GME versus OLS - Which is the best to estimate utility functions?

Luís Coelho (), Andreia Teixeira Marques Dionísio () and Cesaltina Pires
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Andreia Teixeira Marques Dionísio: University of Évora, Department of Management, CEFAGE-UÉ
Authors registered in the RePEc Author Service: Andreia Dionisio

CEFAGE-UE Working Papers from University of Evora, CEFAGE-UE (Portugal)

Abstract: This paper estimates von Neumann andMorgenstern utility functions comparing the generalized maximum entropy (GME) with OLS, using data obtained by utility elicitation methods. Thus, it provides a comparison of the performance of the two estimators in a real data small sample setup. The results confirm the ones obtained for small samples through Monte Carlo simulations. The difference between the two estimators is small and it decreases as the width of the parameter support vector increases. Moreover the GME estimator is more precise than the OLS one. Overall the results suggest that GME is an interesting alternative to OLS in the estimation of utility functions when data is generated by utility elicitation methods.

Keywords: Generalized maximum entropy; Maximum entropy principle; von Neumann and Morgenstern utility; Utility elicitation. (search for similar items in EconPapers)
JEL-codes: C13 C14 C49 D81 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2010
New Economics Papers: this item is included in nep-ecm and nep-upt
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:cfe:wpcefa:2010_02

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