IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v40y2013i1p221-234.html
   My bibliography  Save this article

Estimating utility functions using generalized maximum entropy

Author

Listed:
  • Cesaltina Pires
  • Andreia Dion�sio
  • Lu�s Coelho

Abstract

This paper estimates von Neumann and Morgenstern utility functions using the generalized maximum entropy (GME), applied to data obtained by utility elicitation methods. Given the statistical advantages of this approach, we provide a comparison of the performance of the GME estimator with ordinary least square (OLS) 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 are generated by utility elicitation methods.

Suggested Citation

  • Cesaltina Pires & Andreia Dion�sio & Lu�s Coelho, 2013. "Estimating utility functions using generalized maximum entropy," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(1), pages 221-234, January.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:1:p:221-234
    DOI: 10.1080/02664763.2012.740625
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2012.740625
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2012.740625?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    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. Luca Zanin, 2021. "On the estimation of Okun’s coefficient in some countries in Latin America: a comparison between OLS and GME estimators," Empirical Economics, Springer, vol. 60(3), pages 1575-1592, March.

    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. repec:lrk:lrkwkp:fiirs016 is not listed on IDEAS
    2. Miguel Henry & George Judge, 2019. "Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series," Econometrics, MDPI, vol. 7(1), pages 1-16, March.
    3. Hyeok Lee & Yong Kyun Kim, 2018. "The effects of external shocks on the Korean economy: CGE model-based analysis," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 7(1), pages 1-14, December.
    4. Wang, Yafeng & Graham, Brett, 2009. "Generalized Maximum Entropy estimation of discrete sequential move games of perfect information," MPRA Paper 21331, University Library of Munich, Germany.
    5. Arndt, Channing & Simler, Kenneth R., 2005. "Estimating utility-consistent poverty lines," FCND briefs 189, International Food Policy Research Institute (IFPRI).
    6. Wobst, Peter & Arndt, Channing, 2004. "HIV/AIDS and Labor Force Upgrading in Tanzania," World Development, Elsevier, vol. 32(11), pages 1831-1847, November.
    7. Nicole Branger, 2004. "Pricing Derivative Securities Using Cross-Entropy: An Economic Analysis," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 7(01), pages 63-81.
    8. Amos Golan & Enrico Moretti & Jeffrey M.Perloff, 2004. "A Small-Sample Estimator for the Sample-Selection Model," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 71-91.
    9. Golan, Amos & Karp, Larry S & Perloff, Jeffrey M, 2000. "Estimating Coke's and Pepsi's Price and Advertising Strategies," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 398-409, October.
    10. Rubiera-Morollón, Fernando & Fernández-Vázquez , Esteban & Aponte-Jaramillo, Elizabeth, 2012. "Estimation and analysis of labor productivity in Spanish cities," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 22, pages 129-151.
    11. You, Liangzhi & Wood, Stanley, 2006. "An entropy approach to spatial disaggregation of agricultural production," Agricultural Systems, Elsevier, vol. 90(1-3), pages 329-347, October.
    12. Golan, Amos & Perloff, Jeffrey M. & Wu, Ximing, 2001. "Welfare Effects of Minimum Wage and Other Government Policies," Institute for Research on Labor and Employment, Working Paper Series qt36r7v1cr, Institute of Industrial Relations, UC Berkeley.
    13. Wu, Ximing & Perloff, Jeffrey M., 2004. "China's Income Distribution Over Time: Reasons for Rising Inequality," Institute for Research on Labor and Employment, Working Paper Series qt9jw2v939, Institute of Industrial Relations, UC Berkeley.
    14. Giuseppe Ragusa, 2011. "Minimum Divergence, Generalized Empirical Likelihoods, and Higher Order Expansions," Econometric Reviews, Taylor & Francis Journals, vol. 30(4), pages 406-456, August.
    15. Msangi, Siwa & Howitt, Richard E., 2006. "Estimating Disaggregate Production Functions: An Application to Northern Mexico," 2006 Annual meeting, July 23-26, Long Beach, CA 21080, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    16. Macedo, Pedro & Scotto, Manuel, 2014. "Cross-entropy estimation in technical efficiency analysis," Journal of Mathematical Economics, Elsevier, vol. 54(C), pages 124-130.
    17. Fernández, Esteban & Fernández, Paula, 2008. "An extension to Sun's decomposition methodology: The Path Based approach," Energy Economics, Elsevier, vol. 30(3), pages 1020-1036, May.
    18. António Xavier & Rui Fragoso & Maria Belém Costa Freitas & Maria Socorro Rosário, 2019. "An Approach Using Entropy and Supervised Classifications to Disaggregate Agricultural Data at a Local Level," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(4), pages 763-779, December.
    19. Ohnsorge,Franziska Lieselotte & Stocker,Marc & Some,Modeste Y., 2016. "Quantifying uncertainties in global growth forecasts," Policy Research Working Paper Series 7770, The World Bank.
    20. Anne‐Sophie Robilliard & Sherman Robinson, 2003. "Reconciling Household Surveys and National Accounts Data Using a Cross Entropy Estimation Method," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 49(3), pages 395-406, September.
    21. Dionisio, Andreia & Reis, A. Heitor & Coelho, Luis, 2008. "Utility function estimation: The entropy approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3862-3867.

    More about this item

    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:taf:japsta:v:40:y:2013:i:1:p:221-234. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.