IDEAS home Printed from https://ideas.repec.org/a/kap/expeco/v8y2005i4p325-345.html
   My bibliography  Save this article

Why We Should Not Be Silent About Noise

Author

Listed:
  • John Hey

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. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • John Hey, 2005. "Why We Should Not Be Silent About Noise," Experimental Economics, Springer;Economic Science Association, vol. 8(4), pages 325-345, December.
  • Handle: RePEc:kap:expeco:v:8:y:2005:i:4:p:325-345
    DOI: 10.1007/s10683-005-5373-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10683-005-5373-8
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10683-005-5373-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Loomes, Graham & Moffatt, Peter G & Sugden, Robert, 2002. "A Microeconometric Test of Alternative Stochastic Theories of Risky Choice," Journal of Risk and Uncertainty, Springer, vol. 24(2), pages 103-130, March.
    2. Loomes, Graham & Sugden, Robert, 1998. "Testing Different Stochastic Specifications of Risky Choice," Economica, London School of Economics and Political Science, vol. 65(260), pages 581-598, November.
    3. Enrica Carbone & John D. Hey, 2018. "Which Error Story is Best?," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 16, pages 365-380, World Scientific Publishing Co. Pte. Ltd..
    4. Glenn W. Harrison & John A. List, 2004. "Field Experiments," Journal of Economic Literature, American Economic Association, vol. 42(4), pages 1009-1055, December.
    5. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98, World Scientific Publishing Co. Pte. Ltd..
    6. Wu, George & Gonzalez, Richard, 1998. "Common Consequence Conditions in Decision Making under Risk," Journal of Risk and Uncertainty, Springer, vol. 16(1), pages 115-139, April.
    7. Starmer, Chris & Sugden, Robert, 1989. "Probability and Juxtaposition Effects: An Experimental Investigation of the Common Ratio Effect," Journal of Risk and Uncertainty, Springer, vol. 2(2), pages 159-178, June.
    8. Harless, David W & Camerer, Colin F, 1994. "The Predictive Utility of Generalized Expected Utility Theories," Econometrica, Econometric Society, vol. 62(6), pages 1251-1289, November.
    9. Carbone, Enrica, 1997. "Investigation of stochastic preference theory using experimental data," Economics Letters, Elsevier, vol. 57(3), pages 305-311, December.
    10. John D. Hey, 2018. "Experimental investigations of errors in decision making under risk," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 17, pages 381-388, World Scientific Publishing Co. Pte. Ltd..
    11. John D. Hey, 2018. "Does Repetition Improve Consistency?," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 2, pages 13-62, World Scientific Publishing Co. Pte. Ltd..
    12. Chris Starmer, 2000. "Developments in Non-expected Utility Theory: The Hunt for a Descriptive Theory of Choice under Risk," Journal of Economic Literature, American Economic Association, vol. 38(2), pages 332-382, June.
    13. Hey, John D. & Carbone, Enrica, 1995. "Stochastic choice with deterministic preferences: An experimental investigation," Economics Letters, Elsevier, vol. 47(2), pages 161-167, February.
    14. Chris Starmer, 1992. "Testing New Theories of Choice under Uncertainty using the Common Consequence Effect," Review of Economic Studies, Oxford University Press, vol. 59(4), pages 813-830.
    15. Camerer, Colin F & Ho, Teck-Hua, 1994. "Violations of the Betweenness Axiom and Nonlinearity in Probability," Journal of Risk and Uncertainty, Springer, vol. 8(2), pages 167-196, March.
    16. Chew, S H & Epstein, Larry G & Segal, U, 1991. "Mixture Symmetry and Quadratic Utility," Econometrica, Econometric Society, vol. 59(1), pages 139-163, January.
    17. Neilson, William S & Stowe, Jill, 2002. "A Further Examination of Cumulative Prospect Theory Parameterizations," Journal of Risk and Uncertainty, Springer, vol. 24(1), pages 31-46, January.
    18. Selten, Reinhard, 1991. "Properties of a measure of predictive success," Mathematical Social Sciences, Elsevier, vol. 21(2), pages 153-167, April.
    19. Machina, Mark J, 1985. "Stochastic Choice Functions Generated from Deterministic Preferences over Lotteries," Economic Journal, Royal Economic Society, vol. 95(379), pages 575-594, September.
