Integrating rather than collecting: statistical matching in the data flood era

Riccardo D’Alberto () and Meri Raggi
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Riccardo D’Alberto: Alma Mater Studiorum – University of Bologna

Statistical Papers, 2024, vol. 65, issue 4, No 9, 2135-2163

Abstract: Abstract Statistical matching is progressively emerging as a straightforward approach to data integration. This method of increasing importance and interest is useful to address the unsolved challenges posed by data shortage as well as the several opportunities occurring in the present data flood era. This paper offers an exhaustive review of the methodology from its early beginnings up to the most recent developments, considering also the most relevant applications. The links that statistical matching has with other integration methods are discussed, analysing how a 50-year-old method has been only recently proposed under a consistent but (yet) incomplete framework. Strengths and weaknesses of statistical matching are compared, considering different data features and sample representativeness frameworks, also, given future research ideas, always keeping an eye on uncertainty, the key problem to which statistical matching tries to answer.

Keywords: Data integration; Data fusion; Imputation; Record linkage; Hot deck techniques; 62D10; 62G86; 62P20; 62P25 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-023-01468-3

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