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misclassification in binary variables

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  • Christopher R. Bollinger
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    Abstract

    Misclassification of binary variables is the first case of non-classical measurement error considered. Similar to the classical errors-in-variables result, misclassification of a binary regressor leads to attenuation of slope coefficient estimates in linear regression. Classical instrumental variables will not address the problem. Bounds results under a number of different sets of assumptions can be derived. When the dependent variable is binary, misclassification also leads to slope attenuation. Some identification results are available in this case.

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    This chapter was published in: Steven N. Durlauf & Lawrence E. Blume (ed.) , , pages , 2010, 3rd quarter update.

    This item is provided by Palgrave Macmillan in its series The New Palgrave Dictionary of Economics with number v:4:year:2010:doi:3832.

    Handle: RePEc:pal:dofeco:v:4:year:2010:doi:3832

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    Keywords: binary variables; dependent variable; measurement error; misclassification; regression;

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