  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.
