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Information theoretic methods in small domain estimation


  • Rosa Bernardini Papalia
  • Esteban Fernandez-Vazquez


Small area estimation techniques are becoming increasingly used in survey applications to provide estimates for local areas of interest. The objective of this article is to develop and apply Information Theoretic (IT)-based formulations to estimate small area business and trade statistics. More specifically, we propose a Generalized Maximum Entropy (GME) approach to the problem of small area estimation that exploits auxiliary information relating to other known variables on the population and adjusts for consistency and additivity. The GME formulations, combining information from the sample together with out-of-sample aggregates of the population of interest, can be particularly useful in the context of small area estimation, for both direct and model-based estimators, since they do not require strong distributional assumptions on the disturbances. The performance of the proposed IT formulations is illustrated through real and simulated datasets.

Suggested Citation

  • Rosa Bernardini Papalia & Esteban Fernandez-Vazquez, 2018. "Information theoretic methods in small domain estimation," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 347-359, April.
  • Handle: RePEc:taf:emetrv:v:37:y:2018:i:4:p:347-359
    DOI: 10.1080/07474938.2015.1092834

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    References listed on IDEAS

    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. Golan, Amos, 2008. "Information and Entropy Econometrics — A Review and Synthesis," Foundations and Trends(R) in Econometrics, now publishers, vol. 2(1–2), pages 1-145, February.
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    2. Alberto Díaz Dapena & Esteban Fernández Vázquez & Fernando Rubiera Morollón & Ana Viñuela, 2021. "Mapping poverty at the local level in Europe: A consistent spatial disaggregation of the AROPE indicator for France, Spain, Portugal and the United Kingdom," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 63-81, February.
    3. Maria Plotnikova, 2020. "Explaining Spatial Patterns Of Incapacity Benefit Claimant Rolls," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 14(2), pages 35-48, DECEMBER.
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    5. Maria Plotnikova, 2020. "Explaining Spatial Patterns Of Incapacity Benefit Claimant Rolls," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 14(2), pages 35-47, DECEMBER.
    6. Paolo Postiglione, 2021. "New directions for regional analysis: Methods and applications," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 3-5, February.

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