An introduction to applications of wavelet benchmarking with seasonal adjustment

Homesh Sayal, John A.D. Aston*, Duncan Elliott, Hernando Ombao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)863-889
Number of pages27
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
Volume180
Issue number3
DOIs
StatePublished - Jun 1 2017

Keywords

  • Benchmarking
  • Seasonal adjustment
  • Structural time series
  • Thresholding
  • Wavelets

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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