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Identification and Estimation of Dichotomous Latent Variables Models Using Panel Data

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  • Cheng Hsiao

Abstract

Identification conditions for binary choice errors-in-variables models are explored. Conditions for the consistency and asymptotic normality of the maximum likelihood estimators of binary choice models with unbounded explanatory variables are given. Two- or three-step estimators to simplify computation are also suggested.

Suggested Citation

  • Cheng Hsiao, 1991. "Identification and Estimation of Dichotomous Latent Variables Models Using Panel Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(4), pages 717-731.
    • Handle: RePEc:oup:restud:v:58:y:1991:i:4:p:717-731.
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    3. 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.
    4. Chen, Xiaohong & Hu, Yingyao & Lewbel, Arthur, 2008. "Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments," Economics Letters, Elsevier, vol. 100(3), pages 381-384, September.
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    7. Xiaohong Chen & Yingyao Hu & Arthur Lewbel, 2007. "Nonparametric identification and estimation of nonclassical errors-in-variables models without additional information," CeMMAP working papers CWP18/07, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Wang, Liqun, 1998. "Estimation of censored linear errors-in-variables models," Journal of Econometrics, Elsevier, vol. 84(2), pages 383-400, June.

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