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Activity Number:
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332
- Estimation and Survey
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Type:
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Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Government Statistics Section
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Abstract #322900
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Title:
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High-Dimensional Temporal Disaggregation and Nowcasting: Examination of Trade-In-Services Throughout the Brexit Transition Period
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Author(s):
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Luke Mosley* and Alex Gibberd and Kaveh Salehzadeh Nobari
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Companies:
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Lancaster University and Lancaster University and Imperial College London
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Keywords:
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Time Series;
Temporal Disaggregation;
Nowcasting;
High Dimensional;
Mixed Frequency ;
Official statistics
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Abstract:
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We consider the task of temporally disaggregating quarterly trade-in-services data from the UK throughout the Brexit transition period. The paper demonstrates a novel temporal disaggregation framework that can work with many indicator series, including novel fast-indicators, to simultaneously select and estimate linear combinations of data-streams to use in a high-frequency approximation. Alongside, retrospective temporal disaggregation, we also demonstrate the utility of methods in providing simple near-time nowcasts for trade statistics at the latent monthly frequency. Our experimental results suggest utility of the method in tracking short-term dynamics and interpretation into the driving forces behind monthly trade. A more extensive version of this paper will investigate dynamics across 12 different types of service.
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Authors who are presenting talks have a * after their name.
