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Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand:

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  • Dan Farhat

    () (Department of Economics, University of Otago, New Zealand)

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.

Suggested Citation

  • Dan Farhat, 2014. "Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand:," Working Papers 1404, University of Otago, Department of Economics, revised Mar 2014.
    • Handle: RePEc:otg:wpaper:1404
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    File URL: http://www.otago.ac.nz/economics/news/otago078307.pdf
    File Function: First version, 2014
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    References listed on IDEAS

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    1. Edoardo Gaffeo & Domenico Delli Gatti & Saul Desiderio & Mauro Gallegati, 2008. "Adaptive Microfoundations for Emergent Macroeconomics," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 34(4), pages 441-463.
    2. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    3. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    4. Marco Raberto & Andrea Teglio & Silvano Cincotti, 2008. "Integrating Real and Financial Markets in an Agent-Based Economic Model: An Application to Monetary Policy Design," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 147-162, September.
    5. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    6. Steven Gonzalez, "undated". "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models," Working Papers-Department of Finance Canada 2000-07, Department of Finance Canada.
    7. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    8. Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 71-88, August.
    9. de Menezes, Lilian M. & W. Bunn, Derek & Taylor, James W., 2000. "Review of guidelines for the use of combined forecasts," European Journal of Operational Research, Elsevier, vol. 120(1), pages 190-204, January.
    10. John Cooper, 1999. "Artificial neural networks versus multivariate statistics: An application from economics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 909-921.
    11. Tesfatsion, Leigh, 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," ISU General Staff Papers 200201010800001251, Iowa State University, Department of Economics.
    12. Gatti, Domenico Delli & Guilmi, Corrado Di & Gaffeo, Edoardo & Giulioni, Gianfranco & Gallegati, Mauro & Palestrini, Antonio, 2005. "A new approach to business fluctuations: heterogeneous interacting agents, scaling laws and financial fragility," Journal of Economic Behavior & Organization, Elsevier, vol. 56(4), pages 489-512, April.
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    More about this item

    Keywords

    Artificial neural networks; forecasting; aggregate consumption; social simulation;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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