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Recentered And Rescaled Instrumental Variable Estimation Of Tobit And Probit Models With Errors In Variables

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  • Shigeru Iwata

Abstract

Since Durbin (1954) and Sargan (1958), instrumental variable (IV) method has long been one of the most popular procedures among economists and other social scientists to handle linear models with errors-in-variables. A direct application of this method to nonlinear errors-in-variables models, however, fails to yield consistent estimators. This article restricts attention to Tobit and Probit models and shows that simple recentering and rescaling of the observed dependent variable may restore consistency of the standard IV estimator if the true dependent variable and the IV's are jointly normally distributed. Although the required condition seems rarely to be satisfied by real data, our Monte Carlo experiment suggests that the proposed estimator may be quite robust to the possible deviation from normality.

Suggested Citation

  • Shigeru Iwata, 2001. "Recentered And Rescaled Instrumental Variable Estimation Of Tobit And Probit Models With Errors In Variables," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 319-335.
  • Handle: RePEc:taf:emetrv:v:20:y:2001:i:3:p:319-335 DOI: 10.1081/ETC-100104937
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    References listed on IDEAS

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    Cited by:

    1. Joop Hartog & Luis Díaz-Serrano, 2007. "Earnings risk and demand for higher education: A cross-section test for Spain," Journal of Applied Economics, Universidad del CEMA, vol. 10, pages 1-28, May.
    2. Lee C. Adkins, 2008. "Small Sample Performance of Instrumental Variables Probit Estimators: A Monte Carlo Investigation," Economics Working Paper Series 0807, Oklahoma State University, Department of Economics and Legal Studies in Business.

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