Binary models with misclassification in the variable of interest
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.
|Date of creation:||2004|
|Date of revision:|
|Contact details of provider:|| Postal: Largo dos Colegiais 2, 7000 - 803ÉVORA|
Phone: + 351 266 74 08 94
Fax: + 351 266 74 24 94
Web page: http://www.decon.uevora.pt
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:evo:wpecon:3_2004. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Maria Aurora Murcho Galego)
If references are entirely missing, you can add them using this form.