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Methods Explained: Temporal disaggregation

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  • Graeme Chamberlin

    (Office for National Statistics)

Abstract

SummaryNational statistics institutions often face the task of producing timely data, such as monthly and quarterly time series, even though sources are less timely. Temporal disaggregation is the process of deriving high frequency data from low frequency data, and is closely related to benchmarking and interpolation. This article describes and demonstrates some of the available techniques.

Suggested Citation

  • Graeme Chamberlin, 2010. "Methods Explained: Temporal disaggregation," Economic & Labour Market Review, Palgrave Macmillan;Office for National Statistics, vol. 4(11), pages 106-121, November.
    • Handle: RePEc:pal:ecolmr:v:4:y:2010:i:11:p:106-121
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    Cited by:

    1. Adrienne Mack & Enrique Martínez García, 2011. "A cross-country quarterly database of real house prices: a methodological note," Globalization Institute Working Papers 99, Federal Reserve Bank of Dallas.
    2. Alderete Peralta, Ali & Balta-Ozkan, Nazmiye & Longhurst, Philip, 2022. "Spatio-temporal modelling of solar photovoltaic adoption: An integrated neural networks and agent-based modelling approach," Applied Energy, Elsevier, vol. 305(C).
    3. Tihana Skrinjaric, 2022. "Macroeconomic effects of systemic stress: a rolling spillover index approach," Public Sector Economics, Institute of Public Finance, vol. 46(1), pages 109-140.
    4. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.

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