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Binary models with misclassification in the variable of interest

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  • Esmeralda Ramalho

    (Department of Economics, University of Évora)

Abstract

In this paper we propose a general framework to deal with datasets where a binary outcome is subject to misclassification and, for some sampling units, neither the error-prone variable of interest nor the covariates are recorded. A model to describe the observed data is for-malized and eficient likelihood-based generalized method of moments (GMM) estimators are suggested. These estimators merely require the formulation of the conditional distribution of the latent outcome given the covariates. The conditional probabilities which describe the error and the nonresponse mechanisms are estimated simultaneously with the parameters of inter-est. In a small Monte Carlo simulation study our GMM estimators revealed a very promising performance.

Suggested Citation

  • Esmeralda Ramalho, 2004. "Binary models with misclassification in the variable of interest," Economics Working Papers 3_2004, University of Évora, Department of Economics (Portugal).
  • Handle: RePEc:evo:wpecon:3_2004
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    File URL: http://hdl.handle.net/10174/8412
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    References listed on IDEAS

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    1. Imbens, Guido W, 1992. "An Efficient Method of Moments Estimator for Discrete Choice Models with Choice-Based Sampling," Econometrica, Econometric Society, vol. 60(5), pages 1187-1214, September.
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    6. Ramalho, Esmeralda A., 2002. "Regression models for choice-based samples with misclassification in the response variable," Journal of Econometrics, Elsevier, vol. 106(1), pages 171-201, January.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    nonignorable nonresponse; misclassification; generalized method of moments estimation;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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