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File Matching with Faulty Continuous Matching Variables

Author

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  • Nicole M. Dalzell
  • Jerome P. Reiter
  • Gale Boyd

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.

Suggested Citation

  • Nicole M. Dalzell & Jerome P. Reiter & Gale Boyd, 2017. "File Matching with Faulty Continuous Matching Variables," Working Papers 17-45, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:17-45
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    File URL: https://www2.census.gov/ces/wp/2017/CES-WP-17-45.pdf
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    References listed on IDEAS

    as
    1. Todd D. Gerarden & Richard G. Newell & Robert N. Stavins, 2017. "Assessing the Energy-Efficiency Gap," Journal of Economic Literature, American Economic Association, vol. 55(4), pages 1486-1525, December.
    2. Roee Gutman & Christopher C. Afendulis & Alan M. Zaslavsky, 2013. "A Bayesian Procedure for File Linking to Analyze End-of-Life Medical Costs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 34-47, March.
    3. Hang J. Kim & Jerome P. Reiter & Quanli Wang & Lawrence H. Cox & Alan F. Karr, 2014. "Multiple Imputation of Missing or Faulty Values Under Linear Constraints," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 375-386, July.
    4. Ishwaran H. & James L. F, 2001. "Gibbs Sampling Methods for Stick Breaking Priors," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 161-173, March.
    5. Hunt Allcott & Michael Greenstone, 2012. "Is There an Energy Efficiency Gap?," Journal of Economic Perspectives, American Economic Association, vol. 26(1), pages 3-28, Winter.
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