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LEARNING THE OPTIMAL BUFFER-STOCK CONSUMPTION RULE OF CARROLL

Published online by Cambridge University Press:  18 March 2013

Murat Yıldızoğlu ,
Marc-Alexandre Sénégas ,
Isabelle Salle  and
Martin Zumpe
Murat Yıldızoğlu*
Affiliation:
GREThA UMR CNRS 5113 and University of Bordeaux
Marc-Alexandre Sénégas
Affiliation:
GREThA UMR CNRS 5113 and University of Bordeaux
Isabelle Salle
Affiliation:
GREThA UMR CNRS 5113 and University of Bordeaux
Martin Zumpe
Affiliation:
GREThA UMR CNRS 5113 and University of Bordeaux
*
Address correspondence to: Murat Yıldızoğlu, GREThA (UMR CNRS 5113), Bordeaux University, Avenue Léon Duguit, F-33608 PESSAC Cedex, France; e-mail: [email protected].
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Abstract

This article questions the rather pessimistic conclusions of Allen and Carroll [Macroeconomic Dynamics 5 (2001), 255–271] about the ability of consumers to learn the optimal buffer-stock-based consumption rule. To this end, we develop an agent-based model in which alternative learning schemes can be compared in terms of the consumption behavior that they yield. We show that neither purely adaptive learning nor social learning based on imitation can ensure satisfactory consumption behavior. In contrast, if the agents can form adaptive expectations, based on an evolving individual mental model, their behavior becomes much more interesting in terms of its regularity and its ability to improve performance (which is a clear manifestation of learning). Our results indicate that assumptions on bounded rationality and on adaptive expectations are perfectly compatible with sound and realistic economic behavior, which, in some cases, can even converge to the optimal solution. This framework may therefore be used to develop macroeconomic models with adaptive dynamics.

Keywords

Consumption DecisionsLearningExpectationsAdaptive BehaviorComputational Economics
Type
Articles
Information
Macroeconomic Dynamics , Volume 18 , Issue 4 , June 2014 , pp. 727 - 752
Copyright
Copyright © Cambridge University Press 2013 

