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Endogeneity bias modeling using observables

Author

Listed:
  • Galvao, Antonio F.
  • Montes-Rojas, Gabriel
  • Song, Suyong

Abstract

This paper proposes an alternative solution to the endogeneity problem by explicitly modeling the joint interaction of the endogenous variables and the unobserved causes of the dependent variable as a function of additional observables. We derive identification of the parameters, develop an estimator, and establish its consistency and asymptotic normality.

Suggested Citation

  • Galvao, Antonio F. & Montes-Rojas, Gabriel & Song, Suyong, 2017. "Endogeneity bias modeling using observables," Economics Letters, Elsevier, vol. 152(C), pages 41-45.
  • Handle: RePEc:eee:ecolet:v:152:y:2017:i:c:p:41-45
    DOI: 10.1016/j.econlet.2016.12.021
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    References listed on IDEAS

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    1. Willis, Robert J & Rosen, Sherwin, 1979. "Education and Self-Selection," Journal of Political Economy, University of Chicago Press, vol. 87(5), pages 7-36, October.
    2. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    3. Karim Chalak, 2012. "Identification of Average Random Coefficients under Magnitude and Sign Restrictions on Confounding," Boston College Working Papers in Economics 816, Boston College Department of Economics.
    4. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    5. Montes-Rojas, Gabriel & Galvao, Antonio F., 2014. "Bayesian endogeneity bias modeling," Economics Letters, Elsevier, vol. 122(1), pages 36-39.
    6. Erik Biørn, 2000. "Panel Data With Measurement Errors: Instrumental Variables And Gmm Procedures Combining Levels And Differences," Econometric Reviews, Taylor & Francis Journals, vol. 19(4), pages 391-424.
    7. Aviv Nevo & Adam M. Rosen, 2012. "Identification With Imperfect Instruments," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 659-671, August.
    8. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 40(4), pages 791-821.
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    12. Hausman, Jerry A., 1983. "Specification and estimation of simultaneous equation models," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 7, pages 391-448 Elsevier.
    13. Jinyong Hahn & Jerry Hausman, 2002. "A New Specification Test for the Validity of Instrumental Variables," Econometrica, Econometric Society, vol. 70(1), pages 163-189, January.
    14. Joshua Angrist & Alan Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Working Papers 834, Princeton University, Department of Economics, Industrial Relations Section..
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    1. repec:bla:jorssa:v:181:y:2018:i:3:p:689-716 is not listed on IDEAS

    More about this item

    Keywords

    Endogeneity; Instrumental variables; Proxy variables;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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