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Applying entropy econometrics to estimate data at a disaggregated spatial scale

Author

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  • Esteban Fernandez-Vazquez
  • Andre Lemelin
  • Fernando Rubiera-Morollón

Abstract

A relatively frequent problem when cross-classified data is needed (for example region $$\times $$ × industry) is that only aggregate (not cross-classified) data exists. Filling the gaps by combining data from diverse sources usually requires data conciliation. Ecological inference and entropy estimation techniques can be useful tools for this type of problem. This paper tests an estimation procedure based on entropy econometrics to recover disaggregated information from more aggregated data. We use U.S. Bureau of Economic Analysis data to estimate the 2011 personal income of local areas grouped into labor market size-classes in each State. The estimation is performed blindfolded, using only the distribution of personal income across States and, for the United States as a whole, across labor market size-classes, with local employment data as an a priori proxy indicator. Official local area personal income is aggregated into labor market size-classes for each State and used as a benchmark to compare them with the estimates. The results suggest that this technique could be an efficient way of estimating information at local level when different databases of aggregated information could be combined. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Esteban Fernandez-Vazquez & Andre Lemelin & Fernando Rubiera-Morollón, 2014. "Applying entropy econometrics to estimate data at a disaggregated spatial scale," Letters in Spatial and Resource Sciences, Springer, vol. 7(3), pages 159-169, October.
    • Handle: RePEc:spr:lsprsc:v:7:y:2014:i:3:p:159-169
    DOI: 10.1007/s12076-013-0108-5
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    References listed on IDEAS

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    1. Ludo Peeters & Coro Chasco, 2006. "Ecological inference and spatial heterogeneity: an entropy‐based distributionally weighted regression approach," Papers in Regional Science, Wiley Blackwell, vol. 85(2), pages 257-276, June.
    2. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    3. McDougall, Robert A., 1999. "Entropy Theory and RAS are Friends," Working papers 283439, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    4. McDougall, Robert, 1999. "Entropy Theory and RAS are Friends," GTAP Working Papers 300, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University.
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    Cited by:

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    2. Rares Halbac-Cotoara-Zamfir & Sirio Cividino & Gianluca Egidi & Rosanna Salvia & Luca Salvati, 2020. "Rapidity of Change in Population Age Structures: A Local Approach Based on Multiway Factor Analysis," Sustainability, MDPI, vol. 12(7), pages 1-13, April.
    3. Ricardo Troncoso Sepúlveda & Claudio Parés Bengoechea, 2018. "Estimación de la migración de votantes y ubicación de coaliciones políticas usando máxima entropía generalziada. Evidencia en Chile (2001-2013)," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 37(74), pages 495-522, July.
    4. Luca Salvati & Marco Zitti, 2017. "Urban Concentration, Agglomeration Economies and the Spatial Structure of Italian Local Labor Market Areas," Research in Applied Economics, Macrothink Institute, vol. 9(2), pages 1-17, June.
    5. Ricardo Troncoso Sepúlveda & Claudio Parés Bengoechea, 2018. "Estimación de la migración de votantes y ubicación de coaliciones políticas usando máxima entropía generalizada. Evidencia en Chile (2001-2013)," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 37(74), pages 495-522, July.

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    More about this item

    Keywords

    Ecological inference; Entropy econometrics; Conciliation of databases and geographically disaggregated data; C1; C2; R1 and R3;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • R1 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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