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CQIV: Stata module to perform censored quantile instrumental variables regression

Author

Listed:
  • Victor Chernozhukov

    (MIT)

  • Ivan Fernandez-Val

    (Boston University)

  • Sukjin Han

    (Yale University)

  • Amanda Kowalski

    (Yale University)

Programming Language

Stata

Abstract

cqiv conducts censored quantile instrumental variable (CQIV) estimation. This command can implement both censored and uncensored quantile IV estimation either under exogeneity or endogeneity. The estimator proposed by Chernozhukov, Fernandez-Val and Kowalski (2010) is used if CQIV estimation is implemented. A parametric version of the estimator proposed by Lee (2007) is used if quantile IV estimation without censoring is implemented. The estimator proposed by Chernozhukov and Hong (2002) is used if censored quantile regression (CQR) is estimated without endogeneity. Note that all the variables in the parentheses of the syntax are those involved in the first stage estimation of CQIV and QIV.

Suggested Citation

  • Victor Chernozhukov & Ivan Fernandez-Val & Sukjin Han & Amanda Kowalski, 2012. "CQIV: Stata module to perform censored quantile instrumental variables regression," Statistical Software Components S457478, Boston College Department of Economics, revised 25 Sep 2019.
  • Handle: RePEc:boc:bocode:s457478
    Note: This module should be installed from within Stata by typing "ssc install cqiv". The module is made available under terms of the GPL v3 (https://www.gnu.org/licenses/gpl-3.0.txt). Windows users should not attempt to download these files with a web browser.
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    File URL: http://fmwww.bc.edu/repec/bocode/c/cqiv.ado
    File Function: program code
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    File URL: http://fmwww.bc.edu/repec/bocode/c/cqiv.sthlp
    File Function: help file
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    File URL: http://fmwww.bc.edu/repec/bocode/a/alcoholengel.dta
    File Function: sample data file
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    File URL: http://fmwww.bc.edu/repec/bocode/c/cqiv_article.pdf
    File Function: documentation
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    Citations

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    Cited by:

    1. Richard T. Melstrom & Deshamithra H. W. Jayasekera, 2017. "Two-Stage Estimation to Control for Unobservables in a Recreation Demand Model with Unvisited Sites," Land Economics, University of Wisconsin Press, vol. 93(2), pages 328-341.
    2. Silvia Mendolia & Alfredo R Paloyo & Ian Walker, 2018. "Heterogeneous effects of high school peers on educational outcomes," Oxford Economic Papers, Oxford University Press, vol. 70(3), pages 613-634.
    3. Sven Neelsen & Owen O'Donnell, 2017. "Progressive universalism? The impact of targeted coverage on health care access and expenditures in Peru," Health Economics, John Wiley & Sons, Ltd., vol. 26(12), pages 179-203, December.
    4. Kiran Krishnamurthy, Chandra & Kriström, Bengt, 2013. "Determinants of the price-premium for Green Energy: Evidence from an OECD cross-section," CERE Working Papers 2013:7, CERE - the Center for Environmental and Resource Economics, revised 30 Jun 2014.
    5. Michael J. Peel, 2014. "Addressing unobserved endogeneity bias in accounting studies: control and sensitivity methods by variable type," Accounting and Business Research, Taylor & Francis Journals, vol. 44(5), pages 545-571, October.
    6. Martin Falk & Eva Hagsten, 2014. "Output growth and prices of establishments in the Swedish lodging industry," ERSA conference papers ersa14p360, European Regional Science Association.
    7. Fack, Gabrielle & Landais, Camille, 2016. "The effect of tax enforcement on tax elasticities: Evidence from charitable contributions in France," Journal of Public Economics, Elsevier, vol. 133(C), pages 23-40.
    8. Broberg, Thomas & Kazukauskas, Andrius, 2014. "Inefficiencies in residential use of energy - A critical overview of literature and energy efficiency policies in EU and Sweden," CERE Working Papers 2014:7, CERE - the Center for Environmental and Resource Economics.

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