Ariell Zimran
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Using Discrepancies to Correct for False Matches in Historical Data with Yuya Sasaki We propose a method to correct estimates from historical linked data for bias arising from false matches. We estimate the rate of false matching from the extent to which information that should agree across a linked sample disagrees. We can then correct for the bias that false matches induce. Our methods enable researchers to correct estimates of both population moments and regression coefficients for false matches, where the probability of a false match may be constant or related to discrete variables of interest. We illustrate the properties of these estimators via simulation and demonstrate them using linked US census data.