Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/105569
Authors: 
Saloniki, Eirini-Christina
Gosling, Amanda
Year of Publication: 
2012
Series/Report no.: 
School of Economics Discussion Papers 1214
Abstract: 
This paper addresses the problem of point identification in the presence of measurement error in discrete variables; in particular, it considers the case of having two "noisy" indicators of the same latent variable and without any prior information about the true value of the variable of interest. Based on the concept of the fourfold table and creating a nonlinear system of simultaneous equations from the observed proportions and predicted wages, we examine the need for different assumptions in order to obtain unique solutions for the system. We show that by imposing a simple restriction(s) for the joint misclassification probabilities, it is possible to measure the extent of the misclassification error in that specific variable. The proposed methodology is then used to identify whether people misreport their disability status using data from the British Household Panel Survey. Our results show that the probability of underreporting is greater than the probability of overreporting disability.
Subjects: 
measurement error
discrete
misclassification probabilities
identification
disability
JEL: 
C14
C35
J14
J31
Document Type: 
Working Paper

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