Mosley, Luke, Salehzadeh Nobari, Kaveh ORCID: 0000-0002-4053-0781, Brandi, Giuseppe and Gibberd, Alex
(2025)
Disaggregating time-series with many indicators: an overview of the disaggregateTS package.
The R Journal, 16 (4).
pp. 62-73.
ISSN 2073-4859
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Text (RJ-2024-035)
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Abstract
Low-frequency time-series (e.g., quarterly data) are often treated as benchmarks for interpolating to higher frequencies, since they generally exhibit greater precision and accuracy in contrast to their high-frequency counterparts (e.g., monthly data) reported by governmental bodies. An array of regression-based methods have been proposed in the literature which aim to estimate a target high-frequency series using higher frequency indicators. However, in the era of big data and with the prevalence of large volumes of administrative data-sources there is a need to extend traditional methods to work in high-dimensional settings, i.e., where the number of indicators is similar or larger than the number of low-frequency samples. The package DisaggregateTS includes both classical regressions-based disaggregation methods alongside recent extensions to high-dimensional settings. This paper provides guidance on how to implement these methods via the package in R, and demonstrates their use in an application to disaggregating CO2 emissions.
Item Type: | Article |
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Additional Information: | © 2025 The Author(s) |
Divisions: | Psychological and Behavioural Science |
Date Deposited: | 31 Jul 2025 12:03 |
Last Modified: | 31 Jul 2025 12:03 |
URI: | http://eprints.lse.ac.uk/id/eprint/128975 |
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