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Temporal Disaggregation of Economic Time Series using Artificial Neural Networks

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  • L. Hedhili Zaier
  • M. Abed

Abstract

Several methods based on smoothing or statistical criteria have been used for deriving disaggregated values compatible with observed annual totals. The present method is based on the artificial neural networks. This article evaluates the use of artificial neural networks (ANNs) for the disaggregation of annual US GDP data to quarterly time increments. A feed-forward neural network with back-propagation algorithm for learning was used. An ANN model is introduced and evaluated in this paper. The proposed method is considered as a temporal disaggregation method without related series. A comparison with previous temporal disaggregation methods without related series has been done. The disaggregated quarterly GDP data compared well with observed quarterly data. In addition, they preserved all the basic statistics such as summing to the annual data value, cross correlation structure among quarterly flows, etc.

Suggested Citation

  • L. Hedhili Zaier & M. Abed, 2014. "Temporal Disaggregation of Economic Time Series using Artificial Neural Networks," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(8), pages 1824-1833, April.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:8:p:1824-1833
    DOI: 10.1080/03610926.2012.677088
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    Cited by:

    1. Ricci L. Reber & Sarah J. Pack, 2014. "Methods of Temporal Disaggregation for Estimating Output of the Insurance Industry," BEA Working Papers 0115, Bureau of Economic Analysis.

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