misclassification in binary variables
AbstractMisclassification 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.
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