Information Processing, Pattern Transmission and Aggregate Consumption Patterns in New Zealand

Dan Farhat ()
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Dan Farhat: Department of Economics, University of Otago, New Zealand

No 1405, Working Papers from University of Otago, Department of Economics

Abstract: This study explores the value of information transmission in training heterogeneous Artificial Neural Network (ANN) models to identify patterns in the growth rate of aggregate per-capita consumption spending in New Zealand. A tier structure is used to model how information passes from one ANN to another. A group of ‘tier 1’ ANNs are first trained to identify consumption patterns using economic data. ANNs in subsequent tiers are also trained to identify consumption patterns, but they use the patterns constructed by ANNs trained in the preceding tier (secondary information) as in-puts. The model’s results suggest that it is possible for ANNs downstream to outperform ANNs trained using empirical data directly on average. This result, however, varies from time period to time period. Increasing access to secondary information is shown to increase the similarity of heterogeneous predictions by ANNs in lower tiers, but not substantially affect average accuracy.

Keywords: Artificial neural networks; aggregate consumption patterns; information transmission (search for similar items in EconPapers)
JEL-codes: C45 C63 E27 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2014-03, Revised 2014-03
New Economics Papers: this item is included in nep-mac
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http://www.otago.ac.nz/economics/news/otago078308.pdf First version, 2014 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:otg:wpaper:1405

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