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Optimal predictive densities and fractional moments

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  • Emanuele Taufer
  • Sudip Bose
  • Aldo Tagliani

Abstract

The maximum entropy approach used together with fractional moments has proven to be a flexible and powerful tool for density approximation of a positive random variable. In this paper we consider an optimality criterion based on the Kullback–Leibler distance in order to select appropriate fractional moments. We discuss the properties of the proposed procedure when all the available information comes from a sample of observations. The method is applied to the size distribution of the U.S. family income. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Emanuele Taufer & Sudip Bose & Aldo Tagliani, 2009. "Optimal predictive densities and fractional moments," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(1), pages 57-71, January.
    • Handle: RePEc:wly:apsmbi:v:25:y:2009:i:1:p:57-71
    DOI: 10.1002/asmb.721
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    References listed on IDEAS

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    1. Genius, Margarita & Strazzera, Elisabetta, 2002. "A note about model selection and tests for non-nested contingent valuation models," Economics Letters, Elsevier, vol. 74(3), pages 363-370, February.
    2. Kagan, Abram & Nagaev, Sergei, 2001. "How many moments can be estimated from a large sample?," Statistics & Probability Letters, Elsevier, vol. 55(1), pages 99-105, November.
    3. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    4. Bruce Lindsay & Ramani Pilla & Prasanta Basak, 2000. "Moment-Based Approximations of Distributions Using Mixtures: Theory and Applications," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(2), pages 215-230, June.
    5. Wu, Ximing, 2003. "Calculation of maximum entropy densities with application to income distribution," Journal of Econometrics, Elsevier, vol. 115(2), pages 347-354, August.
    6. Zvi Gilula & S. J. Haberman, 2000. "Density Approximation by Summary Statistics: An Information‐theoretic Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(3), pages 521-534, September.
    7. Ximing Wu & Thanasis Stengos, 2005. "Partially adaptive estimation via the maximum entropy densities," Econometrics Journal, Royal Economic Society, vol. 8(3), pages 352-366, December.
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    Cited by:

    1. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.

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