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An efficient algorithm to compute maximum entropy densities

Author

Listed:
  • D. Ormoneit
  • H. White

Abstract

We describe an algorithm to efficiently compute maximum entropy densities, i.e. densities maximizing the Shannon entropy - [image omitted] under a set of constraints [image omitted] . Our method is based on an algorithm by Zellner and Highfield, which has been found not to converge under a variety of circumstances. To demonstrate that our method overcomes these difficulties, we conduct numerous experiments for the special case gi(x) = xi, n = 4. An extensive table of results for this case and computer code are available on the World Wide Web

Suggested Citation

  • D. Ormoneit & H. White, 1999. "An efficient algorithm to compute maximum entropy densities," Econometric Reviews, Taylor & Francis Journals, vol. 18(2), pages 127-140.
    • Handle: RePEc:taf:emetrv:v:18:y:1999:i:2:p:127-140
    DOI: 10.1080/07474939908800436
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    References listed on IDEAS

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    1. Zellner, Arnold & Highfield, Richard A., 1988. "Calculation of maximum entropy distributions and approximation of marginalposterior distributions," Journal of Econometrics, Elsevier, vol. 37(2), pages 195-209, February.
    2. Stutzer, Michael, 1996. "A Simple Nonparametric Approach to Derivative Security Valuation," Journal of Finance, American Finance Association, vol. 51(5), pages 1633-1652, December.
    3. Buchen, Peter W. & Kelly, Michael, 1996. "The Maximum Entropy Distribution of an Asset Inferred from Option Prices," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 31(1), pages 143-159, March.
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    Citations

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    Cited by:

    1. Rockinger, Michael & Jondeau, Eric, 2002. "Entropy densities with an application to autoregressive conditional skewness and kurtosis," Journal of Econometrics, Elsevier, vol. 106(1), pages 119-142, January.
    2. 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.
    3. Usta, Ilhan & Kantar, Yeliz Mert, 2011. "On the performance of the flexible maximum entropy distributions within partially adaptive estimation," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2172-2182, June.
    4. Thanasis Stengos & Ximing Wu, 2010. "Information-Theoretic Distribution Test with Application to Normality," Econometric Reviews, Taylor & Francis Journals, vol. 29(3), pages 307-329.
    5. Herrmann Klaus & Fischer Matthias, 2010. "An Alternative Maximum Entropy Model for Time-Varying Moments with Application to Financial Returns," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-23, May.
    6. Holly, Alberto & Monfort, Alain & Rockinger, Michael, 2011. "Fourth order pseudo maximum likelihood methods," Journal of Econometrics, Elsevier, vol. 162(2), pages 278-293, June.
    7. Johannes Vilsmeier, 2011. "Updating the Option Implied Probability of Default Methodology," Working Papers 107, Bavarian Graduate Program in Economics (BGPE).
    8. Li, Fen & Lu, Zhenzhou & Feng, Kaixuan, 2021. "Improved chance index and its solutions for quantifying the structural safety degree under twofold random uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    9. Jesse Tack & Ardian Harri & Keith Coble, 2012. "More than Mean Effects: Modeling the Effect of Climate on the Higher Order Moments of Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(5), pages 1037-1054.
    10. Miller, Douglas J. & Liu, Wei-han, 2002. "On the recovery of joint distributions from limited information," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 259-274, March.
    11. Wu, Ximing, 2003. "Calculation of maximum entropy densities with application to income distribution," Journal of Econometrics, Elsevier, vol. 115(2), pages 347-354, August.
    12. repec:hal:journl:peer-00815562 is not listed on IDEAS
    13. Vilsmeier, Johannes, 2014. "Updating the option implied probability of default methodology," Discussion Papers 43/2014, Deutsche Bundesbank.

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    More about this item

    Keywords

    Density Estimation; Maximum Entropy Principle; Shannon Entropy; JEL Classification:C61; C63; C87;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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