Hostname: page-component-7dd5485656-tbj44 Total loading time: 0 Render date: 2025-10-31T04:13:47.932Z Has data issue: false hasContentIssue false
  • English
  • Français

Why We Should Not Be Silent About Noise

Published online by Cambridge University Press:  14 March 2025

John D. Hey
John D. Hey*
Affiliation:
Universities of York and Bari; Department of Economics, University of York, Heslington, York YO10 5DD, UK
*
email: jdh1@york.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

There is an odd contradiction about much of the empirical (experimental) literature: The data is analysed using statistical tools which presuppose that there is some noise or randomness in the data, but the source and possible nature of the noise are rarely explicitly discussed. This paper argues that the noise should be brought out into the open, and its nature and implications openly discussed. Whether the statistical analysis involves testing or estimation, the analysis inevitably is built upon some assumed stochastic structure to the noise. Different assumptions justify different analyses, which means that the appropriate type of analysis depends crucially on the stochastic nature of the noise. This paper explores such issues and argues that ignoring the noise can be dangerous.

Keywords

noisestochastic assumptionstestingestimationinferences

JEL classification

C91: Laboratory, Individual Behavior D81: Criteria for Decision-Making under Risk and Uncertainty B41: Economic Methodology C50: General

Information

Type
Research Article
Information
Experimental Economics , Volume 8 , Issue 4: Exploring the Error in Experimental Economics , December 2005 , pp. 325 - 345
Copyright
Copyright © 2005 Economic Science Association

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.)