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References

REFERENCES

Allen, Todd W. and Carroll, Christopher D. (2001) Individual learning about consumption. Macroeconomic Dynamics 5 (2), 255–271.CrossRefGoogle Scholar
Arifovic, J. (1994) Genetic algorithm learning and the cobweb model. Journal of Economic Dynamics and Control 18, 3–28.Google Scholar
Butler, N.A. (2001) Optimal and orthogonal latin hypercube designs for computer experiments. Biometrika 88 (3), 847–857.CrossRefGoogle Scholar
Carroll, Christopher D. (1992) The buffer-stock theory of saving: Some macroeconomic evidence. Brookings Papers on Economic Activity (2), 61–156.Google Scholar
Carroll, Christopher D. (1997) Buffer stock saving and the life cycle/permanent income hypothesis. Quarterly Journal of Economics 112 (1), 1–56.Google Scholar
Carroll, Christopher D. (2001) A theory of the consumption function, with and without liquidity constraints. Journal of Economic Perspectives 15 (3), 23–46.Google Scholar
Carroll, Christopher D. (2004) Theoretical Foundations of Buffer Stock Saving. Working paper 10867 NBER.Google Scholar
Cioppa, T.M. (2002) Efficient Nearly Orthogonal And Space-Filling Experimental Designs For High-Dimensional Complex Models. Ph.D. dissertation, in philosophy in operations research, Naval Postgraduate School.Google Scholar
Cioppa, Thomas M. and Lucas, Thomas W. (2007) Efficient nearly orthogonal and space-filling latin hypercubes. Technometrics 49 (1), 45–55.Google Scholar
Deaton, Angus (1991) Saving and liquidity constraints. Econometrica 59 (5), 1221–1248.Google Scholar
Deaton, Angus (1992) Understanding Consumption. New York: Oxford University Press.Google Scholar
Evans, G.W. and Honkapohja, S. (2001) Learning and Expectations in Macroeconomics. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Fang, K.T., Lin, D.K.J., Winker, P., and Zhang, Y. (2000) Uniform design: Theory and application. Technometrics 42 (3), 237–248.Google Scholar
Goupy, J. and Creighton, L. (2007) Introduction to Design of Experiments with JMP Examples, 3rd ed.Cary, NC: SAS Institute.Google Scholar
Happe, K. (2005) Agent-Based Modelling and Sensitivity Analysis by Experimental Design and Metamodelling: An Application to Modelling Regional Structural Change. Paper prepared for the XIth International Congress of the European Association of Agricultural Economists.Google Scholar
Holland, John H. (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge, MA: MIT Press.Google Scholar
Holland, J. and Miller, J. H. (1991) Artificial adaptive agents in economic theory. American Economic Review Papers and Procedings 81 (2), 363–370.Google Scholar
Holland, J.H., Holyoak, K.J., Nisbett, R.E., and Thagard, P.R. (1989) Induction. Processes of Inference, Learning and Discoverey. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Howitt, Peter and Özak, Ömer (2009) Adaptive Consumption Behavior. Working paper 15427, NBER.CrossRefGoogle Scholar
Kleijnen, Jack P.C., Sanchez, Susan M., Lucas, Thomas W., and Cioppa, Thomas M. (2005) A user's guide to the brave new world of designing simulation experiments. INFORMS Journal on Computing 17 (3), 263–289.Google Scholar
Lettau, Martin and Harald, Uhlig (1999) Rules of thumb versus dynamic programming. American Economic Review 89 (1), 148–174.Google Scholar
Masters, Timothy (1993) Practical Neural Network Recipes in C++. New York: Academic Press.Google Scholar
Oeffner, M. (2008) Agent-Based Keynesian Macroeconomics—An Evolutionary Model Embedded in an Agent-Based Computer Simulation. Doctoral dissertation, Bayerische Julius—Maximilians Universitat.Google Scholar
R Development Core Team (2003) R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Available at http://www.r-project.org.Google Scholar
Salmon, Mark (1995) Bounded rationality and learning: Procedural learning. In Kirman, Alan and Salmon, Mark (eds.), Learning and Rationality in Economics, pp. 236–275. Oxford, UK: Blackwell.Google Scholar
Sanchez, S.M. (2005) Nolhdesigns Spreadsheet. Available at http://diana.cs.nps.navy.mil/SeedLab/.Google Scholar
Simon, Herbert A. (1976) From substantial to procedural rationality. In Latsis, S.J. (ed.), Method and Appraisal in Economics, pp. 129–148. Cambridge, UK: Cambridge University Press.Google Scholar
Sutton, Richard S. and Barto, Andrew G. (1998) Reinforcement Learning: an Introduction. Cambridge, MA: MIT Press.Google Scholar
Vallée, Thomas and Yıldızoğlu, Murat (2009) Convergence in the finite cournot oligopoly with social and individual learning. Journal of Economic Behaviour and Organization (72), 670–690.Google Scholar
Vriend, Nicolaas (2000) An illustration of the essential difference between individual and social learning, and its consequences for computational analyses. Journal of Economic Dynamics and Control 24 (1), 1–19.Google Scholar
Wickham, Hardley (2009) ggplot2 Elegant Graphics for Data Analysis. Dordrecht: Springer.CrossRefGoogle Scholar
Wilson, Stewart W. (1995) Classifier fitness based on accuracy. Evolutionary Computation 3 (2), 149–175.Google Scholar
Ye, K.Q. (1998) Orthogonal column latin hypercubes and their application in computer experiments. Journal of the American Statistical Association 93 (444), 1430–1439.Google Scholar
Yildizoglu, Murat (2001) Connecting adaptive behaviour and expectations in models of innovation: The potential role of artificial neural networks. European Journal of Economics and Social Systems 15 (3), 203–220.CrossRefGoogle Scholar
Yildizoglu, Murat (2002) Competing R&D strategies in an evolutionary industry model. Computational Economics 19 (1), 52–65.Google Scholar