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Estimation of Nonlinear Models with Mismeasured Regressors Using Marginal Information

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  • Yingyao Hu
  • Geert Ridder

Abstract

We consider the estimation of nonlinear models with mismeasured explanatory variables, when information on the marginal distribution of the true values of these variables is available. We derive a semi-parametric MLE that is shown to be $\sqrt{n}$ consistent and asymptotically normally distributed. In a simulation experiment we find that the finite sample distribution of the estimator is close to the asymptotic approximation. The semi-parametric MLE is applied to a duration model for AFDC welfare spells with misreported welfare benefits. The marginal distribution of the correctly measured welfare benefits is obtained from an administrative source.

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Paper provided by The Johns Hopkins University,Department of Economics in its series Economics Working Paper Archive with number 554.

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Date of creation: Jun 2009
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Handle: RePEc:jhu:papers:554

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Cited by:
  1. Susanne Schennach, 2013. "Convolution without independence," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP46/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Nikolas Mittag, 2013. "A Method Of Correcting For Misreporting Applied To The Food Stamp Program," Working Papers 13-28, Center for Economic Studies, U.S. Census Bureau.

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