Advanced Search
MyIDEAS: Login to save this article or follow this journal

Quantile Regression with Classical Additive Measurement Errors

Contents:

Author Info

  • Gabriel Montes-Rojas

    ()
    (City University London)

Abstract

This note derives the bias of the quantile regression estimator in the presence of classical additive measurement error, and show its connection to least squares models. The bias structure suggests that the instrumental variables estimator proposed for least squares can be applied to the quantile regression case.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.accessecon.com/Pubs/EB/2011/Volume31/EB-11-V31-I4-P257.pdf
Download Restriction: no

Bibliographic Info

Article provided by AccessEcon in its journal Economics Bulletin.

Volume (Year): 31 (2011)
Issue (Month): 4 ()
Pages: 2863-2868

as in new window
Handle: RePEc:ebl:ecbull:eb-11-00343

Contact details of provider:

Related research

Keywords: Quantile Regression; Measurement Errors; Instrumental Variables;

Find related papers by JEL classification:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Schennach, Susanne M., 2008. "Quantile Regression With Mismeasured Covariates," Econometric Theory, Cambridge University Press, vol. 24(04), pages 1010-1043, August.
  2. Zvi Griliches & Jerry A. Hausman, 1984. "Errors in Variables in Panel Data," NBER Technical Working Papers 0037, National Bureau of Economic Research, Inc.
  3. Joshua Angrist & Victor Chernozhukov & Ivan Fernandez-Val, 2004. "Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure," NBER Working Papers 10428, National Bureau of Economic Research, Inc.
  4. Andrew Chesher, 2001. "Parameter approximations for quantile regressions with measurement error," CeMMAP working papers CWP02/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. Susanne M. Schennach, 2004. "Estimation of Nonlinear Models with Measurement Error," Econometrica, Econometric Society, vol. 72(1), pages 33-75, 01.
  6. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
  7. 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.
  8. Wansbeek, Tom, 2001. "GMM estimation in panel data models with measurement error," Journal of Econometrics, Elsevier, vol. 104(2), pages 259-268, September.
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 in new window

Cited by:
  1. Eren, Ozkan & Ozbeklik, Serkan, 2013. "The effect of noncognitive ability on the earnings of young men: A distributional analysis with measurement error correction," Labour Economics, Elsevier, vol. 24(C), pages 293-304.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:ebl:ecbull:eb-11-00343. 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: (John P. Conley).

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.