Kontek, Krzysztof (2010): Mean, Median or Mode? A Striking Conclusion From Lottery Experiments.
Preview |
PDF
MPRA_paper_21758.pdf Download (497kB) | Preview |
Abstract
This paper deals with estimating data from experiments determining lottery certainty equivalents. The paper presents the parametric and nonparametric results of the least squares (mean), quantile (including median) and mode estimations. The examined data are found to be positively skewed for low probabilities and negatively skewed for high probabilities. This observation leads to the striking conclusion that lottery valuations are only nonlinearly related to probability when means are considered. Such nonlinearity is not confirmed by the mode estimator in which case the most likely lottery valuations are close to their expected values. This means that the most likely behavior of a group is fully rational. This conclusion is a significant departure from one of the fundamental results concerning lottery experiments presented so far.
Item Type: | MPRA Paper |
---|---|
Original Title: | Mean, Median or Mode? A Striking Conclusion From Lottery Experiments |
English Title: | Mean, Median or Mode? A Striking Conclusion From Lottery Experiments |
Language: | English |
Keywords: | Lottery experiments; Least Squares, Quantile, Median, and Mode Estimators; Nonparametric and Parametric Estimators; Relative Utility Function; Prospect Theory. |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D81 - Criteria for Decision-Making under Risk and Uncertainty C - Mathematical and Quantitative Methods > C9 - Design of Experiments > C91 - Laboratory, Individual Behavior D - Microeconomics > D0 - General > D03 - Behavioral Microeconomics: Underlying Principles C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C81 - Methodology for Collecting, Estimating, and Organizing Microeconomic Data ; Data Access C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D87 - Neuroeconomics |
Item ID: | 21758 |
Depositing User: | Krzysztof Kontek |
Date Deposited: | 31 Mar 2010 10:33 |
Last Modified: | 27 Sep 2019 14:35 |
References: | 1. Cameron, A. C., Trivedi, P. K., (2005). Microeconometrics. Methods and Applications, Cambridge University Press. 2. Gonzales, R., Wu, G., (1999). On the Shape of the Probability Weighting Function, Cognitive Psychology, 38, pp 129-166. 3. Idzikowska, K., (2009). Determinants of the probability weighting function, presented under name Katarzyna Domurat at SPUDM22 Conference, Rovereto, Italy, August 2009, ttp://discof.unitn.it/spudm22/program_24.jsp, paper Id = 182. 4. Kemp, G. C. R., Silva, J. M. C. S., (2009). Practical semi-parametric mode regression, Department of Economics, University of Essen. 5. Kontek, K., (2009a). Lottery Valuation Using the Aspiration / Relative Utility Function, Working Paper no. 39, Department of Applied Econometrics, Warsaw School of Economics. Available as RePec:wse:wpaper:39, and at SSRN: http://ssrn.com/abstract=1437420. 6. Kontek, K., (2009b). Absolute vs Relative Notion of Wealth Changes, MPRA Paper http://mpra.ub.uni-muenchen.de/17336/, Available at SSRN: http://ssrn.com/abstract=1474229. 7. Lee, M. J., (1989). Mode Regression, Journal of Econometrics, 42, pp 337-349. 8. Traub, S., Schmidt, U., (2009). An Experimental Investigation of the Disparity between WTA and WTP for Lotteries, Economics Working Papers, Christian-Albrechts-University of Kiel, Department of Economics. 9. Tversky A., Kahneman D., (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty, Journal of Risk and Uncertainty, vol. 5(4), October, pp 297-323. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/21758 |