An Introduction to Applications of Wavelet Benchmarking with Seasonal Adjustment

TL;DR

This paper introduces wavelet benchmarking, a new statistical method for adjusting high-frequency economic data to be consistent with lower-frequency data, leveraging wavelet properties for improved accuracy.

Contribution

The paper presents a novel wavelet-based benchmarking procedure that outperforms existing methods and effectively handles complex data adjustment problems.

Findings

Wavelet benchmarking outperforms current methods in simulations.

The method effectively handles complex, real-world data.

Application to official statistics demonstrates practical utility.

Abstract

Prior to adjustment, accounting conditions between national accounts data sets are frequently violated. Benchmarking is the procedure used by economic agencies to make such data sets consistent. It typically involves adjusting a high frequency time series (e.g. quarterly data) so it becomes consistent with a lower frequency version (e.g. annual data). Various methods have been developed to approach this problem of inconsistency between data sets. This paper introduces a new statistical procedure; namely wavelet benchmarking. Wavelet properties allow high and low frequency processes to be jointly analysed and we show that benchmarking can be formulated and approached succinctly in the wavelet domain. Furthermore the time and frequency localisation properties of wavelets are ideal for handling more complicated benchmarking problems. The versatility of the procedure is demonstrated using simulation studies where we provide evidence showing it substantially outperforms currently used methods. Finally, we apply this novel method of wavelet benchmarking to official Office of National Statistics (ONS) data.