  Temporal disaggregation is a method commonly used in official statistics to
enable high-frequency estimates of key economic indicators, such as 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 gross domestic product data, demonstrating the method's ability to
operate when the number of potential indicators is greater than the number of
low-frequency observations.
