Abstract
This study explores the value of information transmission in training het-erogeneous 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 pat-terns 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 outper-form ANNs trained using empirical data directly on average. This result, however, var-ies 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.