Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand

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

No 1404, Working Papers from University of Otago, Department of Economics

Abstract: This study engineers a household sector where individuals process macroeconomic information to reproduce consumption spending patterns in New Zealand. To do this, heterogeneous artificial neural networks (ANNs) are trained to forecast changes in consumption. In contrast to existing literature, results suggest that there exists a trained ANN that significantly outperforms a linear econometric model at out-of-sample forecasting. To improve the accuracy of ANNs using only in - sample information, methods for combining private knowledge into social knowledge are explored. For one type of ANN, relying on an expert is beneficial. For most ANN structures, weighting an individual’s forecast according to how frequently that individual’s ANN is a top performer during in - sample training produces more accurate social forecasts. By focusing only on recent periods, considering the severity of an individual’s errors in weighting their forecast is also beneficial. Possible avenues for incorporating ANN structures into artificial social simulation models of consumption are discussed.

Keywords: Artificial neural networks; forecasting; aggregate consumption; social simulation (search for similar items in EconPapers)
JEL-codes: C45 E17 E27 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2014-03, Revised 2014-03
New Economics Papers: this item is included in nep-cmp, nep-for, nep-mac and nep-ore
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http://www.otago.ac.nz/economics/news/otago078307.pdf First version, 2014 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:otg:wpaper:1404

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