IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb373/200329.html
   My bibliography  Save this paper

Modeling the Learning from Repeated Samples: A Generalized Cross Entropy Approach

Author

Listed:
  • Papalia, Rosa Bernardini

Abstract

In this study we illustrate a Maximum Entropy (ME) methodology for modeling incomplete information and learning from repeated samples. The basis for this method has its roots in information theory and builds on the classical maximum entropy work of Janes (1957). We illustrate the use of this approach, describe how to impose restrictions on the estimator, and how to examine the sensitivity of ME estimates to the parameter and error bounds. Our objective is to show how empirical measures of the value of information for microeconomic models can be estimated in the maximum entropy view.

Suggested Citation

  • Papalia, Rosa Bernardini, 2003. "Modeling the Learning from Repeated Samples: A Generalized Cross Entropy Approach," SFB 373 Discussion Papers 2003,29, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    • Handle: RePEc:zbw:sfb373:200329
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/22244/1/dpsfb200329.pdf
    Download Restriction: no
    ---><---

    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.
    2. Judge, G. G. & Hill, R. Carter & Bock, M. E., 1990. "An adaptive empirical Bayes estimator of the multivariate normal mean under quadratic loss," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 189-213.
    3. Miller, D. & Golan, Amos & Judge, G., 1998. "Information Recovery in Simultaneous Equation Statistical Models," Staff General Research Papers Archive 1319, Iowa State University, Department of Economics.
    4. Golan, Amos & Judge, G. & Miller, D., 1997. "The Maximum Entropy Approach to Estimation and Inference: An Overview," Staff General Research Papers Archive 1327, Iowa State University, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    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. Rosa Bernardini Papalia & Silvia Bertarelli, 2013. "Nonlinearities in economic growth and club convergence," Empirical Economics, Springer, vol. 44(3), pages 1171-1202, June.
    2. Rosa Bernardini Papalia & Silvia Bertarelli & Carlo Filippucci, 2011. "Human capital, technological spillovers and development across OECD countries," Working Papers 15, AlmaLaurea Inter-University Consortium.
    3. Rosa Bernadini Papalia & Silvia Bertarelli, 2013. "Identification and Estimation of Club Convergence Models with Spatial Dependence," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 37(6), pages 2094-2115, November.
    4. Bera, Anil K. & Bilias, Yannis, 2002. "The MM, ME, ML, EL, EF and GMM approaches to estimation: a synthesis," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 51-86, March.
    5. Campbell, Randall C. & Hill, R. Carter, 2005. "A Monte Carlo study of the effect of design characteristics on the inequality restricted maximum entropy estimator," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 1(01), pages 1-30, June.
    6. R. Carter Hill & Randall C. Campbell, 2001. "Maximum Entropy Estimation in Economic Models with Linear Inequality Restrictions," Departmental Working Papers 2001-11, Department of Economics, Louisiana State University.
    7. Arndt, Channing, 1999. "Demand For Herbicide In Corn: An Entropy Approach Using Micro-Level Data," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 24(01), pages 1-18, July.
    8. Golan, Amos & Judge, George & Perloff, Jeffrey, 1997. "Estimation and inference with censored and ordered multinomial response data," Journal of Econometrics, Elsevier, vol. 79(1), pages 23-51, July.
    9. Marsh, Thomas L. & Mittelhammer, Ronald C. & Cardell, N. Scott, 1998. "A Structural-Equation Gme Estimator," 1998 Annual meeting, August 2-5, Salt Lake City, UT 20890, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    10. Rosa Bernardini Papalia & Silvia Bertarelli, 2010. "Evaluating Total Factor Productivity Differences by a Mapping Structure in Growth Models," International Regional Science Review, , vol. 33(1), pages 31-59, January.
    11. repec:lrk:lrkwkp:fiirs016 is not listed on IDEAS
    12. 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.
    13. 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.
    14. 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.
    15. Arndt, Channing & Simler, Kenneth R., 2005. "Estimating utility-consistent poverty lines," FCND briefs 189, International Food Policy Research Institute (IFPRI).
    16. Wobst, Peter & Arndt, Channing, 2004. "HIV/AIDS and Labor Force Upgrading in Tanzania," World Development, Elsevier, vol. 32(11), pages 1831-1847, November.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.

    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:zbw:sfb373:200329. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/sfhubde.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.