Abstract Details
Activity Number:
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593
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #311786
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Title:
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Wavelet Benchmarking with Seasonal Adjustment
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Author(s):
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Homesh Sayal*+ and John Aston and Duncan Elliott and Hernando Ombao
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Companies:
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University of Cambridge and University of Cambridge and Office of National Statistics and University of California, Irvine
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Keywords:
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Benchmarking ;
Seasonal Adjustment ;
Structural Time Series ;
Thresholding ;
Wavelets
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Abstract:
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Prior to adjustment, accounting conditions between national accounts datasets are frequently violated. Benchmarking is the procedure used by economic agencies to make such datasets consistent. It typically involves adjusting a high frequency process (i.e. quarterly data) so it becomes consistent with a lower frequency version (i.e. annual data). Various methods have been developed to approach this problem of inconsistency between datasets. This paper introduces a new statistical procedure; namely wavelet benchmarking. Wavelet properties allow high and low frequency processes to be jointly analysed. We show that benchmarking can be formulated and approached succinctly in the wavelet domain. Furthermore the time and frequency localisation properties of wavelets allow more complicated benchmarking problems to be considered. Its versatility is demonstrated using simulation studies where we provide evidence showing it substantially outperforms currently used methods. Finally, wavelet benchmarking is applied to official Office of National Statistics (ONS) data.
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Authors who are presenting talks have a * after their name.
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