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Data Fusion for Joining Income and Consumtion Information using Different Donor-Recipient Distance Metrics
[journal article]
Abstract
Data fusion describes the method of combining data from (at least) two initially independent data sources to allow for joint analysis of variables which are not jointly observed. The fundamental idea is to base inference on identifying assumptions, and on common variables which provide information t... view more
Data fusion describes the method of combining data from (at least) two initially independent data sources to allow for joint analysis of variables which are not jointly observed. The fundamental idea is to base inference on identifying assumptions, and on common variables which provide information that is jointly observed in all the data sources. A popular class of methods dealing with this particular missing-data problem in practice is based on covariate-based nearest neighbour matching, whereas more flexible semi- or even fully parametric approaches seem underrepresented in applied data fusion. In this article we compare two different approaches of nearest neighbour hot deck matching: One, Random Hot Deck, is a variant of the covariate-based matching methods which was proposed by Eurostat, and can be considered as a 'classical' statistical matching method, whereas the alternative approach is based on Predictive Mean Matching. We discuss results from a simulation study where we deviate from previous analyses of marginal distributions and consider joint distributions of fusion variables instead, and our findings suggest that Predictive Mean Matching tends to outperform Random Hot Deck.... view less
Keywords
data; mean; statistics; income distribution; consumer; simulation
Classification
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods
Research Design
Free Keywords
statistical matching; missing data; predictive mean matching; nearest neighbour Imputation; missing-by-design pattern; EU-SILC 2015
Document language
English
Publication Year
2022
Page/Pages
p. 509-532
Journal
Journal of Official Statistics, 38 (2022) 2
DOI
https://doi.org/10.2478/jos-2022-0024
ISSN
2001-7367
Status
Published Version; peer reviewed
Licence
Creative Commons - Attribution-Noncommercial-No Derivative Works 4.0