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- Title
An introduction to applications of wavelet benchmarking with seasonal adjustment.
- Authors
Sayal, Homesh; Aston, John A. D.; Elliott, Duncan; Ombao, Hernando
- Abstract
Before adjustment, low and high frequency data sets from national accounts are frequently inconsistent. Benchmarking is the procedure used by economic agencies to make such data sets consistent. It typically involves adjusting the high frequency time series (e.g. quarterly data) so that they become consistent with the lower frequency version (e.g. annual data). Various methods have been developed to approach this problem of inconsistency between data sets. The 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 localization properties of wavelets are ideal for handling more complicated benchmarking problems. The versatility of the procedure is demonstrated by using simulation studies where we provide evidence showing that it substantially outperforms currently used methods. Finally, we apply this novel method of wavelet benchmarking to official data from the UK's Office for National Statistics.
- Subjects
WAVELETS (Mathematics); WAVELET transforms; TIME series analysis; THRESHOLDING algorithms; HARMONIC analysis (Mathematics)
- Publication
Journal of the Royal Statistical Society: Series A (Statistics in Society), 2017, Vol 180, Issue 3, p863
- ISSN
0964-1998
- Publication type
Article
- DOI
10.1111/rssa.12241