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

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Author Info

  • Cesaltina Pires

    ()
    (Departamento de Gestão, Universidade de Evora and CEFAGE-UE)

  • Andreia Dionisio

    ()
    (Departamento de Gestão, Universidade de Evora and CEFAGE-UE)

  • Luís Coelho

    ()
    (Departamento de Gestão, Universidade de Evora and CEFAGE-UE)

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.

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Bibliographic Info

Paper provided by University of Evora, CEFAGE-UE (Portugal) in its series CEFAGE-UE Working Papers with number 2010_02.

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Length: 26 pages
Date of creation: 2010
Date of revision:
Handle: RePEc:cfe:wpcefa:2010_02

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Related research

Keywords: Generalized maximum entropy; Maximum entropy principle; von Neumann and Morgenstern utility; Utility elicitation.;

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Cited by:
  1. Rui Fragoso & Maria Leonor Carvalho, 2011. "Estimation of Cost Allocation Coefficients at the Farm Level Using an Entropy Approach," CEFAGE-UE Working Papers 2011_21, University of Evora, CEFAGE-UE (Portugal).

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