File Matching with Faulty Continuous Matching Variables

Nicole M. Dalzell, Jerome P. Reiter and Gale Boyd

Working Papers from U.S. Census Bureau, Center for Economic Studies

Abstract: We present LFCMV, a Bayesian file linking methodology designed to link records using continuous matching variables in situations where we do not expect values of these matching variables to agree exactly across matched pairs. The method involves a linking model for the distance between the matching variables of records in one file and the matching variables of their linked records in the second. This linking model is conditional on a vector indicating the links. We specify a mixture model for the distance component of the linking model, as this latent structure allows the distance between matching variables in linked pairs to vary across types of linked pairs. Finally, we specify a model for the linking vector. We describe the Gibbs sampling algorithm for sampling from the posterior distribution of this linkage model and use artificial data to illustrate model performance. We also introduce a linking application using public survey information and data from the U.S. Census of Manufactures and use LFCMV to link the records.

Keywords: latent class; faults; record linkage; file linking; energy efficiency (search for similar items in EconPapers)
Pages: 42 pages
Date: 2017-01
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https://www2.census.gov/ces/wp/2017/CES-WP-17-45.pdf First version, 2017 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:17-45

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