Assessment of the effect of constraints in a new multivariate mixed method for statistical matching

Juan Claramunt González, Arnout van Delden and Ton de Waal

Computational Statistics & Data Analysis, 2023, vol. 177, issue C

Abstract: A Multivariate Mixed method for Statistical Matching (MMSM) is proposed. The MMSM is a predictive mean matching method to impute values when integrating two datasets from the same population without overlapping units measuring several common and non-common variables. It considers the multivariate structure of the data by using multivariate Bayesian regression. The MMSM can also include auxiliary information from an additional dataset to improve the computation of intermediate values, and constraints to improve the selection of the donors. The results from a simulation study show that including information from an auxiliary dataset leads to far better results, especially in terms of bias and percentage of correct imputations. The inclusion of constraints also increases the quality of the imputations, and hence of the statistical matching.

Keywords: Predictive mean matching; Hard constraints; Soft constraints; Auxiliary dataset; Multiple imputation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:177:y:2023:i:c:s0167947322001499

DOI: 10.1016/j.csda.2022.107569

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