Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand
AbstractThis study uses artificial neural networks (ANNs) to reproduce aggregate per-capita consumption patterns for the New Zealand economy. Results suggest that non-linear ANNs can outperform a linear econometric model at out-of-sample forecasting. The best ANN at matching in-sample data, however, is rarely the best predictor. To improve the accuracy of ANNs using only in-sample information, methods for combining heterogeneous ANN forecasts are explored. The frequency that an individual ANN is a top performer during in-sample training plays a beneficial role in consistently producing accurate out-of-sample patterns. Possible avenues for incorporating ANN structures into social simulation models of consumption are discussed.
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Bibliographic InfoPaper provided by University of Otago, Department of Economics in its series Working Papers with number 1205.
Length: 26 pages
Date of creation: Dec 2012
Date of revision: Dec 2012
International Migration; International Agreements; Regional Labour Markets;
Find related papers by JEL classification:
- F22 - International Economics - - International Factor Movements and International Business - - - International Migration
- F55 - International Economics - - International Relations, National Security, and International Political Economy - - - International Institutional Arrangements
- R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-12-22 (All new papers)
- NEP-CMP-2012-12-22 (Computational Economics)
- NEP-FOR-2012-12-22 (Forecasting)
- NEP-ICT-2012-12-22 (Information & Communication Technologies)
- NEP-ORE-2012-12-22 (Operations Research)
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- 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.
- Steven Gonzalez, . "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models," Working Papers-Department of Finance Canada 2000-07, Department of Finance Canada.
- Church, Keith B. & Curram, Stephen P., 1996. "Forecasting consumers' expenditure: A comparison between econometric and neural network models," International Journal of Forecasting, Elsevier, vol. 12(2), pages 255-267, June.
- 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, Society for Computational Economics, vol. 32(1), pages 147-162, September.
- Tesfatsion, Leigh S., 2002. "Agent-Based Computational Economics: Growing Economies from the Bottom Up," Staff General Research Papers 5075, Iowa State University, Department of Economics.
- Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
- Edoardo Gaffeo & Domenico Delli Gatti & Saul Desiderio & Mauro Gallegati, 2008.
"Adaptive microfoundations for emergent macroeconomics,"
Department of Economics Working Papers
0802, Department of Economics, University of Trento, Italia.
- Edoardo Gaffeo & Domenico Delli Gatti & Saul Desiderio & Mauro Gallegati, 2008. "Adaptive Microfoundations for Emergent Macroeconomics," Eastern Economic Journal, Palgrave Macmillan, vol. 34(4), pages 441-463.
- 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.
- 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.
- Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
- Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Society for Computational Economics, vol. 28(1), pages 71-88, August.
- Mirowski, Philip, 2007. "Markets come to bits: Evolution, computation and markomata in economic science," Journal of Economic Behavior & Organization, Elsevier, vol. 63(2), pages 209-242, June.
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