Improving Statistical Matching when Auxiliary Information is Available

Angelo Moretti, Natalie Shlomo

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

There is growing interest within National Statistical Institutes in combining available datasets containing information on a large variety of social domains. Statistical matching approaches can be used to integrate data sources through a common set of variables where each dataset contains different units that belong to the same target population. However, a common problem is related to the assumption of conditional independence among variables observed in different data sources. In this context, an auxiliary dataset containing all the variables jointly can be used to improve the statistical matching by providing information on the correlation structure of variables observed across different datasets. We propose modifying the prediction models from the auxiliary dataset through a calibration step and show that we can improve the outcome of statistical matching in a variety of settings. We evaluate the proposed approach via simulation and an application based on the European Union Statistics for Income and Living Conditions and Living Costs and Food Survey for the United Kingdom.
Original languageEnglish
Pages (from-to)619–642
Number of pages24
JournalJournal of Survey Statistics and Methodology
Volume11
Issue number3
Early online date13 Feb 2023
DOIs
Publication statusPublished - 1 Jun 2023

Keywords

  • Data fusion
  • Data integration
  • Distance hot deck
  • Model calibration
  • Predictive mean matching

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