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

Mattia Cai and José Rueda-Cantuche

Economic Systems Research, 2019, vol. 31, issue 3, 382-403

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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/09535314.2018.1540404 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:ecsysr:v:31:y:2019:i:3:p:382-403

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CESR20

DOI: 10.1080/09535314.2018.1540404

Access Statistics for this article

Economic Systems Research is currently edited by Bart Los and Manfred Lenzen

More articles in Economic Systems Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().