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Ecological Inference And Spatial Heterogeneity - A New Approach Based On Entropy Econometrics

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  • Ludo Peeters

    ()

  • Coro Chasco-Yrigoyen

    ()

Abstract

In this paper, we compare the results obtained by the application of three alternative methods of ecological inference. The data is on per capita household disposable income in the 50 provinces and 78 municipalities of Asturias, Spain. The first method is based on Ordinary Least Squares regression model, which assumes constancy or homogeneity. The second method is based on a spatial autocorrelation model, which assumes heterogeneity in two spatial regimes. The third method is based on a varying-coefficients model, which assumes total heterogeneity. The second model is estimated by Maximum Likelihood, whereas the latter is estimated by using Generalized Maximum or Cross Entropy.

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File URL: http://www-sre.wu-wien.ac.at/ersa/ersaconfs/ersa05/papers/705.pdf
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Bibliographic Info

Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa05p705.

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Date of creation: Aug 2005
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Handle: RePEc:wiw:wiwrsa:ersa05p705

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  1. Bidani, Benu & Ravallion, Martin, 1995. "Decomposing social indicators using distributional data," Policy Research Working Paper Series 1487, The World Bank.
  2. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
  3. Judge, George G. & Miller, Douglas James & Cho, Wendy K, 2003. "An information theoretic approach to ecological estimation and inference," CUDARE Working Paper Series 946, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
  4. P.A.V.B. Swamy & George S. Tavlas, 1993. "Random coefficient models: theory and applications," Finance and Economics Discussion Series 93-14, Board of Governors of the Federal Reserve System (U.S.).
  5. Judge, George G. & Miller, Douglas J. & Cho, Wendy K. T., 2003. "An Information Theoretic Approach to Ecological Estimation and Inference," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt7h03r00q, Department of Agricultural & Resource Economics, UC Berkeley.
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