Bridging macroeconomic data between statistical classifications: the count-seed RAS approach

In applications, it is often necessary to link heavily aggregated macroeconomic datasets adhering to different statistical classifications. We propose a simple data reclassification procedure for those cases in which a bridge matrix grounded in microdata is not available. The essential requirement o...

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Bibliographic Details
Published in:Economic systems research 2019-07, Vol.31 (3), p.382-403
Main Authors: Cai, Mattia, Rueda-Cantuche, José Manuel
Format: Article
Language:English
Subjects:
backcasting
bridge matrix
Classification change
conversion factors
data reclassification
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Summary:In applications, it is often necessary to link heavily aggregated macroeconomic datasets adhering to different statistical classifications. We propose a simple data reclassification procedure for those cases in which a bridge matrix grounded in microdata is not available. The essential requirement of our approach, which we refer to as count-seed RAS, is that there exists a time period or a geographical entity similar to the one of interest for which the relevant economic variable is observed according to both classifications. From this information, a bridge matrix is constructed using bi-proportional methods to rescale a seed matrix based on a qualitative correspondence table from official sources. We test the procedure in two case studies and by Monte Carlo methods. We find that, in terms of reclassification accuracy, it performs noticeably better than other expeditious methods. The analytical framework underlying our approach may prove a useful way of conceptualizing data reclassification problems.
ISSN:0953-5314
1469-5758
DOI:10.1080/09535314.2018.1540404