of Simultaneous Equation Limited Dependent Variable Models," Review of Economic Studies, Oxford University Press, vol. 56(1), pages 37-57.
    9. James Tobin, 1956. "Estimation of Relationships for Limited Dependent Variables," Cowles Foundation Discussion Papers 3R, Cowles Foundation for Research in Economics, Yale University.
    10. 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. Chernozhukov, Victor & Fernández-Val, Iván & Kowalski, Amanda E., 2015. "Quantile regression with censoring and endogeneity," Journal of Econometrics, Elsevier, vol. 186(1), pages 201-221.
    2. Blanco-Fernández, Angela & Corral, Norberto & González-Rodríguez, Gil, 2011. "Estimation of a flexible simple linear model for interval data based on set arithmetic," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2568-2578, September.
    3. Galvao, Antonio F. & Montes-Rojas, Gabriel, 2015. "On the equivalence of instrumental variables estimators for linear models," Economics Letters, Elsevier, vol. 134(C), pages 13-15.
    4. Blundell, Richard & Powell, James L., 2007. "Censored regression quantiles with endogenous regressors," Journal of Econometrics, Elsevier, vol. 141(1), pages 65-83, November.
    5. Chen, Songnian, 2010. "An integrated maximum score estimator for a generalized censored quantile regression model," Journal of Econometrics, Elsevier, vol. 155(1), pages 90-98, March.
    6. Tae-Hwan Kim & Christophe Muller, 2012. "A test for endogeneity in conditional quantile models," Working papers 2012rwp-49, Yonsei University, Yonsei Economics Research Institute.
    7. Honore, Bo E. & Hu, Luojia, 2004. "Estimation of cross sectional and panel data censored regression models with endogeneity," Journal of Econometrics, Elsevier, vol. 122(2), pages 293-316, October.
    8. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Sharpness in randomly censored linear models," Economics Letters, Elsevier, vol. 113(1), pages 23-25, October.
    9. Arnab Bhattacharjee & Sean Holly, 2013. "Understanding Interactions in Social Networks and Committees," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(1), pages 23-53, March.
    10. Liangjun Su & Zhenlin Yang, 2007. "Instrumental Variable Quantile Estimation of Spatial Autoregressive Models," Development Economics Working Papers 22476, East Asian Bureau of Economic Research.
    11. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    12. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
    13. Chesher, Andrew, 2009. "Excess heterogeneity, endogeneity and index restrictions," Journal of Econometrics, Elsevier, vol. 152(1), pages 37-45, September.
    14. Andrew Chesher & Adam M. Rosen, 2017. "Generalized Instrumental Variable Models," Econometrica, Econometric Society, vol. 85, pages 959-989, May.
    15. Tae-Hwan Kim & Christophe Muller, 2017. "A Robust Test of Exogeneity Based on Quantile Regressions," AMSE Working Papers 1716, Aix-Marseille School of Economics, Marseille, France.
    16. 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.
    17. Patrick Bajari & Han Hong & Ahmed Khwaja, 2006. "Moral Hazard, Adverse Selection and Health Expenditures: A Semiparametric Analysis," NBER Working Papers 12445, National Bureau of Economic Research, Inc.
    18. 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.
    19. Tae-Hwan Kim & Christophe Muller, 2013. "A Test for Endogeneity in Conditional Quantiles," AMSE Working Papers 1342, Aix-Marseille School of Economics, Marseille, France, revised Aug 2013.
    20. Christophe Muller, 2017. "Heterogeneity and Non-Constant Effect in Two-Stage Quantile Regression," Working Papers halshs-01157552, HAL.
    21. Atella, Vincenzo & Pace, Noemi & Vuri, Daniela, 2008. "Are employers discriminating with respect to weight?: European Evidence using Quantile Regression," Economics & Human Biology, Elsevier, vol. 6(3), pages 305-329, December.
    22. Tae-Hwan Kim & Christophe Muller, 2015. "A Particular Form of Non-Constant Effect in Two-Stage Quantile Regression," Working papers 2015rwp-82, Yonsei University, Yonsei Economics Research Institute.
    23. Giulia Bettin & Riccardo Lucchetti, 2012. "Interval regression models with endogenous explanatory variables," Empirical Economics, Springer, vol. 43(2), pages 475-498, October.
    24. Andrew Chesher, 2005. "Identification with excess heterogeneity," CeMMAP working papers CWP19/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

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