Sparse temporal disaggregation
Luke Mosley,
Idris A. Eckley and
Alex Gibberd
Journal of the Royal Statistical Society Series A, 2022, vol. 185, issue 4, 2203-2233
Abstract:
Temporal disaggregation is a method commonly used in official statistics to enable high‐frequency estimates of key economic indicators, such as gross domestic product (GDP). Traditionally, such methods have relied on only a couple of high‐frequency indicator series to produce estimates. However, the prevalence of large, and increasing, volumes of administrative and alternative data‐sources motivates the need for such methods to be adapted for high‐dimensional settings. In this article, we propose a novel sparse temporal‐disaggregation procedure and contrast this with the classical Chow–Lin method. We demonstrate the performance of our proposed method through simulation study, highlighting various advantages realised. We also explore its application to disaggregation of UK GDP data, demonstrating the method's ability to operate when the number of potential indicators is greater than the number of low‐frequency observations.
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://doi.org/10.1111/rssa.12952
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:bla:jorssa:v:185:y:2022:i:4:p:2203-2233
Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X
Access Statistics for this article
Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples
More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().