IDEAS home Printed from https://ideas.repec.org/a/ecm/emetrp/v71y2003i3p905-932.html
   My bibliography  Save this article

Inference in Censored Models with Endogenous Regressors

Author

Listed:
  • Han Hong

    ()

  • Elie Tamer

Abstract

This paper analyzes the linear regression model y = x&bgr;+ε with a conditional median assumption med (ε| z) = 0, where z is a vector of exogenous instrument random variables. We study inference on the parameter &bgr; when y is censored and x is endogenous. We treat the censored model as a model with interval observation on an outcome, thus obtaining an incomplete model with inequality restrictions on conditional median regressions. We analyze the identified features of the model and provide sufficient conditions for point identification of the parameter &bgr;. We use a minimum distance estimator to consistently estimate the identified features of the model. We show that under point identification conditions and additional regularity conditions, the estimator based on inequality restrictions is normal and we derive its asymptotic variance. One can use our setup to treat the identification and estimation of endogenous linear median regression models with no censoring. A Monte Carlo analysis illustrates our estimator in the censored and the uncensored case. Copyright Econometric Society, 2002.

Suggested Citation

  • Han Hong & Elie Tamer, 2003. "Inference in Censored Models with Endogenous Regressors," Econometrica, Econometric Society, vol. 71(3), pages 905-932, May.
  • Handle: RePEc:ecm:emetrp:v:71:y:2003:i:3:p:905-932
    as

    Download full text from publisher

    File URL: http://www.blackwellpublishing.com/ecta/asp/abstract.asp?iid=3&aid=430&vid=71
    File Function: link to full text
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. Khan, Shakeeb & Ponomareva, Maria & Tamer, Elie, 2011. "Sharpness in randomly censored linear models," Economics Letters, Elsevier, vol. 113(1), pages 23-25, October.
    5. 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.
    6. Liangjun Su & Zhenlin Yang, 2007. "Instrumental Variable Quantile Estimation of Spatial Autoregressive Models," Development Economics Working Papers 22476, East Asian Bureau of Economic Research.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Christophe Muller, 2017. "Heterogeneity and Non-Constant Effect in Two-Stage Quantile Regression," Working Papers halshs-01157552, HAL.
    12. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    13. Andrew Chesher & Adam M. Rosen, 2017. "Generalized Instrumental Variable Models," Econometrica, Econometric Society, vol. 85, pages 959-989, May.
    14. 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.
    15. Giulia Bettin & Riccardo Lucchetti, 2012. "Interval regression models with endogenous explanatory variables," Empirical Economics, Springer, vol. 43(2), pages 475-498, October.
    16. Tae-Hwan Kim & Christophe Muller, 2013. "A Test for Endogeneity in Conditional Quantiles," Working Papers halshs-00854527, HAL.
    17. Andrew Chesher, 2005. "Identification with excess heterogeneity," CeMMAP working papers CWP19/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    18. 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.
    19. Blundell, Richard & Powell, James L., 2007. "Censored regression quantiles with endogenous regressors," Journal of Econometrics, Elsevier, vol. 141(1), pages 65-83, November.
    20. 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.
    21. Tae-Hwan Kim & Christophe Muller, 2012. "A test for endogeneity in conditional quantile models," Working papers 2012rwp-49, Yonsei University, Yonsei Economics Research Institute.
    22. 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.
    23. Chesher, Andrew, 2009. "Excess heterogeneity, endogeneity and index restrictions," Journal of Econometrics, Elsevier, vol. 152(1), pages 37-45, September.
    24. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ecm:emetrp:v:71:y:2003:i:3:p:905-932. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum). General contact details of provider: http://edirc.repec.org/data/essssea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.