Sparse Temporal Disaggregation

TL;DR

This paper introduces a sparse temporal disaggregation method that effectively handles high-dimensional indicator data for estimating economic indicators like GDP, outperforming classical methods in simulations and real data applications.

Contribution

The paper proposes a novel sparse disaggregation technique suitable for high-dimensional data, extending traditional methods like Chow-Lin.

Findings

The new method outperforms classical approaches in simulations.

It successfully disaggregates UK GDP data with many indicators.

Demonstrates robustness in high-dimensional settings.

Abstract

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.