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

ABSTRACT

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.

KEYWORDS:

• Classification change
• data reclassification
• bridge matrix
• conversion factors
• backcasting

Acknowledgments

The authors would like to thank Petr Musil and his colleagues at the Czech statistical office for sharing data and information regarding their reclassification practices. Mattia Cai gratefully acknowledges the funding received in the early stages of this work from his previous employer, the Free University of Bolzano-Bozen, Italy. Responsibility for the information and views expressed in this article lies entirely with the authors.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 In real-world applications, convergence may also be hindered by the fact the base-year source and target vectors used to balance the seed matrix come from different vintages and are thus not entirely consistent with each other (e.g. they do not add up to the same grand total).