    20. Camerer, Colin F, 1989. "An Experimental Test of Several Generalized Utility Theories," Journal of Risk and Uncertainty, Springer, vol. 2(1), pages 61-104, April.
    21. Ballinger, T Parker & Wilcox, Nathaniel T, 1997. "Decisions, Error and Heterogeneity," Economic Journal, Royal Economic Society, vol. 107(443), pages 1090-1105, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pavlo Blavatskyy, 2007. "Stochastic expected utility theory," Journal of Risk and Uncertainty, Springer, vol. 34(3), pages 259-286, June.
    2. Pavlo R. Blavatskyy, 2020. "Dual choice axiom and probabilistic choice," Journal of Risk and Uncertainty, Springer, vol. 61(1), pages 25-41, August.
    3. Henry Stott, 2006. "Cumulative prospect theory's functional menagerie," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 101-130, March.
    4. Pavlo Blavatskyy, 2012. "Probabilistic choice and stochastic dominance," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 50(1), pages 59-83, May.
    5. Blavatskyy, Pavlo R., 2008. "Stochastic utility theorem," Journal of Mathematical Economics, Elsevier, vol. 44(11), pages 1049-1056, December.
    6. Pavlo Blavatskyy, 2018. "A second-generation disappointment aversion theory of decision making under risk," Theory and Decision, Springer, vol. 84(1), pages 29-60, January.
    7. Blavatskyy, Pavlo, 2018. "Fechner’s strong utility model for choice among n>2 alternatives: Risky lotteries, Savage acts, and intertemporal payoffs," Journal of Mathematical Economics, Elsevier, vol. 79(C), pages 75-82.
    8. Pavlo Blavatskyy, 2021. "Probabilistic independence axiom," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 46(1), pages 21-34, March.
    9. Charles Mason & Jason Shogren & Chad Settle & John List, 2005. "Investigating Risky Choices Over Losses Using Experimental Data," Journal of Risk and Uncertainty, Springer, vol. 31(2), pages 187-215, September.
    10. Wilcox, Nathaniel T., 2011. "'Stochastically more risk averse:' A contextual theory of stochastic discrete choice under risk," Journal of Econometrics, Elsevier, vol. 162(1), pages 89-104, May.
    11. Jakusch, Sven Thorsten & Meyer, Steffen & Hackethal, Andreas, 2019. "Taming models of prospect theory in the wild? Estimation of Vlcek and Hens (2011)," SAFE Working Paper Series 146, Leibniz Institute for Financial Research SAFE.
    12. Andersen, Steffen & Harrison, Glenn W. & Lau, Morten Igel & Rutström, Elisabet E., 2010. "Behavioral econometrics for psychologists," Journal of Economic Psychology, Elsevier, vol. 31(4), pages 553-576, August.
    13. Bruno S. Frey, "undated". "Knight Fever towards an Economics of Awards," IEW - Working Papers 239, Institute for Empirical Research in Economics - University of Zurich.
    14. Pavlo R. Blavatskyy, "undated". "A Stochastic Expected Utility Theory," IEW - Working Papers 231, Institute for Empirical Research in Economics - University of Zurich.
    15. John Hey & Andrea Morone & Ulrich Schmidt, 2009. "Noise and bias in eliciting preferences," Journal of Risk and Uncertainty, Springer, vol. 39(3), pages 213-235, December.
    16. Pavlo R. Blavatskyy, "undated". "Axiomatization of a Preference for Most Probable Winner," IEW - Working Papers 230, Institute for Empirical Research in Economics - University of Zurich.
    17. Anna Conte & John D. Hey & Peter G. Moffatt, 2018. "Mixture models of choice under risk," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 1, pages 3-12, World Scientific Publishing Co. Pte. Ltd..
    18. Glenn W. Harrison & John A. List, 2004. "Field Experiments," Journal of Economic Literature, American Economic Association, vol. 42(4), pages 1009-1055, December.
    19. Pavlo R. Blavatskyy & Ganna Pogrebna, 2010. "Models of stochastic choice and decision theories: why both are important for analyzing decisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(6), pages 963-986.
    20. Pavlo Blavatskyy, 2014. "Stronger utility," Theory and Decision, Springer, vol. 76(2), pages 265-286, February.

    More about this item

    Keywords

    noise; stochastic assumptions; testing; estimation; inferences;
    All these keywords.

    JEL classification:

    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:expeco:v:8:y:2005:i:4:p:325-345. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.