  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 volume 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, c.f. Mosley et al.
(2022). 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.
