A note on the closed-form identification of regression models with a mismeasured binary regressor
This note considers the identification of a nonparametric regression model with an unobserved 0-1 dichotomous regressor. The sample consists of a dependent variable and a 0-1 dichotomous proxy of the unobserved regressor. We obtain nonparametric identification of every element in the model as a closed-form function of the observed moments or densities. Our identification strategy does not require any additional sample information, such as instrumental variables or a secondary sample. The closed-form solution may be used to construct estimators of the unknowns.
Volume (Year): 78 (2008)
Issue (Month): 12 (September)
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- Xiaohong Chen & Yingyao Hu & Arthur Lewbel, 2007.
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- Bollinger, Christopher R., 1996. "Bounding mean regressions when a binary regressor is mismeasured," Journal of Econometrics, Elsevier, vol. 73(2), pages 387-399, August.
- Huwang, Longcheen & Gene Hwang, J. T., 2002. "Prediction and confidence intervals for nonlinear measurement error models without identifiability information," Statistics & Probability Letters, Elsevier, vol. 58(4), pages 355-362, July.
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- Klepper, Steven, 1988. "Bounding the effects of measurement error in regressions involving dichotomous variables," Journal of Econometrics, Elsevier, vol. 37(3), pages 343-359, March. Full references (including those not matched with items on IDEAS)
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