Article purchase

Temporarily unavailable

References

Abdellaoui, M. and Munier, B. (1998). “The Risk-Structure Dependence Effect: Experimenting with an Eye to Decision Making.” Annals of Operational Research. 80, 237–252.10.1023/A:1018916031387CrossRefGoogle Scholar
Allais, M. (1953). “Le Comportamente del’Homme Rationale devante le Risque: Critiques des Postulats et Axioms de l’Ecole Americaine.” Econometrica. 21, 503–546.CrossRefGoogle Scholar
Allais, M. and Hagen, O. (1979). Expected Utility Hypotheses and the Allais Paradox, Dordrecht: Reidel.10.1007/978-94-015-7629-1CrossRefGoogle Scholar
Ballinger, T.P. and Wilcox, N.T. (1997). “Decisions, Error and Heterogeneity.” Economic Journal. 107, 10901105.10.1111/j.1468-0297.1997.tb00009.xCrossRefGoogle Scholar
Blavatskyy, P. (2005). “A Stochastic Expected Utility Theory.” Working paper Institute for Empirical Research in Economics, University of Zurich.10.2139/ssrn.664364CrossRefGoogle Scholar
Camerer, C. (1989). “An Experimental Test of Several Generalized Utility Theories.” Journal of Risk and Uncertainty. 2, 61–104.10.1007/BF00055711CrossRefGoogle Scholar
Camerer, C. (1992). “Recent Tests of Generalizations of EU Theories.” in Edwards, W. (ed). Utility: Theories, Measurement and Applications, Kluwer.Google Scholar
Camerer, C. (1995). “Individual decision making.” In Kagel, J. and Roth, A. (eds)., The Handbook of Experimental Economics, Princeton, Princeton University Press, 587–703.10.1515/9780691213255-010CrossRefGoogle Scholar
Camerer, C. and Ho, T. (1994). “Violations of the Betweenness Axiom and Nonlinearity in Probability.” Journal of Risk and Uncertainty. 8, 167–196.10.1007/BF01065371CrossRefGoogle Scholar
Carbone, E. (1997). “Investigation of Stochastic Preference Theory Using Experimental Data.” Economic Letters. 57, 305–11.10.1016/S0165-1765(97)00244-9CrossRefGoogle Scholar
Carbone, E. and Hey, J.D. (1994). “Estimation of Expected Utility and Non-Expected Utility Preference Functionals Using Complete Ranking Data.” in Munier B and Machina M J (eds). Models and Experiments on Risk and Rationality, Kluwer Academic Publishers, 119–139.10.1007/978-94-017-2298-8_7CrossRefGoogle Scholar
Carbone, E. and Hey, J.D. (1995). “A Comparison of the Estimates of EU and non-EU Preference Functionals Using Data from Pairwise Choice and Complete Ranking Experiments.” Geneva Papers on Risk and Insurance Theory. 20, 111–133.10.1007/BF01098961CrossRefGoogle Scholar
Carbone, E. and Hey, J.D. (2000). “Which Error Story is Best?.” Journal of Risk and Uncertainty. 20, 161–176.10.1023/A:1007829024107CrossRefGoogle Scholar
Chew, C.S., Epstein, L.G., and Segal, U. (1991). “Mixture Symmetry and Quadratic Utility.” Econometrica. 59, 139–164.CrossRefGoogle Scholar
Gonzalez, R. and Wu, G. (1999). “On the Shape of the Probability Weighting Function.” Cognitive Psychology. 38, 129–166.10.1006/cogp.1998.0710CrossRefGoogle ScholarPubMed
Harless, D.W. and Camerer, C.F. (1994). “The Predictive Utility of Generalized Expected Utility Theories.” Econometrica. 62, 1251–1290.10.2307/2951749CrossRefGoogle Scholar
Harrison, G.W. and List, J.A. (2004). “Field Experiments.” Journal of Economic Literature. 42, 10091055.10.1257/0022051043004577CrossRefGoogle Scholar
Hey, J.D. (1995). “Experimental Investigations of Errors in Decision Making Under Risk.” European Economic Review. 39, 641–648.10.1016/0014-2921(09)40007-4CrossRefGoogle Scholar
Hey, J.D. (2001). “Does Repetition Improve Consistency?.” Experimental Economics. 4, 5–54.10.1023/A:1011486405114CrossRefGoogle Scholar
Hey, J.D. and Carbone, E. (1995). “Stochastic Choice with Deterministic Preferences: An Experimental Investigation.” Economics Letters. 47, 161–167.10.1016/0165-1765(94)00533-8CrossRefGoogle Scholar
Hey, J.D. and Orme, C.D. (1994). “Investigating Generalisations of Expected utility Theory Using Experimental Data.” Econometrica. 62, 1291–1326.10.2307/2951750CrossRefGoogle Scholar
Loomes, G. and Sugden, R. (1995). “Incorporating a Stochastic Element into Decision Theories.” European Economic Review. 39, 641–648.10.1016/0014-2921(94)00071-7CrossRefGoogle Scholar
Loomes, G. and Sugden, R. (1998). “Testing Different Stochastic Specifications of Risky Choice.” Economica. 65, 581–98.10.1111/1468-0335.00147CrossRefGoogle Scholar
Loomes, G., Moffatt, P.G., and Sugden, R. (2002). “A microeconometric test of alternative stochastic theories of risky choice.” Journal of Risk and Uncertainty. 24, 103–130.10.1023/A:1014094209265CrossRefGoogle Scholar
Machina, M.J. (1985). “Stochastic Choice Functions Generated from Deterministic Preferences over Lotteries.” Economic Journal. 95, 575–594.10.2307/2233028CrossRefGoogle Scholar
Neilson, W. and Stowe, J. “A further examination of cumulative prospect theory parameterizations.” Journal of Risk and Uncertainty. 24, 31–46.10.1023/A:1013225326328CrossRefGoogle Scholar
Schmidt, U. and Hey, J.D. (2004). “Are Preference Reversals Errors?” Journal of Risk and Uncertainty. 29, 207–218.10.1023/B:RISK.0000046143.10752.0aCrossRefGoogle Scholar
Selten, R. (1991). “Properties of a Measure of Predictive Success.” Mathematical Social Sciences. 21, 153–167.10.1016/0165-4896(91)90076-4CrossRefGoogle Scholar
Starmer, C. (1992). “Testing New Theories of Choice Under Uncertainty Using the Common Consequence Effect.” Review of Economic Studies. 59, 813–830.10.2307/2297999CrossRefGoogle Scholar
Starmer, C. (2000). “Developments in Non-Expected Utility Theory: The Hunt for a Descriptive Theory of Choice Under Risk.” Journal of Economic Literature. 38, 332–382.10.1257/jel.38.2.332CrossRefGoogle Scholar
Starmer, C. and Sugden, R. (1989). “Probability and Juxtaposition Effects: An Experimental Investigation of the Common Ratio Effect.” Journal of Risk and Uncertainty. 2, 159–178.10.1007/BF00056135CrossRefGoogle Scholar
Wu, G. and Gonzalez, R. (1996). “Curvature of the Probability Weighting Function.” Management Science. 42, 1676–90.10.1287/mnsc.42.12.1676CrossRefGoogle Scholar
Wu, G. and Gonzalez, R. (1998). “Common Consequence Conditions in Decision Making under Risk.” Journal of Risk and Uncertainty. 16, 115–139.10.1023/A:1007714509322CrossRefGoogle Scholar