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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Bollinger, C.R. (2018). Misclassification in Binary Variables. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2905
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DOI: https://doi.org/10.1057/978-1-349-95189-5_2905
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