Skip to main content Accessibility help
×
Home
Hostname: page-component-59b7f5684b-8dvf2 Total loading time: 0.275 Render date: 2022-09-28T13:13:20.030Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

LEARNING THE OPTIMAL BUFFER-STOCK CONSUMPTION RULE OF CARROLL

Published online by Cambridge University Press:  18 March 2013

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: yildi@u-bordeaux4.fr.

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.

Type
Articles
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Allen, Todd W. and Carroll, Christopher D. (2001) Individual learning about consumption. Macroeconomic Dynamics 5 (2), 255271.CrossRefGoogle Scholar
Arifovic, J. (1994) Genetic algorithm learning and the cobweb model. Journal of Economic Dynamics and Control 18, 328.CrossRefGoogle Scholar
Butler, N.A. (2001) Optimal and orthogonal latin hypercube designs for computer experiments. Biometrika 88 (3), 847857.CrossRefGoogle Scholar
Carroll, Christopher D. (1992) The buffer-stock theory of saving: Some macroeconomic evidence. Brookings Papers on Economic Activity (2), 61156.CrossRefGoogle Scholar
Carroll, Christopher D. (1997) Buffer stock saving and the life cycle/permanent income hypothesis. Quarterly Journal of Economics 112 (1), 156.CrossRefGoogle Scholar
Carroll, Christopher D. (2001) A theory of the consumption function, with and without liquidity constraints. Journal of Economic Perspectives 15 (3), 2346.CrossRefGoogle 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), 4555.CrossRefGoogle Scholar
Deaton, Angus (1991) Saving and liquidity constraints. Econometrica 59 (5), 12211248.CrossRefGoogle Scholar
Deaton, Angus (1992) Understanding Consumption. New York: Oxford University Press.CrossRefGoogle 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), 237248.CrossRefGoogle 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), 363370.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.Google Scholar
Howitt, Peter and Özak, Ömer (2009) Adaptive Consumption Behavior. Working paper 15427, NBER.Google 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), 263289.CrossRefGoogle Scholar
Lettau, Martin and Harald, Uhlig (1999) Rules of thumb versus dynamic programming. American Economic Review 89 (1), 148174.CrossRefGoogle 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 ScholarPubMed
Salmon, Mark (1995) Bounded rationality and learning: Procedural learning. In Kirman, Alan and Salmon, Mark (eds.), Learning and Rationality in Economics, pp. 236275. 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. 129148. Cambridge, UK: Cambridge University Press.CrossRefGoogle 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), 670690.CrossRefGoogle 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), 119.CrossRefGoogle Scholar
Wickham, Hardley (2009) ggplot2 Elegant Graphics for Data Analysis. Dordrecht: Springer.Google Scholar
Wilson, Stewart W. (1995) Classifier fitness based on accuracy. Evolutionary Computation 3 (2), 149175.CrossRefGoogle Scholar
Ye, K.Q. (1998) Orthogonal column latin hypercubes and their application in computer experiments. Journal of the American Statistical Association 93 (444), 14301439.CrossRefGoogle 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), 203220.CrossRefGoogle Scholar
Yildizoglu, Murat (2002) Competing R&D strategies in an evolutionary industry model. Computational Economics 19 (1), 5265.CrossRefGoogle Scholar
5
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

LEARNING THE OPTIMAL BUFFER-STOCK CONSUMPTION RULE OF CARROLL
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

LEARNING THE OPTIMAL BUFFER-STOCK CONSUMPTION RULE OF CARROLL
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

LEARNING THE OPTIMAL BUFFER-STOCK CONSUMPTION RULE OF CARROLL
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *