Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/22268.html
   My bibliography  Save this paper

Density Based Regression for Inhomogeneous Data: Application to Lottery Experiments

Author

Listed:
  • Kontek, Krzysztof
Abstract
This paper presents a regression procedure for inhomogeneous data characterized by varying variance, skewness and kurtosis or by an unequal amount of data over the estimation domain. The concept is based first on the estimation of the densities of an observed variable for given values of explanatory variable(s). These density functions are then used to estimate the relation between all the variables. The mean, quantile (including median) and mode re-gression estimators are proposed, with the last one appearing to be the maximum likelihood estimator in the density based approach. The paper demonstrates the advantages of the pro-posed methodology, which eliminates most of the estimation problems arising from data inhomogeneity. These include the computational inconveniences of the standard quantile and mode regression techniques. The proposed methodology, when applied to lottery experiments, makes it possible to confirm and to extend the previously presented conclusion (Kontek, 2010) that lottery valuations are only nonlinear with respect to probability when medians and means are considered. Such nonlinearity disappears once modes are considered. This means that the most likely behavior of a group is fully rational. The regression procedure presented in this paper is, however, very general and may be applied in many other cases of data inhomogeneity and not just lottery experiments.

Suggested Citation

  • Kontek, Krzysztof, 2010. "Density Based Regression for Inhomogeneous Data: Application to Lottery Experiments," MPRA Paper 22268, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:22268
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/22268/1/MPRA_paper_22268.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    2. Krzysztof Kontek, 2009. "Lottery valuation using the aspiration / relative utility function," Working Papers 39, Department of Applied Econometrics, Warsaw School of Economics.
    3. Ulrich Schmidt & Stefan Traub, 2009. "An Experimental Investigation of the Disparity Between WTA and WTP for Lotteries," Theory and Decision, Springer, vol. 66(3), pages 229-262, March.
    4. Kontek, Krzysztof, 2010. "Mean, Median or Mode? A Striking Conclusion From Lottery Experiments," MPRA Paper 21758, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kontek, Krzysztof, 2010. "Multi-Outcome Lotteries: Prospect Theory vs. Relative Utility," MPRA Paper 22947, University Library of Munich, Germany.
    2. Kontek, Krzysztof, 2010. "Estimation of Peaked Densities Over the Interval [0,1] Using Two-Sided Power Distribution: Application to Lottery Experiments," MPRA Paper 22378, University Library of Munich, Germany.

    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. Kontek, Krzysztof, 2010. "Mean, Median or Mode? A Striking Conclusion From Lottery Experiments," MPRA Paper 21758, University Library of Munich, Germany.
    2. Roth, Gerrit, 2006. "Predicting the Gap between Willingness to Accept and Willingness to Pay," Munich Dissertations in Economics 4901, University of Munich, Department of Economics.
    3. Kontek, Krzysztof, 2009. "Absolute vs. Relative Notion of Wealth Changes," MPRA Paper 17336, University Library of Munich, Germany.
    4. Toritseju Begho & Kelvin Balcombe, 2023. "Attitudes to Risk and Uncertainty: New Insights From an Experiment Using Interval Prospects," SAGE Open, , vol. 13(3), pages 21582440231, July.
    5. Uri Benzion & Shosh Shahrabani & Tal Shavit, 2013. "Retesting The Uncertainty Effect Using Lotteries With Real Products And Money," Bulletin of Economic Research, Wiley Blackwell, vol. 65, pages 175-186, May.
    6. Cathleen Johnson & Aurélien Baillon & Han Bleichrodt & Zhihua Li & Dennie Dolder & Peter P. Wakker, 2021. "Prince: An improved method for measuring incentivized preferences," Journal of Risk and Uncertainty, Springer, vol. 62(1), pages 1-28, February.
    7. 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.
    8. Kontek, Krzysztof, 2010. "Multi-Outcome Lotteries: Prospect Theory vs. Relative Utility," MPRA Paper 22947, University Library of Munich, Germany.
    9. Oliver Linton & Esfandiar Maasoumi & Yoon-Jae Wang, 2002. "Consistent testing for stochastic dominance: a subsampling approach," CeMMAP working papers 03/02, Institute for Fiscal Studies.
    10. van den Bergh, J.C.J.M. & Botzen, W.J.W., 2015. "Monetary valuation of the social cost of CO2 emissions: A critical survey," Ecological Economics, Elsevier, vol. 114(C), pages 33-46.
    11. Heiko Karle & Georg Kirchsteiger & Martin Peitz, 2015. "Loss Aversion and Consumption Choice: Theory and Experimental Evidence," American Economic Journal: Microeconomics, American Economic Association, vol. 7(2), pages 101-120, May.
    12. Shoji, Isao & Kanehiro, Sumei, 2016. "Disposition effect as a behavioral trading activity elicited by investors' different risk preferences," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 104-112.
    13. Muhammad Kashif & Thomas Leirvik, 2022. "The MAX Effect in an Oil Exporting Country: The Case of Norway," JRFM, MDPI, vol. 15(4), pages 1-16, March.
    14. Jonathan Meng & Feng Fu, 2020. "Understanding Gambling Behavior and Risk Attitudes Using Cryptocurrency-based Casino Blockchain Data," Papers 2008.05653, arXiv.org, revised Aug 2020.
    15. Daniel Fonseca Costa & Francisval Carvalho & Bruno César Moreira & José Willer Prado, 2017. "Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1775-1799, June.
    16. Robert Gazzale & Julian Jamison & Alexander Karlan & Dean Karlan, 2013. "Ambiguous Solicitation: Ambiguous Prescription," Economic Inquiry, Western Economic Association International, vol. 51(1), pages 1002-1011, January.
    17. Boone, Jan & Sadrieh, Abdolkarim & van Ours, Jan C., 2009. "Experiments on unemployment benefit sanctions and job search behavior," European Economic Review, Elsevier, vol. 53(8), pages 937-951, November.
    18. Castro, Luciano de & Galvao, Antonio F. & Kim, Jeong Yeol & Montes-Rojas, Gabriel & Olmo, Jose, 2022. "Experiments on portfolio selection: A comparison between quantile preferences and expected utility decision models," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 97(C).
    19. Jos'e Cl'audio do Nascimento, 2019. "Behavioral Biases and Nonadditive Dynamics in Risk Taking: An Experimental Investigation," Papers 1908.01709, arXiv.org, revised Apr 2023.
    20. Luigi Guiso, 2015. "A Test of Narrow Framing and its Origin," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 1(1), pages 61-100, March.

    More about this item

    Keywords

    Density Distribution; Least Squares; Quantile; Median; Mode; Maximum Likelihood Estimators; Lottery experiments; Relative Utility Function; Prospect Theory.;
    All these keywords.

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:22268. See general information about how to correct material in RePEc.

    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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.