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A Composite Generalized Cross-Entropy Formulation in Small Samples Estimation

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  • R. Bernardini Papalia

Abstract

This article introduces a maximum entropy-based estimation methodology that can be used both to represent the uncertainty of a partial-incomplete economic data generation process and to consider the direct influence of learning from repeated samples. Then, a composite cross-entropy estimator, incorporating information from a subpopulation based on a small sample and from a population with a larger sample size, is derived. The proposed estimator is employed to estimate the local area expenditure shares of a sub population of Italian households using a system of censored demand equations.

Suggested Citation

  • R. Bernardini Papalia, 2008. "A Composite Generalized Cross-Entropy Formulation in Small Samples Estimation," Econometric Reviews, Taylor & Francis Journals, vol. 27(4-6), pages 596-609.
  • Handle: RePEc:taf:emetrv:v:27:y:2008:i:4-6:p:596-609 DOI: 10.1080/07474930801960469
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Rosa Bernardini Papalia & Enrico Ciavolino, 2015. "Developing a composite index by using spatial latent modelling based on information theoretic estimation," Quality & Quantity: International Journal of Methodology, Springer, pages 989-997.
    2. 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.
    3. Robert Kunst, 2014. "Report of the Editors," Empirical Economics, Springer, pages 393-395.
    4. repec:kap:jgeosy:v:19:y:2017:i:4:d:10.1007_s10109-017-0259-9 is not listed on IDEAS
    5. Esteban Fernández-Vázquez, 2014. "Estimating the effect of technological factors from samples affected by collinearity: a data-weighted entropy approach," Empirical Economics, Springer, pages 717-731.

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